1. No category

“Honey and Honey-Based Products: Bioavailability and Functionality”
2 0 1 5, Vol. 65, No. 2
Published since 1957 as
Roczniki Chemii i Technologii Żywności and Acta Alimentaria Polonica (1975–1991)
EDITOR-IN-CHIEF
Prof. dr hab. Henryk Zieliński, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
ASSOCIATE EDITORS
Food Technology Section
Prof. Alberto Schiraldi, DISTAM, sez. Chimica, Università di Milano, Milano, Italy
Dr hab. inż. Marek Adamczak, prof UWM, University of Warmia and Mazury, Olsztyn, Poland
Food Chemistry Section
Dr. Maria Dolores del Castillo, Institute of Food Science Research (CSIC-UAM), Madrid, Spain
Dr hab. Magdalena Karamać, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
Food Quality and Functionality Section
Dr. Maria Juana Frias Arevalillo, Institute of Food Science, Technology and Nutrition (ICTAN), Madrid, Spain
Dr. Zuzana Ciesarova, NPPC National Agricultural and Food Centre, VUP Food Research Institute, Bratislava, Slovak Republic
Prof. dr hab. Ryszard Amarowicz, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn,
Poland
Nutritional Research Section
Prof. André Mazur, Human Nutrition Unit, INRA, Clermont, France
Prof. dr hab. Anna Brzozowska, University of Life Sciences, Warsaw, Poland
Prof. dr hab. Jerzy Juśkiewicz, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
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Joanna Molga,“News” Section, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
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Published
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Reproduction and Food
Research of Polish Academy
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“Honey and Honey-Based Products: Bioavailability and Functionality”
ISSN (1230–0322)
2 0 1 5, Vol. 65, No. 2
Pol. J. Food Nutr. Sci.
2015, Vol. 65, No. 2
Advisory Board of PJFNS 2015–2018
Huda Al-Kateb
University of the West of England, Bristol, UK
Jennifer M. Ames
University of the West of England, Bristol, UK
Wilfried Andlauer
University of Applied Sciences, Sion, Switzerland
Sa’eed Halilu Bawa
University of the West Indies, St. Augustine,
The Republic of Trinidad and Tobago
Bhaskar C. Behera
Agharkar Research Institute, India
Vural Gökmen
Hacettepe University, Ankara, Turkey
Adriano Gomes Da Cruz
UNICAMP, Sao Paulo, Brazil
Liwei Gu
University of Florida, Gainesville, USA
Henryk Jeleń
Poznań University of Life Sciences, Poland
Georgios Koutsidis
Northumbria University, Newcastle-upon-Tyne, UK
Theodore P. Labuza
Department of Food Science and Nutrition,
University of Minnesota, USA
Andrzej Lenart
Warsaw University of Life Sciences, Poland
Johns Lodge
Northumbria University, Newcastle-upon-Tyne, UK
Claudine Manach
INRA, Centre de Recherche de Clermont-Ferrand,
Theix, France
Adolfo J. Martinez-Rodriguez
CSIC, Madrid, Spain
Brian McKenna
National University of Ireland, Dublin, Ireland
Najma Memon
University of Sindh, Sindh, Pakistan
Francisco J. Morales
CSIC, Madrid, Spain
Mostafa Noroozi
Qazvin University of Medical Sciences, Qazvin, I.R. Iran
David W. Pascual
Montana State University, Bozeman, USA
Ron Pegg
University of Georgia, Athens, USA
Mariusz K. Piskuła
Institute of Animal Reproduction and Food Research PAS,
Olsztyn, Poland
Edward Pospiech
Poznań University of Life Sciences, Poland
Mark Shamtsyan
St. Petersburg State Institute of Technology, St. Petersburg,
Russia
Hilmer Sorensen
Royal Veterinary and Agricultural University, Frederiksberg,
Denmark
Susanne Sorensen
University of Copenhagen, Frederiksberg, Denmark
Da-Wen Sun
National University of Ireland, Dublin, Ireland
Ludwik Theuvsen
Georg-August University of Goettingen, Germany
Özlem Tokuşoglu
Celal Bayar University, Manisa, Turkey
Lida Wądołowska
Warmia and Mazury University, Olsztyn, Poland
Maria Wojtatowicz
Wrocław University of Life Sciences, Poland
Pol. J. Food Nutr. Sci.
2015, Vol. 65, No. 2
Dear Colleagues,
Polish Journal of Food and Nutrition Sciences is pleased to announce a Special Issue on “Honey
and Honey-Based Products: Bioavailability and Functionality”.
Honey is a natural sweet substance produced by honey bees using nectar that is collected by the bees from
the nectar of plants. For centuries, honey has been used for nutrition in different cultures. The oldest evidence of honey collection by humans dates from c. 6000 BC in a cave near Valencia in Spain. Honey, the only
sweetener available in the wild, has also captured the attention of people because of its complex composition,
which has been associated with antiseptic, antimicrobial, anticancer, anti-inflammatory, and wound-healing
properties. The composition of honey varies depending on the origin of the raw material as nectar or honeydew, the bee species, the edaphoclimatic conditions, the available floral source and the storage conditions.
Honey consists mainly of glucose and fructose but also contains amino acids, phenolic compounds, organic
acids, vitamins, minerals, lipids, enzymes and other phytochemicals. Among the compounds with biological
activity that are present in honeys, the compounds that display antioxidant capacity, such as phenolic acids, flavonoids and the enzymes glucose oxidase and catalase, have received special attention from research
groups, due to their role in the prevention of diseases associated with oxidative stress. Honey is also a natural,
nontoxic, and inexpensive product for the need of novel therapies against bacterial infections. The clinical use
of honey has an enormous potential, especially in the fight against antibiotic-resistant strains. However, little
information is available on the composition and bioactive properties of honeys and honey-based products
from floral sources in the world. In this Special Issue we have focused on trans-disciplinary aspects of honeys
and honey-derived products in the areas of:
• Processing and storage of honey – effect on quality and functionality
• Bioactive constituents of honeys and honey-derived products
• Quality and safety of honeys
• Aroma volatile compounds of honeys and honey-derived products
• Microbiological activity of honeys and honey-derived products
• Honey as an indicator of environmental quality
• Development of new analytical methods for honey’s originality assessment.
The Guest Editor, Prof. Teresa Szczęsna, has compiled articles focused on honeys from various scientific
disciplines. The Editors and Authors hope that this Special Issue will serve as a reference tool for honey researchers globally. This Special Issue is also a tool for all interested in concluding basic research on honey
and honey-based products.
Guest Editor
Prof. Teresa Szczęsna
Editor-in-Chief: Journal of Apicultural Science
Editor
Prof. Henryk Zieliński
Editor-in-Chief: Polish Journal of Food and Nutrition Sciences
Pol. J. Food Nutr. Sci.
2015, Vol. 65, No. 2
REVIEW
Polyphenol-Protein Complexes and Their Consequences for the Redox Activity,
Structure and Function of Honey. A Current View and New Hypothesis – a Review.
K. Brudzynski, L. Maldonado-Alvarez ............................................................................................................................................... 71
ORIGINAL PAPERS
Antioxidant Effect of Natural Honeys Affected by Their Source and Origin.
M. Mellen, M. Fikselová, A. Mendelová, P. Haščík ........................................................................................................................... 81
Antioxidant Properties of Honey from Different Altitudes of Nepal Himalayas.
B.P. Neupane, K.P. Malla, A. Kaundinnyayana, P. Poudel, R. Thapa, S. Shrestha ........................................................................... 87
Multi-Element Composition of Honey as a Suitable Tool for Its Authenticity Analysis.
M. Oroian, S. Amariei, A. Leahu, G. Gutt ....................................................................................................................................... 93
Physico-Chemical, Enzymatic, Mineral and Colour Characterization of Three Different Varieties of Honeys
from Kashmir Valley of India with a Multivariate Approach.
G.A. Nayik, V. Nanda ..................................................................................................................................................................... 101
Spray Drying of Honey: The Effect of Drying Agents on Powder Properties.
K. Samborska, P. Gajek, A. Kamińska-Dwórznicka ....................................................................................................................... 109
Effect of Microwave Treatment on Microbial Contamination of Honeys and on Their Physicochemical
and Thermal Properties.
M. de la Paz Moliné, N.J. Fernández, S.K. Medici, D. Fasce, L.B. Gende ...................................................................................... 119
Effects of Honey Addition on Antioxidative Properties of Different Herbal Teas.
G. Toydemir, E. Capanoglu, S. Kamiloglu, E. Firatligil-Durmus, A.E. Sunay, T. Samanci, D. Boyacioglu .................................... 127
Influence of Sweetness and Ethanol Content on Mead Acceptability.
T. Gomes, T. Dias, V. Cadavez, J. Verdial, J. Sá Morais, E. Ramalhosa, L.M. Estevinho .............................................................. 137
Origin of Synthetic Particles in Honeys.
G. Liebezeit, E. Liebezeit ............................................................................................................................................................... 143
Instruction for Authors .............................................................................................................................................................149
Subscription
2015 – One volume, four issues per volume. Annual subscription rates are: Poland 140 PLN, all other countries 70 EUR. Prices
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Subscription
2015 – One volume, four issues per volume. Annual subscription rates are: Poland 140 PLN, all other countries
70 EUR. Prices are subject to exchange rate fluctuation. Subscription payments should be made by direct bank transfer
to Bank Gospodarki Żywnościowej, Olsztyn, Poland, account No 17203000451110000000452110 SWIFT code:
GOPZPLWOLA with corresponding banks preferably. Subscription and advertising offices at the Institute of Animal
Reproduction and Food Research of Polish Academy of Sciences, ul. J. Tuwima 10, 10–747 Olsztyn, Poland,
tel./fax (48 89) 5234670, fax (48 89) 5240124, e-mail: [email protected]; http://journal.pan.olsztyn.pl
Zamówienia prenumeraty: Joanna Molga (e-mail: [email protected]) oraz
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Wersja pierwotna (referencyjna) kwartalnika PJFNS: wersja papierowa (ISSN 1230–0322)
Nakład: 100 egz.; Ark. wyd. 11,5; Ark. druk. 11,5
Skład i druk: Mercurius, www.mercurius.com.pl
Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 71–80
DOI: 10.1515/pjfns-2015-0030
http://journal.pan.olsztyn.pl
Review article
Section: Food Chemistry
Polyphenol-Protein Complexes and Their Consequences for the Redox Activity,
Structure and Function of Honey. A Current View and New Hypothesis – a Review
Katrina Brudzynski1*, Liset Maldonado-Alvarez2
Department of Drug Discovery and Development, Bee-Biomedicals Inc.,
A9–210 Glendale Ave., Suite 109, St. Catharines, ON, L2T 3Y6, Ontario, Canada
2
Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
1
Key words: honey, polyphenol-protein interactions, “protein-type” complexes, “polyphenol-type” complexes, polyphenol auto-oxidation, quinones,
melanoidins, gain-and-loss of function
There is increasing evidence that protein complexation by honey polyphenols is changing honey structure and function. This relatively less investigated filed of honey research is presented in a context of known mechanism of formation of the stable polyphenol-protein complexes in other foods. At
a core of these interactions lies the ability of polyphenols to form non-covalent and covalent bonds with proteins leading to transient and/or irreversible
complexes, respectively. Honey storage and thermal processing induces non-enzymatic oxidation of polyphenols to reactive quinones and enables them
to form covalent bonds with proteins. In this short review, we present data from our laboratory on previously unrecognized types of protein-polyphenol
complexes that differed in size, stoichiometry, and antioxidant capacities, and the implications they have to honey antioxidant and antibacterial activities. Our intent is to provide a current understanding of protein-polyphenol complexation in honey and also some new thoughts /hypotheses that can
be useful in directing future research.
INTRODUCTION
NON-COVALENT BINDING
Polyphenols and proteins are minor components of honeys but they can significantly influence honey antioxidant
and antibacterial properties. The reason behind these effects
relates to polyphenols’ ability to bind proteins via non-covalent or covalent bonds and sequestering proteins into soluble or insoluble complexes. Binding affects function of both
proteins and polyphenols. Proteins modified by polyphenols
undergo conformation transitions that are changing their
biological activity, as in a case of honey enzymes. Binding
of polyphenols to proteins on the other hand, affects antioxidant activity of polyphenols because the binding engages
the same functional groups that are involved in redox cycling;
electron donation, or metal chelation. These functional alterations following protein and polyphenols complexations
can be transient or irreversible, depending on whether non-covalent or covalent bonds are formed between these molecules. Environmental factors such as pH, temperature, ionic
strength often modulate transient interactions toward formation of stable protein-polyphenols complexes with a long
half-life. Here, we review these aspects of protein-polyphenol
interactions and describe functional consequences they have
on honey functions.
Non-covalent binding between protein and polyphenols
involves hydrogen bonds that are formed between electronegative atoms of nitrogen or oxygen, especially of amino
(–NH2) and hydroxyl (–OH) groups, and a positively charged
hydrogen atom from neighboring hydroxyl or amino group
of another polyphenol or protein molecules. Depending on
polyphenol structure and degree of hydroxylation, the interaction may produce single or multiple hydrogen bonds that
influence strength of the formed complexes [Haslam, 1974].
Hydrogen bonds between neighboring protein chains can create bridges that crosslink proteins into aggregates.
In addition to hydrogen bonds that involve polar groups,
protein and polyphenols may interact via hydrophobic, non-polar aromatic rings of polyphenols and aromatic amino acids, (proline, phenylalanine, tyrosine, tryptophan, histidine)
[Charlton et al., 2002; Siebert, 1999]. Hydrophobic bonds
influence the structure of the complex by stacking of aromatic
rings of polyphenols against those of aromatic amino acids
such as in the case of proline pyrrolidone rings in proline-rich proteins and galloyl rings of tannins [Baxter et al., 1997;
Siebert et al., 1996]. Formation of protein-polyphenol complexes usually results from multiple cooperative hydrophobic
and hydrogen binding and may lead to colloidal size aggregates. The non-covalent protein-polyphenol interactions are
responsible for the haze formation in beers, wines and fruit
* Corresponding Author: E-mail: [email protected] (K. Brudzynski)
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
72
juices [Siebert et al., 1996; Baxter et al., 1997; Siebert, 1999]
and colloids in honey [Paine et al., 1934; Lothrop & Paine,
1931].
HONEY COLLOIDS - MELANOIDINS CONNECTION
Honey colloids are mixture of non-crystalline particles,
vastly differing in sizes that are evenly dispersed and suspended in supersaturated sugar solution. They cannot be removed
from honey by traditional membrane filtration or centrifugation because of their size heterogeneity, differences in solubility, and the particle association-dissociation dynamics.
The presence of colloids affects physical properties of honey
such as color, flavor, clarity, crystallization and thixotropy
[Paine et al., 1934; Witczak et al., 2011]. The colloidal content in honey is low and varies between 0.1 to 1.0% for light
and dark honey, respectively [Lothrop & Paine, 1931; Mitchell et al., 1955]. The chemical composition of honey colloids
and the mechanism of their formation remained enigmatic
for a long time. According to old literature, honey colloids
consisted of proteins (enzymes), pigments (polyphenols)
and some waxes [White, 1957]. More than 50% of its mass
was found to be comprised of proteins [Mitchell et al., 1955].
The fact that honey colloids could be precipitated upon dilution with water [Lothrop & Paine, 1931] suggested that they
are hydrophobic in nature. Their isoelectric point was found
to be 4.3 [White, 1957] therefore in honey, with the average
pH of 3.9, colloids are positively charged.
High concentration of sugars in honey may increase colloidal stability. Usually, the stability and size of colloidal
particles is strongly controlled by environmental conditions
such as pH, temperature, or ionic strength. Since the interactions between colloid particles are governed by Coulombic electrostatic interactions and van der Waals interactions
[Israelachvili, 1992], changes in pH and salt concentration
increase the tendency of particles to coalesce to large aggregates and to promote their flocculation.
We cannot resist the impression that the brown, colloidal
material in honey described in old literature fits the description
of melanoidins. Melanoidins are high molecular weight colloidal complexes that are formed in thermally processed foods
as the result of the Maillard reaction [for review see, Wang
et al., 2011]. A structure of intact melanoidins has proven to
be challenging to characterize at the molecular level and it remains mostly unknown. However, all food melanoidins contain proteins, polyphenols, sugars and the Maillard reaction
products as the main components. Such structures have been
also recently found in honey [Brudzynski & Miotto, 2011b, c].
Protein-polyphenol complexes are an integral part of both
colloids and melanoidins. The colloid-melanoidin connection
is supported by the following facts. Firstly, proteins, as surface active molecules are “colloidally” active and can readily
assemble to high molecular weight aggregates via protein-protein interaction and/or interactions with other ligands
including polyphenols [Haslam, 1996]. Secondly, the driving
force in the protein-polyphenol complexation is a hydrophobic force/effect. This property is responsible for colloid formation, such as haze formation in beer as well as melanoidin
formation, such as that of coffee. By making a connection
Polyphenol-Protein Interactions
between honey colloids and melanoidins, one could easily
envision the location and milieu in which the protein-polyphenol interaction take place in honey. Despite uncertainty
of the colloids-melanoidin connection, it is undoubtedly true
that the protein-polyphenol complexes are highly relevant to
physical, structural and functional properties of honey. There
is a multitude of proteins of different functions in honey that
can be target by polyphenols.
HONEY PROTEINS
Protein modification by polyphenols alters the biological activity of honey proteins in the way that it reduces their
original function and nutritional benefits. Proteins are minor
components of honey, amounting to 0.2 to 0.7% of honey
mass [Bogdanov, 2008]. Together with nectar-carbohydrates
they serve as the primary source of honeybee’s diet. Proteins
of pollen origin are the main source of nitrogen for bees [Baroni et al., 2002; Iglesias et al., 2006]. Besides the nutritional
role, proteins of pollen origin comprise of stress-inducible,
pathogenesis-related proteins (PRP) [Midoro-Horiuti et al.,
2001; Breiteneder, 2004] that may enhance bee’s immunity
and the tolerance to pathogens. To the group of pollen disease-resistant proteins belongs also the 31 kDa dirigent-like
protein recently discovered in honey [Brudzynski et al., 2013].
However, majority of honey proteins are of bee origin
and consist of enzymes involved in sugar metabolism: glucose
oxidase [Schepartz & Subers, 1964], alpha-glucosidase (invertase) [White & Kushnir, 1966; Bonvehi et al., 2000], beta-glucosidase [Pontoh & Low, 2002], alpha-amylase (diastase)
[Oddo et al., 1995; Babacan & Rand, 2007], transglucosylase
and phosphorylases. The proper functioning of these enzymes
ensures an adequate supply of simple carbohydrates that are
for bees a source of energy for flight and colony maintenance.
The most abundant non-enzymatic honey protein originating from bees is a 55–57 kDa glycoprotein, Major Royal
Jelly Protein1 (MRJP1) or apalbumin-1 [Šimúth, 2001;
Šimúth et al., 2004; Won et al., 2008]. The size and a high
concentration of MRJP1 in honey suggested its nutritional role [Šimúth, 2001]. This view was initially supported
by a finding that the MRJP1 protein can be specifically degraded by serine proteases, generating a substantial number
of MRJP1-related peptides [Rossano et al., 2012]. However,
the degradation products of MRJP1 were also shown to have
immune-stimulatory activities [Majtan et al., 2006; Tonks
et al., 2003] and cell growth stimulatory activity [Watanabe
et al., 1998; Kamakura et al., 2001; Majtan et al., 2009].
The expanding number of functions of the MRJP1 includes
its significant influence on honey structure. The MRJP1 has
propensity for oligomerization, and in honey, it often forms
a large, 350 kDa hetero-hexamer, known as apisin [Kimura
et al., 1995, 1996] with a peptide named apisimin [Bilikova
et al., 2002] that serves as a linker between the MRJP1 oligomers [Tamura et al., 2009]. The MRJP1 molecule is highly
glycosylated [Kimura et al., 1995, 1996]. The MRJP1 size
and presence of several reactive groups can influence the attraction and repulsion forces with other ligands. Thus, these
features make the MRJP1 the most suitable candidate to
be involved in colloid-melanoidin formation. Honey also con-
K. Brudzynski & L. Maldonado-Alvarez
tains other types of MRJPs (MRPJ2 to 5) [Di Girolamo et al.,
2012] thereby creating a substantial pool of non-enzymatic
proteins of honeybee origin.
In view of these multiple biological roles of honey proteins, it becomes instantly apparent that their interaction
and complexation with polyphenols will have a significant impact on honey function and nutritional availability.
STOICHIOMETRY OF PROTEIN-POLYPHENOL
COMPLEXES
Association and dissociation between proteins and polyphenols is initially a surface phenomenon. The stability
of complexes depends on the molecular size of interacting
partners, their concentrations and external conditions in which
the interaction takes place [Spencer et al., 1988; Haslam,
1996]. Polyphenols of higher molecular size showed greater
tendency to form stable complexes with proteins [De Freitas
& Mateus, 2001]. In this respect, it has to be realized that proteins and polyphenols vastly differ in sizes. The average size
of polyphenols is ~500 daltons while a typical size of protein
is 30,000 daltons (in terms of their sequence size as determined
by the number of amino acids). To produce stable complexes,
the ratio of polyphenol to protein must be high to form multiple non-covalent bonds. Secondly, the strength of complexes
is influenced by polyphenol hydrophobicity. At low polyphenol
concentrations, their attachment to proteins does not change
hydrophilic character of complexes and the complexes remain
soluble. However, with the increased concentration of polyphenols, the protein-polyphenol complexes become more
hydrophobic by encouraging interactions between non-polar
residues of polyphenols and proteins. The intramolecular hydrophobic interactions between different protein-polyphenol
complexes are the driving force toward aggregation and precipitation as large, insoluble aggregates [Spencer et al., 1988;
Naczk et al., 2011]. Taken together, the polyphenols’ hydrophobic aromatic rings and hydrophilic hydroxyl groups contribute to multiple, non-covalent bonds with proteins and act
as multidenate ligands [Spencer et al., 1988; Haslam, 1996].
The size and solubility of protein-polyphenol complexes
are temperature-sensitive; elevated temperatures facilitate tendency to hydrophobic interactions by unfolding the proteins
chains and exposing hydrophobic amino acids (hydrophobic
effect). Thus, the size and number of protein-polyphenol complexation will increase with the raise of temperature. There are,
however, other factors influencing the complexation.
POLYPHENOLS AUTO-OXIDATION AND FORMATION
OF QUINONE-PROTEIN COVALENT BONDS
In addition to temperature, the environmental factors such
as exposure to oxygen, presence of oxidizing agents (hydrogen peroxide), and the presence of transition metal ions significantly facilitates formation of covalent bonds that irreversibly link polyphenols with proteins. Such binding occurs upon
polyphenol auto-oxidation in the presence of O2. The autooxidation involves one- or two-step electron transfer (as
it is shown for quercetin in Figure 1). Firstly, the transfer of an
electron from polyphenol to O2 leads to formation of ortho-
73
semiquinone anion radical (Q˙¯). o-Semiquinone radicals,
as unstable compounds, could undergo radical-radical reaction giving o-quinone and a reconstituted parent molecule.
Q˙¯ radical can also react with molecular oxygen and give
rise to a superoxide anion radical (O2˙¯), initiating the redox
cycle. Subsequently, the superoxide radical can be scavenged
by the parent polyphenol and produce again o-semiquinone
radicals and hydrogen peroxide (Figure 1). This redox cycle
may continue until the system becomes depleted of oxygen.
In contrast to one electron transfer, the two-step electron
transfer from the parent molecule gives more thermodynamically stable o-quinones [Methodieva et al., 1999]. The formation of o-quinone is followed by a rapid isomerization to
para-quinone methide intermediates. As potent electrophiles,
quinones and quinone methides bind specifically and irreversibly to nucleophilic groups of amino acids such as sulfhydryl, amine, amide, indole or imidazole groups of proteins
(Figure 1) [Methodieva et al., 1999; Cilliers & Singleton,
1991, Hotta et al., 2001].
The structure of polyphenols and their ability of redox
cycling plays important role in formation of covalent bonds.
Ability to auto-oxidize and hence to have pro-oxidant effect
is directly related to the presence of (a) a catechol group on
the B-ring (b) the 2, 3-double bond in conjugation with a carbonyl group at 4-position in the C-ring, and (c) the presence
of hydroxyl groups at the 3 and 5 position (Figure 2) [Rice-Evans et al., 1998; Bors et al., 1990].
FIGURE 1. A simplified outline of auto-oxidation of quercetin to o-semiquinone radical and quinone by two subsequent electron transfers. O-semiquinone radical can react with molecular oxygen and give rise to a superoxide
anion radical (O2˙¯), producing pro-oxidant effect. Quinones and subsequent quinone methides react with proteins.
FIGURE 2. Structural determinants of flavonoids contributing to antioxidant and proxidant activities as shown in the molecule of quercetin:
essential OH groups, 2, 3 –double bond and carbonyl-group at 4 position
of C ring are highlighted.
74
Polyphenol-Protein Interactions
gel electrophoresis, ORAC and LC-ESI-MS [Brudzynski
& Miotto, 2011a, b; Brudzynski et al., 2013]. The implementation of this methodology allowed separation of honey
complexes onto two groups that differed in size, stoichiom-
1.2
1.0
0.8
Intens. (x108)
In addition to binding the nucleophilic amino acids groups,
semiquinone radicals may undergo radical-radical reactions
between themselves generating oligomers and polyphenol
polymers [Cilliers & Singleton, 1991; Hotta et al., 2001; Bors
et al., 2004]. Such radical-induced reactions underlie dimerization and oligomerization of caffeic acids [Bors et al., 2004].
These oligomeric forms that possess multiple catechol rings
have been shown to be much more effective radical scavengers
than monomeric polyphenols [Bors et al., 2004].
Thus, polyphenol auto-oxidation generates irreversible,
covalent bonds with proteins and other polyphenols. The new
structures acquire either increased antioxidant (scavenging
free radicals) as in a case of caffeic acid oligomers or pro-oxidant capacities (generating free radicals) as in a case of quinone-protein complexes. Once the polyphenol auto-oxidation
is initiated, it proceeds spontaneously with time.
0.6
0.4
0.2
“PROTEIN-TYPE” AND “POLYPHENOL-TYPE”
COMPLEXES IN HONEY
0
0
In honey, the interaction between protein and polyphenols increases with time of storage and after heat treatment.
The nature of protein-polyphenol complexation was studied using size-exclusion chromatography, polyacrylamide
2
4
6
8
10
12
14
16
18
Time (min)
FIGURE 3. Total ion current chromatograms (TICs) in the negative ion
mode for LC-ESI-MS analysis of “protein-type” (upper line) and “polyphenol-type” (bottom line) complexes isolated from honey.
1.2
1.0
0.8
0.6
0.4
Intens. (x108)
0.2
0
1.2
1.0
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
12
14
16
18
Time (min)
FIGURE 4. Extracted ion mass chromatogram of “protein-type” complexes against target mass ions of p-coumaric acid m/z 163 (peak 1), ferulic acid
m/z 193 (peak 2), and caffeic acid m/z 179 (peak 3) (lower panel). The position of extracted mass ions is related to the total ion current chromatogram
(upper panel).
75
K. Brudzynski & L. Maldonado-Alvarez
etry and antioxidant/pro-oxidant capacity. Considering data
on browning indexes, characteristic UV spectra (200 nm to
400 nm), color, molecular size, and ORAC values obtained
before and after heat-treatment, the complexes were identified
as melanoidins [Brudzynski & Miotto, 2011a, b].
Determination of the total protein and phenolic contents
in these complexes provided a general assessment of the stoichiometry of the reactants expressed as percentage of the total
mass. The stoichiometry results, together with SDS-PAGE,
revealed that high molecular weight complexes (230–180
kDa) were enriched in proteins (“protein-type” complexes)
while lower molecular size complexes (110–85 kDa) were
enriched in polyphenols (“polyphenol-type” complexes)
[Brudzynski et al., 2013].
In addition to differences in size and protein to polyphenol ratio, the complexes presented different polyphenol profiles in LC-ESI-MS. The direct comparison of the total ion
currents (TICs) during 20 min elution showed that “polyphenol-type” complexes were lacking hydrophilic compounds
that were contributing to mass spectrum at retention times
of 7 to 13 min (Figure 3) [Brudzynski & Miotto, 2011b]. Furthermore, screening of mass ion profiles against target mass
ions of caffeic (m/z 179), ferulic (m/z 193) and p-coumaric
(m/z 163) acids demonstrated that these simple, monophe-
nolic acids were absent in “polyphenol-type” complexes but
occurred in “protein-type” complexes (Figures 6 and 4, respectively). The phenolic acids, specifically caffeic acid, appeared as unknown conjugates that were eluted at longer
retention time than RT=4.19 min expected for a standard
caffeic acid monomer under the chromatographic conditions
used (Table 1). We have noted however that caffeic acid mass
ion of m/z 179 co-eluted together with m/z 253 (chrysin) at
RT 13.9 min, m/z 255 (pinocembrin) at RT=14.4 and with
m/z 283 (acetatin) at RT 15.2 (Figure 4). Although interesting, these very early results do not warrant any suggestion
of possible association between above-mentioned flavonoids
and caffeic acid in honey. The same screening method used
against target mass ions of pinobanksin (m/z 271), pinocembrin (m/z 285), chrysin (m/z 253) and acacetin (m/z 283)
supported the presence of these flavonoids in both types
of complexes (Figures 5 and 6). These polyphenols have
been chosen as typical representatives of phenolics in honey
[Pyrzynska & Biesaga, 2009]. Their identification was based
on a comparison with mass ions and retention times of standards under chromatographic conditions described previously (Table 1) [Brudzynski & Miotto, 2011]. The low polarity
of “polyphenol-type” complexes (longer elution times) indicated their hydrophobic nature.
1.2
1.0
0.8
0.6
0.4
Intens. (x108)
0.2
0
1.2
1.0
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
12
14
16
18
Time (min)
FIGURE 5. Extracted ion chromatogram of “protein-type” complexes against target mass ions of p-coumaric acid m/z 163 (peak 1), chrysin m/z 253
(peak 2), pinocembrin m/z 255 (peak 3) and acecatin m/z 283 (peak 4) (lower panel). The upper panel presents the TIC.
76
Polyphenol-Protein Interactions
1.0
0.8
0.6
0.4
Intens. (x108)
0.2
0
1.0
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
12
14
16
18
Time (min)
FIGURE 6. Extracted ion chromatogram of “polyphenol-type” complexes against target mass ions of pinobanksin m/z 271 (peak 1), chrysin m/z 253
(peak 2), pinocembrin m/z 255 (peak 3) and acecatin m/z 283 (peak 4). The upper panel present the TIC of “polyphenol-type” complexes.
Another feature differentiating “protein-type” from
“polyphenol-type” complexes was the level of their antioxidant/pro-oxidant capacity. The radical scavenging activity
of “protein-type” complexes was up to 2.5 fold higher than
that of “polyphenol-type” complexes in unheated honeys
and increased up to 29.5 fold after heat-treatment of honey,
as indicated by ORAC values [Brudzynski & Miotto, 2011b].
Polyphenols present in “protein-type” and “polyphenoltype” complexes also showed a distinct susceptibility to autooxidation. This was determined colorimetrically using a dye,
NitroBlue Tetrazolium (NBT). In the presence of redox-active quinones, the NBT was reduced producing the purple
formazan [Paz et al., 1991]. The reaction proceeds under
alkaline conditions in the presence of glycine as a reducing
agent that reduces quinones to hydroquinones. Hydroquinones in turn, reduce the NBT to formazan. Thus, the intensity of formazan staining directly correlated with the levels
of quinone-protein complexes that underwent reduction to
hydroquinones. By employing this method, it occurred that
“protein-type” complexes possessed a limited reducing activity toward NBT and therefore had less quinones/ hydroquinones, while “polyphenol-type” complexes strongly reduced
the dye [Brudzynski et al., 2013].
The importance of these differences is implicit in the fact
that in “protein-type complexes” the reduced, non-oxidized
polyphenols may carry antioxidant activity, while the oxidized polyphenols of “polyphenol-type” complexes may
contribute to pro-oxidant effects. These interesting preliminary observations need to be further thoroughly investigated.
FUNCTIONAL CONSEQUENCE OF PROTEIN-POLYPHENOL COMPLEXATION: LOSS OR GAIN
OF FUNCTION
Enzyme inactivation
The interactions of proteins and polyphenols influence
the structural and functional properties of both molecules.
The covalent interactions of proteins with polyphenols may
be responsible for the inactivation of honey enzymes; alphaamylase (diastase), alpha-glucosidase, and D-fructofuranoside-fructohydrolase, invertase [White et al., 1964; Huidobro
et al., 1995; Oddo et al., 1999; Semkiw et al., 2010]. Although
such relationship in the case of honey enzymes has not yet
been established, other studies showed a clear connection
between polyphenol bindings and inhibition of variety of enzymes [Haslam, 1996; Harborne & Williams, 2000; Narayana
et al., 2001; Charlton et al., 2002]. Honey has also been shown
to inhibit polyphenol oxidase activity and enzymatic oxidation
of polyphenols [Oszmianski & Lee, 1990].
77
K. Brudzynski & L. Maldonado-Alvarez
TABLE 1. Summary of retention times of polyphenolic standards and polyphenols in “protein-type” and polyphenol-type” complexes.
Polyphenol
[M-H]
-m/z
Chemical structure
Standard
Protein-type compounds Polyphenol-type compounds
Retention Time (min) Retention Time (min)
Retention Time (min)
Caffeic acid
179
3,4-Dihydroxy-cinnamic acid
4.19
13.9; 14.5; 15.1
p-coumaric acid
163
4-hydroxycinnamic acid
8.7
8.7
Ferulic acid
193
3-methoxy-4-hydroxycinnamic acid
9.5
9.5
Pinobanksin
271
3,5,7-trihydroxyflavanone
9.1
9.1; 11.0
9.1
Pinocembrin
255
5,7-dihydroxyflavanone
14.4
14.4;13.9
14.4;10.7
Chrysin
253
5,7-dihydroxyflavone
14.0
14.0
14.0
Acacetin
283
5,7-Dihydroxy-4'-methoxyflavone
Apigenin-4'-methylether
15.1
15.2
15.1; 14.6
Changes in antioxidant capacities
With respect to polyphenols, their binding to proteins
usually causes a decrease of their antioxidant activities [Rohn
& Rawel, 2004]. Often, the antioxidant capacities of protein-polyphenol complexes were found to be lower than the sum
of the antioxidant capacities of individual components [Arts
et al., 2001]. However, in contrast, antioxidant and metal
chelating activities were shown to be retained by melanoidins of beer [Morales & Jimenes-Perez, 2004], bread crust
[Michalska et al., 2008], vinegar [Tagliazucchi et al., 2010]
and wines [Lopez de Lerma et al., 2010].
In honey, the incorporation of polyphenol-protein complexes into melanoidin was responsible for both gain and loss
of melanoidins’ antioxidant activity [Brudzynski & Miotto,
2011a]. In unheated honeys, the radical scavenging activity
of melanoidins depended on the initial content and antioxidant activity of polyphenols. In light- and medium-colored
honeys possessing low ORAC values, heat treatment caused
a strong increase in antioxidant activity of melanoidins with
the concomitant accelerated formation of high molecular weight melanoidins. Melanoidins of light- and medium
honeys gained the antioxidant capacity. In contrast, heat
treatment of dark, buckwheat honeys, that possessed a high
polyphenol content and high initial antioxidant activity, resulted in an overall reduction of ORAC values and a decrease
of polyphenol content in melanoidin complexes. Melanoidins
of dark honey lost the antioxidant capacity. As a result, much
stronger correlation was observed between phenolic content
and antioxidant capacity of melanoidins in unheated honeys than in heated honeys (R=0.89, p<0.0002 and R=0.72,
p<0.007, respectively) [Brudzynski & Miotto, 2011].
Evidence that thermal treatment of honey did not produce
uniform effects on antioxidant capacity of honeys was also reported by other researchers [Turkmen et al., 2006; Šarić et al.,
2013]. It has to be appreciated that heat-treatment of honey accelerated the global reduction-oxidation pathways,
and several products of the Maillard reaction could contribute to the antioxidant capacities, in addition to polyphenols.
We have also noted that heat-treatment of buckwheat honeys
caused accelerated formation of very large, insoluble, brown
aggregates that precipitate out of solution and these events
coincided with the reduction of antioxidant capacities of soluble melanoidins [Brudzynski & Miotto, 2011b].
Our preliminary observations of two types of protein-polyphenol complexes, the “protein-type” and “polyphenol-type”, carrying antioxidant and pro-oxidant capacities
in honeys requires further, thorough investigation. However,
the existence of these complexes may provide a previously
unrecognized link through which the polyphenol interaction
with proteins could directly impact either antioxidant or prooxidant capacities of food products.
Inhibition of antibacterial activity
An important functional consequence of protein-polyphenol interaction is the loss of honey’s antibacterial activity during storage [Brudzynski & Kim, 2011]. Polyphenols
alone have been shown to contribute to antibacterial activity
by binding and inactivating proteins of crucial importance for
bacterial survival [Haslam, 1996; Cushnie & Lamb, 2005].
In honey, such role has been assigned to pinocembrin [Bogdanov, 2011].
However, the highest relevance to antibacterial activity of honey might be related to polyphenol oxidation
and the propensity of quinones to react with proteins. Probable targets for quinone bindings on the bacterial cell are proteins of cell envelope and cell wall, surface-exposed adhesins
and membrane-bound enzymes [Cowan, 1999]. The decrease
of honey’s antibacterial activity during storage could be explained by increasing complexation of quinones with honey
proteins that in turn, reduced quinone availability to react
with bacterial proteins. This explanation found a partial support in the observation that the decline in antibacterial activity
during storage coincided with increasing formation of melanoidins as indicated by a significant browning (p<0.0025),
increased concentration of UV-absorbing compounds
(p<0.0001), and the appearance of polymeric structures
[Brudzynski & Kim, 2011].
CONCLUDING REMARKS
From these results, a picture emerges that redox properties of polyphenols are at the center of polyphenol-protein
interactions in honey. The polyphenol auto-oxidation leads
to formation of irreversible, covalent bonds between quinones and proteins. The increased complexation of proteins
by polyphenols during honey storage correlated with a loss
78
of enzyme activities, a decrease of antibacterial activity
and a change of the balance between antioxidant-pro-oxidant
capacities. In relation to the latter, we presented data on previously unrecognized types of protein-polyphenol complexes
that differed in size, stoichiometry, and antioxidant capacities.
The possible implication of this finding is that “polyphenol-type” complexes could carry mostly pro-oxidant activity due
to presence of oxidized polyphenols while “protein-type “complexes could be responsible for antioxidant activity. The differences in redox cycling between these two types of complexes
could partially explain a loss or gain of antioxidant capacities observed in different honeys after heating. Another new
hypothesis presented here is a potential association between
honey flavonoids and polyphenols such as association of caffeic acid with flavones-, chrysin and acecatin, and flavanone-,
pinocembrin. A phenomenon of co-pigmentation between
anthocyanins with caffeic, coumaric, chlorogenic, sinapic,
and ferulic acids is well recognized in wines [Boulton, 2001].
The importance of co-pigmentation in wines is in the protection of anthocyanins from oxidation and that it could also
merit study in honey. Finally, the environmental factors are
significantly implicated in honey structure and function
by modulating oxidation-reduction reactions in honey.
ACKNOWLEDGMENTS
This work was supported in part by the grant from the Ontario Centres of Excellence # BM50849 (K.B) and the OCE
graduate student stipend award (L.M-A). We are very grateful
to Mr. Tim Jones for LC-ESI-MS analysis of honey fractions.
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Submitted: 27 October 2014. Revised: 26 February 2015.
Accepted: 2 March 2015. Published on-line: 15 April 2015.
Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 81–85
DOI: 10.1515/pjfns-2015-0020
http://journal.pan.olsztyn.pl
Original article
Section: Food Quality and Functionality
Antioxidant Effect of Natural Honeys Affected by Their Source and Origin
Martin Mellen, Martina Fikselová*, Andrea Mendelová, Peter Haščík
Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, Nitra, 949 76, Slovakia
Key words: antioxidants, honey, authentication, phenols, ABTS method
In purpose to examine the antioxidant activity of 15 natural honeys of different origin ABTS method was used, total phenol content and dry matter
content of honey samples were determined. Honeys were collected from different locations of Slovakia, Poland and Serbia and were represented as
monofloral and multifloral samples (10) which originated from Poland and Slovakia, forest samples (4) originated from Serbia and honeydew honey.
Average values of antioxidant activity observed in samples of honeys ranged from 0.62 to 4.63 mmol/kg. The highest antioxidant activity was detected
in buckwheat honey and the lowest was shown in acacia honey. By observing the impact of individual honey samples on antioxidant activity it was found
that the sample had a highly statistically significant effect. 10 homogeneous groups which varied in the antioxidant activity among each other were
established by all 15 samples. Antioxidant activity of honeys could be a positive influence factor in terms of honey differentiation, especially in the case
of the forest honeys collected from different places. Monofloral and multifloral honeys (10) established 5 homogenous groups, but in the case of several
multifloral honeys which originated from different places of Poland and Slovakia no statistically significant differences were found.
INTRODUCTION
By the Codex Alimentarius honey is the natural sweet substance, produced by honeybees from the nectar of plants or
from secretions of living parts of plants, or excretions of plant-sucking insects on the living parts of plants, which the bees
collect, transform by combining with specific substances
of their own, deposit, dehydrate, store and leave in honeycombs to ripen and mature. The colour, aroma and consistency of honey all depend upon which flowers the bees have
been foraging. The consumption of honey is increasing because of its beneficial biological properties, including antioxidant and antibacterial activities [Montenegro & Mejias,
2013]. A large number of in vitro and limited clinical studies
have confirmed the broad-spectrum antibacterial, antifungal,
antiviral, and antimycobacterial properties of honey, to immune modulating and anti-inflammatory properties of honey
[Israili, 2014]. In our previous study, we tested potential antimicrobial activity of selected honeys against four species
of bacteria (Escherichia coli CCM 3988, Pseudomonas aeroginosa CCM 1960, Staphylococcus epidermis CCM 4418, Bacillus cereus CCM 2010) and two species of yeasts (Saccharomyces cerevisiae CCM 8191, Candida albicans CCM 8216).
The strongest antimicrobial activity was shown in honey
samples of 50% concentration against Escherichia coli, Pseudomonas aeroginosa and Staphylococcus epidermis [Fikselova
et al., 2014].
* Corresponding Author: E-mail: [email protected]
(M. Fikselova)
Antioxidant activities were demonstrated in commercial
Indian honeys, which may have therapeutic potential, though
honey has a significant amount of phenols [Saxena et al., 2010].
Monofloral Malaysian honeys were analysed by Hussein
et al. [2011] to determine their antioxidant activities and total
phenolic and flavonoid contents, with and without gamma
irradiation. Honey can scavenge free radicals and exhibit
high antioxidant-reducing power, in good correlation with its
phenolic content [Hussein et al., 2011]. Honey provides sugars and other nutrients, such as mineral elements, proteins,
and antioxidant active compounds. The presence of several
minor components and the antioxidant activity in honey
is related to the botanical origins of the product [Escuredo
et al., 2013]. It is a rich source of antioxidant and antiseptic
compounds including Maillard reaction products, vitamins,
carotenoids and polyphenols [O’Sullivan et al., 2013]. Carotenoids were the predominant floral pigments in several tested
honeys, while xanthophylls and anthocyanins were the least
predominant ones [Alqarni et al., 2012], total chlorophylls
were also found.
By the European Parliament resolution there are Calls on
the Commission to respond to the requests for example by improving statistical data in relation to production forecasts, including the application of the same quality requirements for
honey, Calls to consider, in the framework of the legislative
proposal on agricultural quality policy, changing the rules on
origin labelling of honey in order to avoid misleading information to consumers, especially in the case of a blend of honeys originating from EU and non-EU. Therefore quality
parameters of honey are studied in order to confirm the au-
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
82
thenticity, the food safety and also to provide the nutritional
values of regional honeys. Physicochemical properties, main
mineral content and antioxidant activity were determined
in Portuguese honeys, statistical analysis demonstrated that
antioxidant parameter and main mineral content had a positive influence on honeys differentiation [Alves et al., 2013].
New criteria based on regional characteristics of Saudi honeys including antioxidants, micro-constituents were suggested
by Alqarni et al. [2012].
Therefore the purpose of this study was to determine
the antioxidant activity of 15 natural honeys in samples originated from different sources and areas, also in order to test
the influence of honey origin on its antioxidant activity. Antioxidant methods were used to support the determination
of the total phenolic content in the analysed honeys.
MATERIALS AND METHODS
Honey samples (Table 1) were collected from different locations of Slovakia (6 samples), Poland (5 samples)
and Serbia (4 samples) and were obtained directly from local
beekeepers. Honeys were represented as monofloral and multifloral samples (10) which originated from Poland and Slovakia (Table 1), forest samples (4) originated from Serbia
and honeydew honey (1) from Slovakia.
Antioxidant activity determination
Samples of honey (2 g) were mixed with 10 mL of distilled water. The prepared mixture was centrifuged for 5 min at
10,000 rpm and a temperature of 20°C. Extracts prepared were
stored during the experiments in a refrigerator at 4–6°C. Experiments were performed either directly, or samples were further diluted before measurement as needed.
ABTS test of honeys
ABTS•+ solution was prepared as described Re et al.
[1999]. Before use of 1 mL of this solution, ABTS•+ was diluted with 50 mL of distilled water. Concentration of the solution prepared was around c=1×10–4 mol/L.
Aqueous solution of ABTS•+ (1×10–4 mol/L) was put into
a syringe (1 mL volume) and into identical second syringe
there was put 1 mL of aqueous solution of honey. Both syringes were connected with a micro-mixing chamber, connected into an EPR cell (internal volume 400 μL), during
the measurement permanently placed in the cavity of the EPR
spectrometer (Bruker, Germany). This arrangement allowed
to start the measurement at the moment of mixing the sample
and ABTS•+. The time evolution of 15 spectra for 22.5 min
of mixing ABTS•+ with a solution of honey was observed.
Each spectrum represents the average of 30 individual scans.
Distilled water was used as a reference sample. All measurements were performed in duplicates and EPR spectrometer
was used for determination.
EPR spectrometer parameters were as follows: Central
field: 350 mT, Sweep width: 9 mT, Modulation amplitude:
0.052 mT, Receiver gain: 4.48.103 G, Source performance:
6 mW, Frequency of microwave radiation: 9.81 GHz.
The measured EPR spectra were processed in programs
WINEPR (Bruker ®) and ORIGIN (MicroCalc ®). Results
Antioxidant Effect of Natural Honeys
TABLE 1. Investigated honey samples.
No
Sample
Origin
1
forest
Serbia (Milevici), altitude 800 m
2
forest
Serbia (Babine), altitude 1250 m
3
forest
Serbia (Jabuka), altitude 1250 m
4
(multi)floral
Poland
5
heather
Poland
6
(multi)floral
Poland
7
floral (rape)
Slovakia (Nitra)
8
acacia
Slovakia (Nitra)
9
(multi)floral
Slovakia (Michalovce)
10
forest raspberry
Slovakia (Bystrá)
11
forest
Serbia, altitude 1000 m
12
buckwheat
Poland
13
(multi)floral
Poland
14
(multi)floral
Slovakia (Stupava)
15
honeydew
Slovakia (Relov)
were expressed as Trolox equivalent values (TEACABTS • + ) as
follows :
whereas: c0 (ABTS•+), ct (ABTS•+) is concentration of ABTS•+
in time t=0, resp. t=10.5 min.; V(ABTS•+) is volume of ABTS•+
added; V(vzorky) is volume of sample added;  is stoichiometric
coefficient of the reaction ABTS and TROLOX, in this case
=1/2; and Z is the dilution factor.
Determination of total phenol content (TPC) in honey
Total polyphenol content was determined according to
the modified method using the Folin-Ciocalteau reagent
[Singleton et al., 1999]. Exactly 200 mL sample of the aqueous solution of honey was mixed with 15.8 mL of distilled
water and with 1 mL of Folin-Ciocalteau reagent. After
10 min, 3 mL of a 20% sodium carbonate solution was added
and the mixture was stirred well. After 60 min, absorbance
of each solution was measured at 765 nm. The results were
expressed as gallic acid equivalent (GAE mg/kg). Calibration
curve was prepared using standards of gallic acid in the range
of 0–1000 mg/L. All measurements were performed in duplicate. Determination was performed with the use of UV-VIS-NIR Shimadzu spectrometer (UV - 3600 with accessories).
Determination of dry matter content
Dry matter content of honeys was determined refractometrically by digital refractometer [Kačániová et al., 2009].
Statistical analysis
The mean values and standard deviations were calculated.
Data were elaborated with the analysis of variance (ANOVA),
83
M. Mellen et al.
Fisher’s least significant difference (LSD), and Pearson’s correlation coefficient were determined. Analysis of the results
was performed using the statistical software Statistica 8.0
[Statsoft, Inc. 2008].
TABLE 2. Analysis of variance (ANOVA) for ABTS (mmol/kg) by sample.
Source
of variability
Between
groups
RESULTS AND DISCUSSION
Antioxidant effect of honeys determined by the ABTS
method
Mean values of the antioxidant activity in the analysed
samples of honeys determined with the ABTS method ranged
from 0.62 to 4.63 mmol/kg. By monitoring the impact of individual honey samples on the antioxidant activity it was found
that the sample had a highly statistically significant effect
(Table 2). By further testing with the use of the Fisher test
there were monitored differences among groups of samples
of honey in terms of the antioxidant activity. Ten homogeneous groups were established by 15 samples of honeys that
varied in the antioxidant activity among each other (Table 3).
Results of ABTS assay showed that the mean antioxidant activity in all multifloral honeys ranged from 0.64 to
1.47 mmol/kg, including from 0.9 to 1.42 mmol/kg in multifloral honeys originating from Poland and from 0.64 to
1.47 mmol/kg in Sloval multifloral honeys. All analysed
monofloral and multifloral honeys (10) established 5 homogenous groups (Table 3). In the same homogenous groups
(b,d), there were found multifloral honeys originating from
Slovakia and from Poland as well, so there were no statistical
differences among them regarding antioxidant activity.
The lowest antioxidant activity was evaluated in a sample of acacia honey (0.62 mmol/kg). In Slovenia, with using the FRAP and the DPPH methods, results showed that
the antioxidant activity varies depending on the type of honey.
Similarly, the honey from Acacia belonged to the antioxidatively weakest sample [Bertoncelj et al., 2007; Fikselová et al.,
2014]. Floral honeys were observed to be low at antioxidant
effect by DPPH method as well as at antimicrobial activity
[Fikselová et al., 2014].
Forest honeys originating from Serbia were proved to
have more effective antioxidant activity ranging from 1.58 to
2.71 mmol/kg. Dark honeys were shown to be antioxidative
the best samples in several studies [Bertoncelj et al., 2007;
Wilczyńska 2010], they are often a rich source of vitamins
and minerals, but their variation in the content of the various honeys is large [Bradbear et al., 2009]. Four forest
samples monitored in our study (No. 1,2,3,11) created four
homogenous groups, which shows that their origin had
a significant effect on their antioxidant effect. Even forest
samples (no. 2 and 3) from the same altitude (1250 m) but
originating from different places of Serbia created different
homogenous groups (i, fg).
The ABTS assay showed good antioxidant effect of honeydew honey (2.12 mmol/kg). Honeydew and chestnut honeys produced in a European Atlantic area had the highest
mineral, protein, and flavonoid contents, as well as the highest antioxidant activities [Escuredo et al., 2013]. The highest
antioxidant activity in our study showed buckwheat honey
(4.63 mmol/kg), which was confirmed also in our previous
research by DPPH method [Fikselová et al., 2014]. High an-
Sum
of squares
Df
Mean
square
F-Ratio
30.03
14
2.15
678.98**
Within groups
0.05
15
000
–
Total
30.08
29
–
–
** statistically significant at <0.01; df - degree of freedom; n=30.
TABLE 3. The average values of ABTS method determined in honeys
and homogeneous groups based on Fisher’s test.
No.
of sampleb
ABTS (mmol/kg)
Conf. intervals
Mean a
-95 %
+95 %
8
0.62
a
0.54
0.71
7
0.64 a
0.56
0.73
14
0.85 b
0.77
0.94
13
0.90 b
0.81
0.98
4
1.11
c
1.03
1.20
6
1.42 d
1.33
1.50
9
1.47 de
1.38
1.55
5
1.49 de
1.40
1.57
11
1.58
e
1.49
1.66
15
2.12 f
2.04
2.21
3
2.19 fg
2.11
2.28
1
2.25 g
2.16
2.33
10
2.45
h
2.36
2.53
2
2.71 i
2.62
2.79
12
4.63 j
4.55
4.72
Values in the same column with different letters are significantly different (<0.05).
b
1 forest (Serbia), 2 forest (Serbia), 3 forest (Serbia), 4 floral (Poland),
5 heather (Poland), 6 floral (Poland), 7 floral, rape (Slovakia), 8 acacia
(Slovakia), 9 floral (Slovakia), 10 forest raspberry (Slovakia), 11 forest
(Serbia), 12 buckwheat (Poland), 13 floral (Poland), 14 floral (Slovakia),
15 honeydew (Slovakia).
a
tioxidative effect is mainly due to flavonoids content, which
represent a major group of natural antioxidants in buckwheat.
Following our previous results [Ivanišová & Fikselová, 2010],
the highest antiradical activity measured by DPPH method
was shown for the sample of buckwheat extract as well.
Phenolic compounds variation in samples of honeys
A number of studies assumed that eating plant foods containing phenolic compounds may contribute significantly to
health improvement [Naczk & Shahidi, 2004]. Honey is a rich
source of phenolic acids and flavonoids [Farooqui et al., 2011],
polyphenolic compounds of honey act as natural antioxidants and are becoming increasingly popular because of their
84
Antioxidant Effect of Natural Honeys
TABLE 4. The average levels of polyphenols (TPC) expressed as gallic
acid equivalent and homogeneous groups based on Fisher’s test.
TPC (mg/kg)
Mean
Conf. interval
-95 %
+95 %
13
611.20 a
593.35
629.04
8
635.98
a
618.14
653.82
7
703.72 b
685.88
721.57
14
704.20 b
686.36
722.04
11
769.50 c
751.66
787.34
1
804.64
d
786.80
822.48
15
899.72 e
881.87
917.56
2
909.17 e
891.33
927.01
6
961.19 f
943.35
979.03
4
990.01
972.17
1007.85
3
1052.09 h
1034.25
1069.93
5
1124.73 i
1106.89
1142.57
10
1244.80 j
1226.95
1262.64
9
1257.12
j
1239.28
1274.96
12
2962.24 k
2944.40
2980.08
g
Values in the same column with different letters are significantly different (<0.05).
2
1 forest (Serbia), 2 forest (Serbia), 3 forest (Serbia), 4 floral (Poland),
5 heather (Poland), 6 floral (Poland), 7 floral, rape (Slovakia), 8 acacia
(Slovakia), 9 floral (Slovakia), 10 forest raspberry (Slovakia), 11 forest
(Serbia), 12 buckwheat (Poland), 13 floral (Poland), 14 floral (Slovakia),
15 honeydew (Slovakia).
a
TABLE 5. Analysis of variance (ANOVA) for TPC (mg/kg) by sample.
Source
of variability
Between
groups
Within groups
Total
Dry matter content variation in samples of honeys
The last parameter observed was dry matter content
of honeys. The mean content of the dry matter in honey
samples (Figure 1) ranged from 78.2 to 83.77%. By monitor-
Sum
of squares
Df
Mean
square
F-Ratio
9,076,740.00
14
648,339.00
4626.15**
85
2102.20
15
140.15
–
84
9,078,840.00
29
–
–
83
** statistically significant at <0.01; df - degree of freedom; n=30.
potential role in the protection of human health. These substances can also be used as an indicator of geographical origin
and source of honey [Tulipani et al., 2009]. Correlation analysis related the contents of the polyphenolic compounds with
the antioxidant activities of the honeys, indicated that the flavonoids had a great influence on this activity [Escuredo et al.,
2013]. Thirty two samples of different types of Polish honeys
were investigated by Wilczyńska [2010] in order to assess their
total phenolic content and potential antioxidant activity. Results
of the study showed that the total phenolic content and antioxidant activity differed widely among different honey types.
Mean TPC in our samples expressed as gallic acid equivalent ranged from 611.20 to 2962.24 (mg/kg). By monitoring
the impact of individual samples on TPC it was found that
Dry matter (%)
No.
of sample
the sample had a highly statistically significant effect. Samples formed 11 homogeneous groups which differed among
each other in their TPC (Table 4 and 5). Four forest samples
established similarly 4 homogenous groups as in the case
of the antioxidant activity .
TPC in multifloral honeys ranged from 611.00 to
1257.12 mg/kg, within them in honeys originating from Poland it ranged from 611 to 990 mg/kg, and Slovak multifloral
honeys it ranged from 703.7 to 1257.12 mg/kg.
Forest honeys originating from Serbia were found to have
similar TPC, which ranged from 769 to 1052 mg/kg. Although
the forest honeys were found to be more effective antioxidants
in general compared to floral ones, in TPC they were similar,
therefore it can be seen that not only phenolic substances are
involved in the antioxidant effectiveness of forest honeys.
The highest TPC (2962.24 mg/kg) was found in a sample
of buckwheat honey, which is in accordance with our antioxidative results of buckwheat honey. Phenolic content of the honey
samples is partially responsible for their antioxidant activity,
which supports the relevance of this type of honey being an
important dietary source of antioxidant compounds and its
traditional use as a medicinal product [Silva et al., 2013].
Total polyphenols of honeydew honeys originating
from Romania, Bulgaria, Croatia, Greece and Turkey were
evaluated by Bobis et al. [2011]. Polyphenols, expressed as
mg GAE/100 g, ranged from 53.91 to 196.0 mg GAE in honey
from Romania; from 118.46 to 133.12 mg GAE in honey from
Bulgaria; from 94.58 to 133.92 mg GAE in honey from Croatia etc. Honeydew honey from our study originating from
Slovakia showed a high content of polyphenolic compounds
in the amount of 899.72 mg/kg GAE.
82
81
80
79
78
77
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Sample
FIGURE 1. Variability of dry matter content (%) in honeys. Explanations:
1 forest (Serbia), 2 forest (Serbia), 3 forest (Serbia), 4 floral (Poland),
5 heather (Poland), 6 floral (Poland), 7 floral, rape (Slovakia), 8 acacia
(Slovakia), 9 floral (Slovakia), 10 forest raspberry (Slovakia), 11 forest
(Serbia), 12 buckwheat (Poland), 13 floral (Poland), 14 floral (Slovakia),
15 honeydew (Slovakia).
M. Mellen et al.
ing the impact of individual honey samples on the dry matter
content of honey we confirmed that the sample has a highly
statistically significant effect (not shown). Further testing
by the Fisher test showed relative differences among samples
of honey in dry matter content, 11 homogenous groups were
formed (not shown).
SUMMARY AND CONCLUSIONS
Antioxidant activity of honeys could be a positive influence
factor in terms of honey differentiation, especially in the case
of the forest honeys collected from different places. The analysed monofloral and multifloral honeys (10) established 5 homogenous groups, but in the case of several multifloral honeys which originated from Poland and Slovakia no statistical
significant differences were found.
Forest honeys were proved to have more effective antioxidant effect compared to floral ones, although their phenolic
content was similar to floral honeys. The ABTS assay showed
good antioxidant activity of honeydew honey, the highest antioxidant activity had buckwheat honey, while the antioxidative
weakest sample was the sample of acacia honey. By observing
the impact of individual honey samples on the antioxidant activity it was statistically confirmed that the sample has a highly
statistically significant effect and similar trend was determined
in the case of total phenol and dry matter content of honeys.
ACKNOWLEDGEMENTS
For helping us in this research study we thank our colleagues prof. Miroslava Kačaniová from the Slovak University of Agriculture in Nitra, assoc. prof. Malgorzata Džugan
from the University of Rzeszow (Poland) and Dr. Nenad
Vukovič from the University of Kragujevac (Serbia) who have
kindly supported us by honey samples from different places.
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Submitted: 5 August 2014. Revised: 7 October and 30 November
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Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 87–91
DOI: 10.1515/pjfns-2015-0024
http://journal.pan.olsztyn.pl
Original article
Section: Food Chemistry
Antioxidant Properties of Honey from Different Altitudes of Nepal Himalayas
Bishnu Prasad Neupane1*, Komal Prasad Malla1, Atis Kaundinnyayana1,
Prakash Poudel1, Rashmi Thapa1, Sabina Shrestha2
School of Health and Allied Sciences, Pokhara University, Lekhnath-12, Kaski, PO Box No. 427, Nepal
Faculty of Biotechnology, College of Applied Life Science, Jeju National University, Jeju 690–756, Republic of Korea
1
2
Key words: Nepalese honey, antioxidant activity, phenolic content, physicochemical parameters
Twenty two multifloral honey samples representing central western parts of Nepal were examined spectrophotometrically for their antioxidant
properties and total phenol content. The modified Folin-Ciocalteu method was used to determine total phenol content and 2,2-diphenyl-1-picrylhydrazyl radicals (DPPH•) assay for antiradical activity. In all samples, physicochemical parameters like moisture, reducing sugar, sucrose, ash, free
acidity and water insoluble matter were also measured according to harmonized methods of the International Honey Commission (IHC). The results
of physicochemical analysis showed that all the values, except for moisture of a small number of high altitude honey samples, are in good agreement
with the current Nepalese standard. The total phenolic contents of honey, collected from high and low altitude, ranged from 154.87 to 41.90 mg gallic
acid equivalent (GAE/100 g) respectively, at corresponding antiradical activity using DPPH• expressed as percent inhibition of 76.66% and 25.69%.
The IC50 values of selected high altitude honey samples ranged from 56 to 72 mg/mL. The total antioxidant properties were correlated (P<0.01) between total phenol content and antiradical activity (r=0.992). The obtained results demonstrate that the Nepalese honey collected from high altitude
region contained more antioxidants than honey of low altitude region.
INTRODUCTION
Honey is naturally sweet and viscous liquid made from
the nectar of flowers collected by honey bees. It comes in numerous varieties with different colours, textures and flavours.
The flavour, colour and sweetness of honey depend on climatic
and environmental conditions and diverse botanical origins
from which it is harvested [Gheldof & Engeseth, 2002; Küçük
et al., 2007; Aljadi & Kamaruddin, 2004]. Man’s use of honey
goes back tens of thousands of years for its nutritional as well
as curative purpose [Kaal, 1991]. The usage has continued
into present-day folk medicine and is increasingly becoming
a part of modern professional medicines. The role of honey
in the treatment of various ailments has received a considerable attention recently, and its therapeutic value has been partly
attributed to its antioxidant properties [Gheldof & Engeseth,
2002; Aljadi & Kamaruddin, 2004]. Antioxidant molecules
prevent or inhibit reactive oxygen species (ROS) produced
in metabolic and physiological process, and harmful oxidative
reactions occurring in organisms [Young & Woodside, 2001].
Under certain conditions, the increase in oxidants and decrease in antioxidants cannot be prevented, and the oxidative
or antioxidative balance shifts towards the oxidative status.
Consequently, oxidative stress can be responsible for over
* Corresponding Author: Tel.: +977–9841533885;
E-mail: [email protected] (Bishnu Prasad Neupane)
100 disorders [Halliwell et al., 2000]. Therefore, as food additive antioxidant potential of honey can play a positive role to
overcome human health issues. Many methods for determining the antioxidative activity in honey have been used such as
determination of active oxygen species, their radical scavenging
ability [Gheldof & Engeseth, 2002], the 2,2-diphenyl-2-picrylhydrazil (DPPH) antioxidant content [Chen et al., 2000]
and enzymatic and non-enzymatic measurements of lipid peroxidation inhibition [Chen et al., 2000; McKibben & Engeseth,
2002; Nagai et al., 2001]. The selection of honey samples with
various altitude origins in this study was based on the assumption that varying total phenol content and antioxidant capacity is expected for honey produced from diverse floral sources
having different geographic regions of Himalayan foothills to
the plains of Terai region. The extensive exploitation of honey
combs by honey hunters and traders has led to drastic decline
of accessible honey combs of Himalayan rock bee (Apis laboriosa) in Himalayan region. In spite of its high medicinal values,
virtually little research data are available on the phenolic antioxidants and radical scavenging activity in the context of Nepal.
The aim of this study was to determine the antioxidant
properties of twenty two honey samples available in central
western part of Nepal, with special focus on two different
altitudinal ranges of honey collection, high altitude (1500–
–3500 m asl) and low altitude (800–1500 m asl). In addition,
in all of the honey samples, physicochemical parameters were
also measured.
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
88
MATERIALS AND METHODS
Honey samples
Twenty two raw honey samples of varying geographical
origins (11 from low altitude, 800–1500 m asl and 11 from
high altitude, 1500–3500 m asl) were used for this study.
The honey samples were obtained directly from beekeepers
and honey hunters of Kaski district of Nepal during AprilMay 2013. All tested honey samples were multifloral honeys as derived from at least 55% pollen contribution from
more than one floral source [Von der Ohe et al., 2004]. Five
grams of each honey sample was distributed into test tubes
and diluted to 50 mL with distilled water using a vortex mixture. The solution was then filtered through Whatman No.
1 filter paper and analysed for physicochemical parameters,
total phenol content and antioxidants. The tests of all determination were performed in triplicate and expressed as
mean±SD.
Chemicals
Folin-Ciocalteau reagent and 2,2-diphenyl-2-picrylhydrazine (DPPH•) free radical were purchased from Sigma-Aldrich, Germany. All other chemicals and reagents used in this
study were of analytical grade.
Physicochemical parameters
In all samples, the physicochemical parameters such as
moisture, reducing sugars, sucrose, ash, free acidity, water insoluble matter and pH were determined according to
the methods recommended by International Honey Commission [Bogdanov, 2009]. pH was determined using pH meter
(PH500 Benchtop) by dissolving 10 g honey sample in 75 mL
carbon dioxide free water. The free acidity was quantified volumetrically, titrating a honey sample with a solution of 0.05 N
NaOH, up to pH 8.3, and expressing the results in milliequivalent of acids at 1000 g of honey. Moisture was determined using the refractometric method of Chataway [1932].
All measurements were taken using an Abbe refractometer,
and the moisture (g /100 g honey) was obtained from the refractive index of the honey sample by consulting a standard
table (Chataway table). Sugar and sucrose were determined
by Fehling solution method [Lane & Eynon, 1923]. For
the determination of water insoluble matter, the gravimetric
method was used. Twenty grams of honey were diluted with
200 mL water, filtered through crucible and washed carefully, until free from sugars. The presence of sugars was tested
by the addition of 1% phloroglucinol in ethanol to some filtrate and few drops of concentrated sulphuric acid, because
sugars produce colour at the interface. The crucible was dried
at 135±1oC for an hour [Lord et al., 1988]. The gravimetric methodology was used for the determination of ash content. Ten grams of the sample were transferred to the crucible
and two drops of olive oils were added. Afterwards, the sample was heated in a hot plate until carbonized. The sample
was kept in the preheated furnace at 600±25oC for at least
one hour. The crucible was cooled in desiccator and weighed.
The ashing procedure was continued until constant weight
has been reached.
Antioxidant Properties of Honey from Different Altitudes of Nepal Himalayas
Total phenolic content
The determination of the total phenol (TP) content of honey samples was performed according to the Folin-Ciocalteau
method with slight modifications [Singleton et al., 1999].
A 0.5 mL of aliquot of the freshly prepared honey solution was
added to 2.5 mL of 0.2 N Folin-Ciocaleau reagents and mixed
for 5 min, followed by the addition of 2 mL of 75 gm/L sodium carbonate. After incubation at room temperature for
2 h, the absorbance of the reaction mixture was measured
at 760 nm. The TP content was expressed as mg gallic acid
equivalents GAE/100 g of honey, using the calibration curve
of gallic acid (0–200 mg/L) standards [Meda et al., 2005].
Antiradical activity
Antiradical activity of honey samples were determined
by using the 2, 2-diphenyl-1-picrylhydrazyl radicals (DPPH.)
assay. It was measured according to the method previously
described by Zhang & Hamauzu [2004]. Each honey sample was precisely diluted to 4o BX (Refractrometer, ERMA,
Tokyo) with distilled water. A 1.5 mL aliquot of 0.1 mmol/L
DPPH• solution in methanol was mixed on a vortex and left
to stand at 25oC in the dark for 60 min. Then, the decrease
in absorbance was measured at 517 nm on spectrophotometer against a methanol blank. The radical scavenging activity
(A %) was calculated from the following equation:
A % =
A0 – AA
A0
×100
where AA - absorbance of the studied sample and A0 - absorbance of the control sample. The parameter IC50 was also
determined for those samples having high radical scavenging activity. IC50 parameter was calculated from linear fitting
of the radical scavenging activity to the DPPH radical as
a function of antioxidants concentration (20–100 mg/mL).
Absorption measurements were performed using a UV-VIS
Shimadzu 1601spectrophotometer.
Statistical analysis
The results of all experiments were expressed as mean
± SD values and are representative of three independent
experiments. Statistical analysis was carried out by t-test
(2 tailed) one using PRISM version 5.0 statistical analysis software (GraphPad Software, Inc., San Diego). Values
of P<0.01 were considered significant.
RESULTS AND DISCUSSION
Physicochemical parameters
The seven different physicochemical parameters namely; moisture, reducing sugar, sucrose, ash, free acidity, pH
and water insoluble matter were summarised in Table 1.
The moisture of four high altitude honey samples (2, 5, 6
and 7) was found in between 29.1–25.0%. These values are
higher than the maximum permissive content for honey described by Nepalese standard. The permissible standard value of moisture for the honey in Nepal is not more than 23%
[FNCCI/AEC, 2006]. Moreover, the results of physicochemical studies showed that all the average values of different
89
B.P. Neupane et al.
TABLE 1. Physicochemical parameters of the Nepalese honey samples (n = 22).
Sample
No.
Moisture (%)
(Mean±SD)
Reducing sugars (%)
(Mean±SD)
Sucrose (%)
(Mean±SD)
Ash (%)
(Mean±SD)
Free acidity (mmol/kg) Water insoluble matter
(Mean±SD)
(%) (Mean±SD)
pH
High altitude sample
1.
22.0±0.34
62.55±0.70
4.4±0.06
0.05±0.00
14.0±0.15
0.3±0.14
4.76
2.
25.3±0.70
64.03±2.33
2.55±0.99
0.04±0.00
13.0±0.13
0.25±0.13
4.80
3.
20.6±0.70
66.57±0.84
5.57±0.49
0.04±0.00
27.7±0.16
0.22±0.12
4.56
4.
21.3±0.30
64.77±0.80
5.84±0.85
0.44±0.07
31.3±0.14
0.12±0.11
4.38
5.
29.1±0.20
61.71±2.03
4.83±0.90
0.14±0.05
43.3±0.10
0.11±0.13
4.89
6.
25.5±0.50
62.51±1.05
2.87±0.81
0.45±0.06
44.7±0.11
0.15±0.10
5.09
7.
26.13±0.23
67.85±0.78
5.65±1.58
0.37±0.12
34.7±0.15
0.14±0.14
4.44
8.
20.13±0.23
63.15±1.81
6.51±0.42
0.34±0.92
33.7±0.17
0.16±0.16
4.51
9.
21.13±0.30
71.44±6.43
5.16±0.05
0.20±0.00
17.3±0.12
0.19±0.15
4.68
10.
22.60±0.20
64.87±2.62
3.16±0.58
0.32±0.04
17.3±2.54
0.10±0.14
4.70
11.
23.20±0.20
68.25±0.43
4.13±0.01
0.24±0.00
12.7±3.67
0.18±0.13
4.85
Low altitude sample
12.
22.01±0.10
62.15±0.10
6.35±0.02
0.35±0.81
14.0±0.18
0.13±0.12
4.79
13.
20.12±2.01
65.13±1.00
3.39±0.01
0.25±0.25
13.3±0.11
0.15±0.11
4.71
14.
20.25±2.10
63.71±2.00
2.38±0.05
0.31±0.12
11.3±0.14
0.11±0.8
4.72
15.
27.05±1.05
62.12±1.50
8.55±0.20
0.45±0.12
52.3±0.13
0.12±0.6
4.84
16.
25.02±1.01
56.02±2.15
5.25±0.91
0.31±1.50
53.3±0.16
0.14±0.9
4.84
17.
22.66±1.50
59.13±1.25
7.58±0.30
0.48±1.60
15.0±0.18
0.11±0.10
4.68
18.
21.1±0.20
55.23±1.65
4.34±0.49
0.37±0.05
20.7±0.12
0.16±0.11
4.48
19.
22.46±0.15
57.21±0.056
3.01±0.45
0.47±0.01
22.0±0.13
0.14±0.13
4.52
20.
22.23±0.22
68.32±0.55
6.75±0.75
0.48±0.20
54.7±0.13
0.16±0.13
4.84
21.
22.01±0.24
62.12±0.34
4.02±0.15
0.35±0.15
51.3±1.50
0.10±0.16
4.88
22.
21.1±0.20
55.23±1.65
3.45±0.15
0.47±0.01
34.0±0.13
0.15±0.14
4.13
parameters were within the limits set by Nepalese standard
indicating the use of good practices by bee farmers in central western part of Nepal. The noted higher moisture content in high altitude honey samples might be due tendency
of bees to build hives near water source and dependence on
water for cooling as well as thinning honey to be fed to larva
and the immediate collection of raw honey from the natural
habitat along with some waxes.
Antioxidant properties
Since phenolic substances have been shown to be responsible for the honey antioxidant activity, total phenol content
of the honey samples was investigated. The results of antioxidant activity and total phenolic content (TP) of the samples
determined by DPPH assay and Folin-Ciocalteu method are
presented in Table 2. Significant TP content differences were
recorded among the honey types. The total phenolic contents
were found to vary from 41.90 to 72.14 mg GAE/100 g in low
altitude and 65.23 to 154.87 mg GAE/100 g in high altitude
honey samples respectively. Similar phenolic contents (78.96–
–114.75 mg GAE/100 g) of several honeys from variuos floral
sources were reported in literature, among which the highest
TP contenet was found in strawberry tree (Fragaria ananassa)
honey and honeydew honey samples [Gheldof & Engeseth,
2002; Beretta et al., 2005; Bertoncelj et al., 2007]. Several investigations have found a significant level of phenolic compounds in the honey samples of different floral origins [Meda
et al., 2005; Bertoncelj et al., 2007; Ferreres et al., 1991; Gil
et al., 1995; Martos et al., 2000; Blasa et al., 2006]. There
is a significant difference (P<0.01) between the mean TP
content of low altitude honeys 61.77 mg GAE/100 g and high
altitude honeys 118.65 mg GAE/100 g.
The antiradical activity of tested honeys was evaluated
and found significantly potential in the DPPH• radical reaction system. The radical scavenging potential, expressed
as % inhibition, of honey collected from high and low altitude with respect to DPPH radical was in between 76.66–
–38.23% and 37.27–25.59% respectively (Table 2). The average percent inhibition determined in the high altitude
honey (59.53%) was found to be significantly higher than
90
Antioxidant Properties of Honey from Different Altitudes of Nepal Himalayas
TABLE 2. Total phenol (TP) content with respect to gallic acid equivalents (GAE) and antiradical activity by DPPH• assay of the analysed
honey samples.
TP
(mg GAE/100 g±SD)
DPPH assay
(~% inhibition±SD)
High altitude samples
1.
150.60±3.69
74.553±6.096
2.
154.87±3.41
76.667±2.814
3.
115.87±3.20
58.354±6.61
4.
114.52±2.65
59.167±8.04
5.
104.65±3.44
51.809±2.16
6.
98.45±3.34
50.224±2.70
7
139.18±4.23
68.902±0.38
8
135.04±3.66
66.850±1.07
9.
144.85±1.34
71.707±1.25
10.
65.23±3.45
38.232±3.22
11.
77.60±3.55
38.415±2.63
Mean
118.65
59.53
Low altitude samples
12.
41.90±3.42
25.69±3.694
13.
61.91±5.35
30.65±8.676
14.
61.32±3.50
35.30±2.076
15.
59.45±625
29.43±2.928
16.
69.55±3.78
34.43±2.144
17.
65.22±7.49
37.24±1.890
18.
66.02±3.25
32.68±10.020
19.
55.41±5.25
34.86±2.542
20.
59.25±3.30
29.33±5.770
21.
67.41±1.28
36.34±1.582
22.
72.14±3.56
35.71±2.782
Mean
61.77
32.87
that of the low altitude honey (32.87%). It is difficult to
compare the obtained values with data obtained by other
authors that investigated Polish honeys due to different
modes of presentation such as results expressed as mmol
TEAC/kg [Rodríguez et al., 2012; Kuś et al., 2014]. However, the results are similar to those obtained by Socha et al.
[2011]. A significant correlation was found between antioxidant activity determined by DPPH assay and phenolic
content (r=0.992). Similar to our findings, some literature
also reported strong correlation between the antioxidant
capacity and total phenol content (r=0.873) [Beretta et al.,
2005; Bertoncelj et al., 2007]. Among all honeys tested for
their antioxidant capacity, the high altitude honey samples
(Sample 1, 2 & 9) with the highest percentage inhibition
in DPPH• assay were subjected for IC50 determination.
80
Scavenging the DDPPH radical (%)
Sample
no.
100
60
40
Sample 2
Sample 1
20
Sample 9
0
0
20
40
60
80
100
Concentration of honey (mg/mL)
FIGURE 1. The capability of selected high altitude honey samples to
scavenge the DPPH radical as a function of honey concentration for
the determination of IC50 values.
From the results obtained it follows that sample 2 had big
capability to scavenge the DPPH radicals with IC50 equal
to 56 mg/mL. Samples 1 and 9 also had considerable capacity to scavenge DPPH radicals than low altitude honey
samples and their IC50 equalled 68 mg/mL and 72 mg/mL,
respectively. One can conclude that the values of IC50 parameter determined in this paper are comparable to those
determined by other authors [Meda et al., 2005; Krpan
et al., 2009; Kuś et al., 2014]. Figure 1 shows concentration
dependence of the DPPH reduction for selected antioxidant
high altitude honey samples.
CONCLUSION
The results of antiradical activity with respect to DPPH•
radical and total phenol content revealed that the high altitude honey contained a higher level of antioxidants than
low altitude honey, justifying that there would be the chance
of synthesis of highly potent antioxidative secondary metabolites by the plants grown at high altitude Himalayan
regions to cope with the harsh and extreme climatic conditions. The findings of present research demonstrated that
the honey made from high altitude nectar by honeybees from
plants grown at high altitude regions possessed high antioxidant capacity.
ACKNOWLEDGEMENTS
This research was funded by Research Division, University Grants Commission, Nepal, under the title Faculty Reserch Grants-2013. We are grateful to all local bee farmers
and honey hunters of Kaski district, Nepal, for their coopertions during the field visits.
91
B.P. Neupane et al.
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Submitted: 29 September 2014. Revised: 18 February 2015.
Accepted: 23 February 2015. Published on-line: 15 April 2015.
Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 93–100
DOI: 10.1515/pjfns-2015-0018
http://journal.pan.olsztyn.pl
Original article
Section: Food Chemistry
Multi-Element Composition of Honey as a Suitable Tool for Its Authenticity Analysis
Mircea Oroian*, Sonia Amariei, Ana Leahu, Gheorghe Gutt
Faculty of Food Engineering, Stefan cel Mare University of Suceava, University Street, no. 13, Suceava, Romania
Key words: honey, elements, ICP-MS, PCA, SDA
The aim of this study was to evaluate the composition of 36 honey samples of 4 different botanical origins (acacia, sun flower, tilia and honeydew)
from the North East region of Romania. An inductively coupled plasma-mass spectrometry (ICP-MS) method was used to determine 27 elements
in honey (Ag, Al, As, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, U, V and Zn). We would like to achieve the following goal: to demonstrate that the qualitative and quantitative multi-element composition determination of honey can be used as a suitable tool
to classify honey according to its botanical origin. The principal component analysis allowed the reduction of the 27 variables to 2 principal components which explained 74% of the total variance. The dominant elements which were strongly associated with the principal component were K, Mg
and Ca. Discriminant models obtained for each kind of botanical honey confirmed that the differentiation of honeys according to their botanical origin
was mainly based on multi-element composition. A correct classification of all samples was achieved with the exception of 11.1% of honeydew honeys.
INTRODUCTION
Honey, according to the Council Directive 2001/110/EC,
is the natural sweet substance produced by Apismelifera bees
from the nectar of plants or from secretions of living parts
of plants or excretions of plant-sucking insects on the living
parts of plants, which the bees collect, transform by combining with specific substances of their own, deposit, dehydrate,
store and leave in honeycombs to ripen and mature.
Romania has an ancient tradition of beekeeping. The honey production of Romania, according to the National Institute of Statistics, is about 18,000 tons/year, 85% of the production being exported. The most common unifloral honeys
produced in Romania are acacia (Robiniapsedudoacacia), tilia
(Tiliaeuropea), sunflower (Helianthus annuus) and honeydew.
The North-East region of Romania produces around 2,700
tons honey/year.
Honey authenticity is an important issue for honey consumers; therefore it should comply with its declared botanical
and geographical origin. Unifloral honeys have always higher
commercial value than the polyfloral ones; therefore, finding
reliable chemical markers to ascertain the floral origin of honey is a priority research objective in the apiculture industry.
Melissopalynological analysis, based on the identification
and quantification of the percentage of pollen by microscopic
examination, has traditionally been accepted to authenticate
the botanical origin of honey and therefore, it is considered to
be a reference method [Ohe, 2005]. Physicochemical parameters have also been suggested as complementary informa* Corresponding Author: E-mail: [email protected] (Mircea Oroian)
tion to characterise honey [Anklam, 1998]. Additionally, such
parameters as sugars, amino acids, proteins and flavours are
among markers which are able to characterise various types
of honey in conjunction with a number of techniques [Arvanitoyannis et al., 2005].
Gas chromatography coupled to mass spectrometry
(GC–MS) combines high sensitivity and efficacy required
by the analysis of the very complex mixtures of volatiles present in honey at low concentrations and provides structural
information (mass spectrum) for their qualitative analysis
[Soria et al., 2008]. The aroma profile can be considered to
be a “chemical marker” of monofloral honey due to the fact
that it is directly related to the plant nectar extracted by bees
[Amtmann, 2010; Overton & Manura, 1994]. For this reason,
volatile fraction assessment could be a useful tool to characterise geographical or botanical origins [Castro-Vazquez
et al., 2010; Cuevas-Glory et al., 2007].
Honey authenticity was studied by analysing trace elements presented in honeys. There are many studies that use
the multi-elements to classify honeys. Chudzinska & Baralkiewicz [2011] have used Al, B, Ba, Ca, Cd, Cu, K, Mg,
Mn, Na, Ni, Pb and Zn to classify honeydew, buckwheat
and rape honeys from Poland. They observed that K, Al, Ni
and Cd were the parameters that best predicted the authenticity of honey. Also Pisani et al. [2008] studied the elemental composition (23 elements) of 51 Italian honey samples
using ANOVA and PCA. The results confirmed the highly
significant influence of the botanical origin of honey on their
chemical composition. The element composition of honey
is influenced by: the environment and soil type where the nectar plants grow, and by anthropogenic factors (e.g. pollu-
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
94
tion). In other study, the characterisation of Hatay honeys
was made according to their multi-element composition (Al,
B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Sr
and Zn) by Yucel & Sultanoglu [2013]. The study revealed
that cluster analysis and principal component analysis were
useful tools to differentiate the authenticity of honey samples
using the profile of mineral content, highlighting the relationship between the elements’ distribution and honey type.
Fernandez-Torres et al. [2005] applied the multi-element
analysis to classify honey according to its botanical origin.
They analysed eleven elements (Zn, P, B, Mn, Mg, Cu, Ca,
Ba, Sr, Na and K) and made a classification into four different
botanical origins: eucalyptus, heather, orange and rosemary.
They observed a good prediction of the botanical origins
of honey using the multi-element analysis (greater than 97%).
The Northwest Morocco honeys (multifloral honey, Apiaceae, eucalyptus, citrus, Lythrum and honeydew) have been
classified using the K, Mg, Mn, Cu, Fe and Zn according to
their botanical origin by Terrabet al.[2003]. The classification
of eucalyptus and honeydew honeys using the multi-element
content has been higher than 97%.
All the multi-element classifications of honeys could not
be made without the chemometrics approach. These authors
have used different approaches in honey classification: Principal Component Analysis, Cluster Analysis, Linear Discriminant Analysis and Multilayer Perceptrons [Terrab et al., 2003;
Fernandez-Torres et al., 2005; Yucel & Sultanoglu, 2013].
To the authors’ knowledge no other study related to
the multi-element composition of Romanian honeys has been
reported so far.
The aim of this study was to evaluate, from a qualitative
and quantitative point of view, the multi-element composition
of four honey types from the North-East region of Romania
using an ICP-MS technique to determine simultaneously elements and get the possibility to classify honey samples according to their multi-element composition using chemometric analysis.
MATERIAL AND METHODS
Honey samples
To carry out this study, 36 honey samples of different origins: acacia (9 samples), tilia (9 samples), sunflower (9 samples) and honeydew (9 samples) were purchased from local
beekeepers of North East region of Romania. All the samples
were placed and stored in glass bottles and kept at 4–5ºC
in dark prior to analysis.
Melissopalynological analysis
The pollen analysis was made according to the method
of Louveaux et al. [1970], using a non-acetolytic method. Ten
grams of honey were mixed with about 40 mL of distilled water;
then centrifuged at 4500 rpm (3383×g) for 15 min, the supernatant being carefully removed. The residue was re-dissolved
again and centrifuged for other 15 min. The full sediment was
used to prepare the slide. The pollen spectrum of each honey
sample was determined by a light microscopy (Motic ×40)
by counting at least 800 pollen grains. For all pollen types
the individual occurrence was expressed as percentage.
Honey Authenticity
Electrical conductivity
Electrical conductivity was determined in accordance with
the harmonised methods of the International Honey Commission [Bogdanov, 2002].
Sample preparation
Approximately 1 g of each honey sample was weighed into
PTFE vessels and dissolved in 9 mL 65% HNO3 and 1 mL
30% H2O2. The digestion procedures were carried out in a micro-wave oven (Speed wave MWS-2, Berghof Products + Instrument Gmbh, Germany) according to instrumental parameters and settings reported previously (in a part Apparatus).
Blank solutions were prepared in the same way.
Reagents and solutions
All reagents were of analytical grade. Double deionised
water (18 M cm resistivity) produced by a water purification
system (Thermofisher, Germany) was used in all solutions.
The element standard solutions were prepared by diluting
a stock solution of 1000 mg/L of Ag, Al, As, Ba, Be, Ca, Cd,
Co, Cr, Cs, Cu, Fe, Ga, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se,
Sr, Tl, U, V and Zn. Honey samples were digested with concentrated nitric acid (65% HNO3, Sigma Aldrich, Germany)
and hydrogen peroxide (30% H2O2 pure p.a, Sigma Aldrich,
Germany).
Apparatus
The mineral elements analysis was performed using an
Agilent Technologies 7500 Series (Agilent, USA) system
coupled plasma-mass spectrometer. The ICP-MS parameters
were: nebulizer 0.9 mL/min, RF power 1500 W, carrier gas
0.92 L/min, makeup gas 0.17 L/min, mass range 7–205 uma,
integration time 0.1 s, acquisition 22.76 s. Detector parameters were: discriminator 8 mV, analogue HV 1770 V and pulse
HV 1070 V.
Statistical analysis
Statistical analysis was performed using the version 5.1
of the Statgraphics Plus software system. The data corresponding to each variable were analysed by one-way analysis
of variance (ANOVA). Multiple comparisons were performed
using the least significant difference test (LSD) and Fisher ratio (F), and statistical significance was set at =0.05.
The Principal Component Analysis (PCA) was performed
using Unscrambler X 10.1 (CAMO Process AS, Oslo, Norway), all the multi-elements were weighed and normalised
to perform the cluster analysis. The Principal Components
Analysis (PCA) was applied to describe the relations among
the multi-element composition. The discriminant analysis
was made using SPSS trial version (USA).
RESULTS AND DISCUSSION
Melissopalynological analysis
The pollen content of the three types of honey ranged between 620 and 6598 pollen grains. According to the classification made by Maurizio [1939], the honey samples analysed
can be classified in the 1st (less than 2000 pollen grains per
gram) and 2nd class (between 2000–10,000 pollen grains per
95
M. Oroian et al.
gram). According to the number of pollen grains, it seems
that the acacia honey had the smallest number (the number
of pollen grains per gram ranged between 620 and 5389).
In the case of tilia honey, the number of pollen grains per
gram ranged between 825 and 5231, while in the case of sunflower ranged between 784 and 6598 pollen grains per gram.
The honey samples have been classified, acorrding to the melissopalynological analysis, into four main classes as follows:
acacia (Robinia pseudoacacia), sun flower (Helianthus annuus), tilia (Tilia europea) and honeydew.
The pollen grains found in the acacia honeys were: Robinia pseudoacacia, Brassica napus, Plantago, Prunus, Trifoloium and Rubus. The Brassica napus pollen was the main pollen. The pollen grains of Robinia pseudoacacia were placed
in the 2nd place as frequency; the percentage of this type
of pollen ranged between 7% and 37%.
The following types of pollen grains were found in sunflower honeys: Helianthus annuus, Taraxacum officinale, Trifolium, Fragaria, Tilia, Brassica napus and Robinia pseudoacacia. The major type of pollen was Helianthus annuus, ranging
between 52.5% and 67.2%.
In the case of tilia honey, the following were observed:
Tiliaeuropea, Brassica napus, Helianthus annuus, Galium
and Trifolium pollen grains. The major pollen was Tilia europea (31.2–87.4%).
Honeydew honey is a poor pollen honey type, having
an average concentration of pollen grains of 2241 grains.
The major pollen grains present in honeydew honeys were:
Castanea sativa and Quercus, followed by Brassica napus, Helianthus annuus and Trifolium repens.
The electrical conductivity of acacia, sunflower and tilia
honeys ranged between 0.122–0.198, 0.420–0.520 and respectively 0.608–0.730 mS/cm. Honeydew electrical conductivity
ranged between 0.92 and1.26 mS/cm. A higher value than
0.80 mS/cm is not an acceptable one for floral honeys, being specific to honeydew honeys, therefore this parameter
can be used as a quality parameter to distinguish honeydew
and floral honeys [Bogdanov et al., 2004]. The electrical conductivity values for each honey type are in agreement with
those presented in the literature [Kadar et al., 2010; Oroian,
2012; Escriche et al., 2009].
METHOD OF VALIDATION
The 27 elements were simultaneously determined using ICP-MS after acid mineralization. The capability
of the method as a routine analysis method was estimated
through the determination of the detection limits of each
element studied. The limits of detection (LOD) and limits
of quantification (LOQ), were calculated with three and ten
timed the standard deviation of the blank divided by the slope
of the analytical curve, respectively [Thompson et al., 2002;
Khan et al., 2014]. The values of LOD were in the range
of 0.251–18.321 μg/kg as it is presented in Table 1. The LOQs
ranged between 0.761 and 385.513 μg/kg. Precision is described as the degree of variability given by the expression
of results, not taking into account the influence of the sample
(sample variability). The precision was evaluated as the relative standard deviation of 10 repeated determinations for one
TABLE. 1. Limit of detection (LOD), limit of quantification (LOQ), precision, recovery for the 27 elements analysed using ICP-MS.
Analyte
LOD
(μg/L)
LOQ
(μg/L)
Precision
(CV %)
Recovery
(%)
Ag
19.512
59.121
1.29
99
Al
3.812
11.55
3.21
97
As
0.751
2.276
2.75
98
Ba
0.915
2.772
1.21
94
Be
0.351
1.064
2.54
96
Ca
3.156
9.563
4.87
96
Cd
62.624
189.751
1.95
104
Co
86.254
261.35
4.09
103
Cr
0.592
1.794
2.93
97
Cs
0.51
1.545
1.29
105
Cu
0.346
1.048
4.21
98
Fe
0.829
2.512
4.87
99
Ga
0.325
0.985
2.41
99
In
36.214
109.728
2.21
105
K
118.321
358.513
4.89
101
Li
0.271
0.821
4.21
98
Mg
1.212
3.672
4.05
99
Mn
0.456
1.382
4.21
99
Na
115.125
348.829
3.89
98
Ni
0.261
0.791
3.26
95
Pb
1.598
4.842
1.35
103
Rb
0.251
0.761
2.65
97
Se
1.61
4.878
1.92
104
Sr
87.916
266.385
2.98
101
Tl
5.104
15.465
1.89
102
U
0.924
2.8
1.98
102
V
0.271
0.821
1.51
99
Zn
22.659
68.657
2.98
98
sample [Chudzinska & Baralkiewicz, 2011]. Table 1 shows
the coefficient of variation for each element. The coefficient
of variation for the 27 elements analysed ranged between 1.21
and 4.89%, complying with the required criteria of 5%.
Analytical quality control was also verified by the recovery
experiments for the 27 selected elements, spiking at two selected concentration levels, 10 and 100 mg/kg. The recoveries,
depicted in Table 1, were in the range of 94–105%.
Multi-element content in honey samples
Table 2 shows the elemental composition of the honey
samples analysed. The values of elements were not homogeneous. The highest total element content was observed
96
Honey Authenticity
TABLE. 2. Elemental composition of acacia, honeydew, sun flower
and tilia honeys.
Honey type
Acacia
Honeydew
Sun flower
Tilia
Element
(mg/kg)
Ag
0.037a
0.017b
0.015b
0.019ab
2.10ns
Al
11.045b
27.038a
13.561b
11.155b
9.82***
As
0.009a
0.007ab
0.005bc
0.003c
6.26**
Ba
0.228
0.506
a
0.349
0.174
4.73*
Be
0.001
0.002
a
0.001
0.001
16.42***
Ca
52.914c
101.518b
163.878a
137.854ab
13.49***
Cd
0.001b
0.004a
0.003ab
0.001b
3.62*
Co
0.008
0.017
a
0.010
0.009
3.63*
Cr
0.051
0.049
a
0.037
0.029
7.38***
Cs
0.003b
0.013a
0.007b
0.004b
12.21***
Cu
1.822b
3.354a
2.390b
1.563b
7.36***
Fe
b
19.387
28.285
24.009
19.156
2.76ns
Ga
0.015bc
0.030a
0.021b
0.012c
7.03***
K
553.867b
1648.16a
849.36b
955.289a
23.85***
Li
11.157b
19.693a
13.677b
12.055b
4.07*
Mg
51.212
75.415
63.772
50.549
3.64*
Mn
1.715
2.529
1.001
0.868
2.04ns
Na
171.149ab
229.333a
154.068b
123.754b
4.64**
Ni
0.191b
0.325a
0.183b
0.122b
9.48***
Pb
0.062
0.078
a
0.040
0.026
c
8.23***
Rb
0.442
2.246
a
1.097
bc
11.78***
Se
0.009c
0.013a
0.014a
0.011b
14.47***
Sr
0.264b
0.414a
0.351ab
0.304ab
2.58ns
Tl
0.001b
0.003a
0.002b
0.002ab
4.19*
U
0.002
0.002
0.001
0.001
b
3.77*
V
0.006b
0.023b
0.798a
0.004b
3.32*
Zn
2.421a
3.871a
3.241a
2.655a
1.20ns
b
b
b
a
b
ab
ab
c
b
a
a
a
a
F-value
ab
b
b
b
ab
ab
ab
bc
b
b
b
b
b
b
b
b
b
0.895
ns – not significant (P>0.05), * P<0.05, ** P<0.01, *** P<0.001; a, b,
c,d – statistical groups.
in the case of the honeydew sample (2805.08 mg/kg)
and the lowest one was observed in the case of the sunflower sample (663.65 mg/kg). The high content of total
element in the case of honeydew is mainly due to the presence of potassium in high concentration (2108.21 mg/kg);
the same observation was made by others scientists [Lachman et al., 2007; Chua et al., 2012]. Golob et al. [2005]
and Vanhanen et al. [2011] observed higher total element
contents in the case of honeydew honeys from New Zealand
(4060 mg/kg) and Slovenia (3680 mg/kg), respectively.
The first group of elements had higher concentrations than
30 mg/kg, such as: K, Na, Ca and Mg. The major concentration was observed in the case of potassium, which ranged
between 380.91 and 2108.21 mg/kg. The potassium content
covered the elemental composition between 56.16 and 80.56%
and was in agreement with the previous studies [Pisani
et al., 2008; Terrab et al., 2003; Chua et al., 2012; Vanhanen
et al., 2011]. The potassium content ranged between 57.39
and 68.64% in the case of sunflower with a medium concentration of 64.82%, between 70.84 and 75.61% in the case of tilia honey with a medium concentration of 72.59%, between
56.16 and 67.71% in the case of acacia honey with a medium
concentration of 63.07%, and between 72.45 and 80.56%
in the case of honeydew honey with a medium concentration
of 77.06%, respectively. The potassium content decreased as
follows: honeydew honey (77.06%) >tilia honey (72.59 %) >
sunflower honey (64.82 %) > acacia honey (63.06%).
Sodium and calcium were the second and the third predominant minerals in honey samples with a total content
ranging between 7.23 to 25.66% and 2.98 to 15.32%, respectively. The next element was Mg with a total content ranging
between 2.88% and 9.40%, followed by iron which ranged between 0.95% and 4.57%. The content of Ca was in agreement
with the data reported by Lachman et al. [2007].
The second group of elements included Li, Al, Mn, Fe,
Cu and Zn, all of them having higher concentrations than
1 mg/kg and lower than 30 mg/kg. Honeydew samples had
the highest concentration of elements from the second group.
Lithium effects include leukocytosis, polyuria, dry mouth,
confusion, nausea, vomiting, muscle twitch, however it is recommended in bipolar disorder treatment. Aluminium is an
unwanted element for humans, due to its neurological, lungs,
fertility and cancer effects.
The copper content was three times higher in honeydew
than in the other three honey types, as it was observed by Chua
et al. [2012] and Chudzinska & Baralkiewicz [2011], ranging
between 0.644 and 5.491 mg/kg. Still trace amount of copper
is essential for the formation of haemoglobin, namely oxygen
carrying blood component. Furthermore, it helps in the production of melanin which is responsible for pigmentation
of eyes, hair and skin.
Out of a total of 27 elements, 16 elements were trace elements: Be, V, Cr, Co, Ni, Ga, As, Se, Rb, Sr, Ag, Cd, Cs,
Ba, Tl, Pb and U, having lower concentrations than 1 mg/kg
in honeys; they belonged to the third group of elements. Selenium was found in all the four honey types, it is a micronutrient which is very important in proper functioning of the immune system, especially thyroid function in humans.
Honeydew samples were richer than the other samples not
only in the case of elements from the 1st and 2nd groups. It can
be observed that Al content was much higher than 2, Mn was
much higher than 1.5, Fe much higher than 1.2, Ni much
higher than 1.6, Cu much higher than 1.4, Zn much higher
than 1.2, Rb much higher than 2, Cs much higher than 1.6,
Ba much higher than 1.4 and Pb much higher than 1.2 times
in the case of honeydew samples than in the case of acacia,
sunflower and tilia honeys, respectively.
Heavy metals (Cr, Zn, As, Cd and Pb) in the composition
of the honeys under study were registered as well. Cr con-
97
M. Oroian et al.
tent ranged between 0.013 and 0.074 pm, Zn content ranged
between 0.741 and 8.011 mg/kg, As content from 0.002 to
0.015 mg/kg, Cd content from 0.001 to 0.011 mg/kg and
Pb content from 0.020 to 0.142 mg/kg, respectively. Contents
of heavy metals were in the same range with those reported
by Chua et al. [2012] in the case of honey samples from Malaysia. Lead and arsenic are the most sever environment contaminants. Mostly, these contaminant elements come from
industrial activities or automobile exhaust gas emission. Contact with stainless steel surfaces during harvesting, processing
and/or preparation of honey for the market, can generate high
Cr content, due to corrosive effect of honey acidity [Przybylowski & Wilczynska, 2001].
The analysis of variance was applied to all the elements
found in the honey samples (Table 2). In the case of five elements (Mn, Fe, Zn, Sr and Ag), no statistically significant
difference was found among honey samples (P>0.05). For
twelve elements (Be, Al, K, Ca, Cr, Ni, Cu, Ga, Se, Rb, Cs
and Pb), there has been noticed a highly statistically significant difference between honey samples (P<0.001). Considering the Fisher ratio, K content is the most influential element
depending on honey type (F=23.85).
Chemometric analysis
The chemometric analysis is commonly used in science
today, so variance analysis (ANOVA), principal component
Rotated Scores
PC-2 (17%)
0.1
0
- 0.1
- 0.2
- 0.1
0
PC-1 (57%)
0.1
0.2
FIGURE. 1. Principal component analysis of the multi-elements scores of acacia, sunflower, tilia and honeydew honeys.
Correlation Loadings (X)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
PC-2 (17%)
0.2
0.1
0
- 0.1
- 0.2
- 0.3
- 0.4
- 0.5
- 0.6
- 0.7
- 0.8
- 0.9
-1
-1
- 0.9
- 0.8
- 0.7
- 0.6
- 0.5
- 0.4
- 0.3
- 0.2
- 0.1
0
0.1
PC-1 (57%)
0.2
0.3
0.4
0.5
0.6
0.7
FIGURE 2. Principal component analysis of the multi-element composition of acacia, sunflower, tilia and honeydew honeys.
0.8
0.9
1
98
Honey Authenticity
15
10
Function 2
5
- 30
- 20
- 10
0
10
20
30
-5
- 10
- 15
- 20
Function 1
FIGURE 3. Linear discriminant score plot: rhombus– acacia honeys, triangle – tilia honeys, square – sunflower honeys, cross – honeydew samples.
TABLE. 3. Results obtained from the discriminant analysis applied to elemental composition in order to differentiate acacia, tilia, sunflower and honeydew honeys.
Count
Original
Cross
validated
%
%
Predicted group membership
Acacia
Sunflower
Tilia
Honeydew
Total
(%)
Acacia
100
–
–
–
100
Sunflower
–
100
–
–
100
Tilia
–
–
100
–
100
Honeydew
–
–
–
100
100
Acacia
100
–
–
–
100
Sunflower
–
100
–
–
100
Tilia
–
–
100
–
100
Honeydew
–
–
11.1
88.9
100
Type
analysis (PCA) and stepwise discriminant analysis (SDA)
were used to check the similarities between samples according to botanical origin.
Principal component analysis
The principal component analysis was conducted to
evaluate the global effect of elemental composition on honey type, from a descriptive point of view. Figures 1 and 2
present the scores and compound loadings of PCA analysis performed. It was found that the two principal components (PCs) explained 74% of the variations in the data
set. The PC1 explained 57% of the variability and the PC2
explained 17%. It can be observed that the honey samples
are divided into 4 groups by the two principal components.
Magnesium influences the projection of acacia honeys;
potassium influences the projection of honeydew honeys,
while calcium influences the projection of sunflower honeys. The elements placed in the outer ellipse of the correlation loadings have a higher influence on the projection than
those placed in the inner ellipse.
Stepwise discriminant analysis
A stepwise discriminant analysis was applied, out of which
six classification models were constructed. All the elemental
components analysed were used for this purpose, and the discriminant functions were constructed using all the variables
(Table 3). In order to evaluate the model classification capacity, the percentage of samples classified correctly was considered: original grouped (using all samples to estimate the classification model) and cross-validated grouped (leaving one
out) to estimate its robustness. This procedure calculates
the model with all samples minus one, after which the prediction is performed. This data processing was repeated as many
times as the number of samples was. In this way, it was possible to evaluate the capacity of predicting correctly the group
that unknown samples belong to. In all the cases, the same
classification of groups was observed. Irrespective of the parameters chosen the percentage of cases correctly classified
were 100% in the case of the original classification while
in the case of the cross-validation classification the samples
were 97.2% correctly classified (Table 3). Acacia, sunflower
M. Oroian et al.
and honeydew samples were correctly classified, while a honeydew sample was classified as tilia. This fact can be due to
the low content of potassium found in that honeydew sample.
The linear discriminant analysis applied to all the physicochemical parameters resulted into two canonical functions
with the Eigen values of 237.126 and 125.682 and the Wilks’s
lambda values of 0.001 and 0.003, respectively. The linear discriminant analysis is shown in Figure 3. Function 1 explains
65.1%, while function 2 explains 34.44%of the total variance.
The bi-dimensional plot (Figure 3) of the first two functions
shows four groups for the four honey types. The SDA allows
visualisation of data in botanical origin representations, simplifying the observation and interpretation of information.
The highest absolute value which dominated the first discriminant function is represented by Be content (F1=16.91,
F2=7.57), followed closely by the Ca content (F1=14.74,
F2=4.73). These two parameters dominated and the second
discriminant function did, too. The V content (F1=-0.36,
F2=- 2.24) had the lowest influence on the first discriminant
function, while Fe content (F1=4.41, F2=0.88) had the lowest influence on the second discriminant function.
CONCLUSIONS
The multi-element composition of honey provided us
with useful information on the differentiation of acacia,
honeydew, sunflower and tilia. Therefore, the honey type
has a great influence on the multi-element composition. Potassium is the element with the highest concentration in all
the honeys irrespective of their botanical origin. The multivariate analysis allowed the discrimination of honey types
according to their botanical origin using the multi-element
composition. The cross validation of honey samples was correct for the 97.2% of the honey samples (11.1% of the honeydew samples were classified as tilia honeys). Having in view
the chemometric approach, we can consider that the multielement composition of honeys is a suitable tool in predicting
their botanical origin.
ACKNOWLEDGEMENTS
The study was financed from resources of the Stefan cel
Mare University of Suceava.
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2015.
Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 101–108
DOI: 10.1515/pjfns-2015-0022
http://journal.pan.olsztyn.pl
Original article
Section: Food Quality and Functionality
Physico-Chemical, Enzymatic, Mineral and Colour Characterization of Three Different
Varieties of Honeys from Kashmir Valley of India with a Multivariate Approach
Gulzar Ahmad Nayik*, Vikas Nanda
Department of Food Engineering and Technology, Sant Longowal Institute
of Engineering and Technology, Longowal 148106 (Punjab) India
Key words: Kashmir, honey, acacia, enzymatic, pine honeydew, multifloral
The present study was undertaken to determine the physico-chemical properties (moisture content, reducing sugars, proline content, electrical conductivity, ash content, pH, titrable acidity, HMF, water activity, total soluble solids and total solids), enzymatic activity (diastase and invertase), colour
characteristics (mmpfund, ABS450 and CIE L*a* b*) and mineral content (Cu, Mn, Fe, Zn, Pb and Cd) of three different varieties of honeys from Kashmir valley of India (acacia honey, pine honeydew and multifloral honey). Of the honey samples analysed, only pine honeydew were grouped in dark
category of honey (L*<50) while acacia honey and multifloral honey were confirmed as light coloured honeys (L*>50) and possessed both red and yellow components. The concentrations of mineral content were found highest in pine honeydew followed by multi floral and least in acacia honey. All
the physico-chemical properties and enzymatic activity indicated that all the three analysed varieties of honey met the criteria set by the International
Honey Commission and revised codex standards for honey. The source of honey had a significant effect (p<0.05) on physico-chemical characteristics,
enzymatic activity, mineral content and colour properties. Strong and positive correlations exhibited among minerals, colour (mmpfund) and L* value
indicated that dark coloured honey contained high mineral content. Multivariate analysis proved to be an effective tool in classifying the three varieties
of honey based on physico-chemical characteristics, enzymatic activity, mineral content and colour properties.
INTRODUCTION
Honey, the wonderfully rich golden liquid is the miraculous product of honey bees and a naturally delicious alternative to sugar. Chemically, honey is mostly dominated by sugars in the form of fructose and glucose (70–80%), water
(10–20%) and other minor constituents such as organic acids
(gluconic acid, acetic acid), mineral salts (potassium, calcium,
sodium, phosphorus etc.), vitamins (ascorbic acid, niacin),
proteins, enzymes (invertase, glucose oxidase, catalase, phosphatases), volatile chemicals, phenolic acids and flavonoids
[Bouseta et al., 1996; Terrab et al., 2001; Gheldof et al.,2002;
Blasa et al., 2006; Ouchemoukh et al., 2007; Nayik & Nanda,
2015]. The composition and physico-chemical properties
of honey are wholly dependent on the plant species visited
by the honeybees as well as the processing, storage, regional,
and climatic conditions [Saxena et al., 2010]. The colour,
which serves as an indicator of floral origin ranges from light
to dark often reddish or yellowish [Soria et al., 2004; Terrab
et al., 2004]. It is well known fact that there is a strong correlation among colour, antioxidant activity, electrical conductivity
and ash content [Marghitas et al., 2009]. Generally dark coloured honey has higher phenolic content and consequently
* Corresponding Author: Cell: +91–9478153553; Fax: 01672–280057;
E-mail: [email protected] (Gulzar Ahmad Nayik)
higher antioxidant activity as compared to honey with light
colour [Berreta et al., 2005].
Multivariate analysis viz. principal component analysis
(PCA) and linear discriminate analysis (LDA) have been extensively used to classify honey based on physico-chemical
data, mineral analysis and colour characteristics. Conti et al.
[2007] classified three Italian honey samples (acacia, multifloral, honeydew) on the basis of their physico-chemical data
and mineral content by applying multivariate statistical analysis. Chudzinska & Baralkiewicz, [2010] analysed fifty-five
Polish honey samples to rationalize and interrupt the analytical data for the determination of thirteen metallic and nonmetallic elements by using PCA tool. Similar study was reported
by Devillers et al. [2002] who examined French honeys.
Rearing of honey bees for production of honey (apiculture) is one of the prevalent agricultural activities done in India. As per the data published by Press Information Bureau
[2012–2013], there are more than 1600 honey producing
units in Jammu and Kashmir where honey production is near
about 2000 metric tons. The physicochemical characterisation of different honeys from different parts of the world has
been carried out extensively [Azeredo et al., 2003; Finola et al.,
2007; Ouchemoukh et al., 2007]. Honeys produced in India
from various unifloral sources as well as from commercial
honeys were already studied by various authors [Ahmed et al.,
2007; Nanda et al., 2009; Saxena et al., 2010]. After successful
studies of characterisation of quality parameters of Eucalyp-
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
102
tus, Brassica, Helianthus, L. chinensis, Z. mauritiana, C. sinensis and P. persica [Nanda et al., 2003, 2006, 2009], the present
study was undertaken, not only to study the physico-chemical
parameters, mineral content, enzymatic analysis and colour
characteristics from three different varieties from Kashmir
valley of India viz. acacia honey, pine honeydew and multifloral honey but also to perform pattern recognition methods
by using PCA and LDA to classify honey varieties which has
never been performed till now.
MATERIALS AND METHODS
Honey sample
The present study was carried out using 30 samples
(10 samples from each variety) of three different raw and fresh
honey samples (acacia honey, pine honeydew and multifloral
honey) collected from bee keepers during September 2012
to May 2014 from different regions (Pulwama, Srinagar,
and Budgam) packed and sealed in glass bottles of Kashmir
valley of India and stored at 4oC. The honey samples were kept
at ambient temperature 25±1oC overnight before the analyses were performed. The origins of each honey sample were
confirmed by microscopic pollen analysis (Melissopalynology). Honey samples were classified according to their botanical origin using the method described by Louveaux et al.
[1978]. The following terms were used for frequency classes:
predominant pollen (>45% of pollen grains counted), secondary pollen (16–45%), important minor pollen (3–15%)
and minor pollen (<3%).
Physico-chemical analysis
The samples of honey were analysed according to
the methods established by AOAC [2012] and International
Honey Commission [2009] for moisture content, ash content, electrical conductivity, diastase and invertase activity,
reducing sugars, proline content, titrable acidity, pH, total
solids and total soluble solids, hydroxymethylfurfural content
and water activity. The colour characteristics of the honeys
were assessed according to the CIE L*a*b* method described
by Saxena et al. [2010]. Colour (mmpfund) was determined
by the method described by White [1984] while colour intensity (ABS450) was done by the method adopted by Beretta et al.
[2005]. Mineral elements Cu, Mn, Fe, Zn, Pb and Cd were
determined by using air acetylene flame atomic absorption
spectrometer (AAS4141). The response from the equipment
was periodically checked with known standards. Hollow cathode pump and air-acetylene flame were used for all the samples from three varieties. Calibration curves were constructed
for each element by using appropriated standard solutions.
Statistical analysis
The results were expressed in triplicates, mean, standard
deviation and correlation was obtained by using Microsoft Excel 2007. The significant differences were obtained by a one-way analysis of variance (ANOVA) followed by Duncan’s
multiple range test (DMRT) (p<0.05).
Principal component analysis and linear discriminate
analysis were performed to classify honey samples from different botanical sources by using XLSTAT.
Quality Characterization of Honey from Kashmir
RESULTS AND DISCUSSION
The pollen spectra of honey samples studied have been
briefly described and the percentages are related to pollen
of nectar producing plants. Acacia honey contained 54–60%
pollen of R. pseudoacacia sp. The honeydew element/pollen
grain (HDE/P) ratio was in a range of 2.79–3.01 in pine honeydew (Pinus wallichiana), which was in good agreement with
Louveaux et al. [1978]. The microscopic analysis revealed
some fungal spores in pine honeydew which is in good agreement with those found in Greek pine honeys [Karabagias et al.,
2014]. Multifloral honey contained 2–5% pollens of Plectranthus rugosus, other pollens found were those from Prunus sp.,
Brassica sp., Thyme sp. and Ailanthus sp. The mean (±S.D)
results obtained from the physico-chemical, enzymatic
and colour characteristics of honey samples are presented
in Table 1 while mineral content is shown in Table 2. Moisture
content was reported as an important parameter and honey
with lower moisture content showed longer shelf life [Fredes
& Montenegro, 2006]. In this study all the samples from three
varieties showed the level of moisture content (18.2 to 19.11)
which was lower than the limit prescribed by Codex standard for honey [Codex Almentarius, 2001] and International
Honey Commission [2009] and the results were significantly
different (p<0.05) for each honey obtained from different
sources. Our results were in agreement with previous studies
[Yilmaz & Kufrevioglu, 2000; Pan & Ji, 1998; Duman et al.,
2008]. The moisture content of honey was affected by various
factors such as harvesting time, climatic factors and maturity period [Nanda et al., 2003; Finola et al., 2007]. The oBrix
values ranged from 79.16 to 80.03 while the refractive index
of the samples ranged from 1.4889 to 1.4908. With the increase in the solid content, there was increase in refractive
index. The total solid content of the samples was in a range
of 80.89% to 81.8% which were quite similar to the results
(77.8–80.4%) reported by Saxena et al. [2010].
The pH values of all the honey varieties ranged from
3.52 to 3.78 which confirmed that all the honey varieties were acidic in nature. The pH values were consistent
with the results reported by various authors [Azeredo et al.,
2003; Ouchemoukh et al., 2007; Saxena et al., 2010; Kamboj
et al., 2013]. Results indicated maximum acidity in the form
of formic acid in pine honeydew variety (0.39%) followed
by acacia honey and multifloral floral varieties in decreasing order with 0.17% and 0.14%, respectively. The titrable
acidity and pH of all the samples analysed was found within
the corresponding limits as described by Codex Almentarious
Commission [2001]. A high strong positive correlation was
found between pH and titrable acidity (r=0.99).The percentage ash content is an indicator of the mineral content present in a given sample. The maximum ash content was found
in pine honeydew (0.39%) followed by acacia honey (0.06%)
whereas ash content on multifloral floral honey was at
0.05%. Similar results were reported by Kamboj et al. [2013]
(0.140–0.211%) and Nanda et al. [2009] (0.13 to 0.35%). As
per previous studies, electrical conductivity (EC) was used to
distinguish honeydew honeys from blossom honey [Mateo
& Bosch-Reig, 1998]. The electrical conductivity of all analysed honey samples ranged from 0.25 to 0.79 mS/cm. As per
103
G.A. Nayik & V. Nanda
TABLE 1. Physico-chemical parameters of honey samples with Duncan’s multiple range test results.
Parameter
Acacia honey (n=10)
Pine honeydew (n=10)
Multifloral honey (n=10)
Ash (%)
0.06±0.03
a
0.35±0.02
0.05±0.01b
Electrical Conductivity (mS/cm)
0.26±0.05b
0.79±0.06a
0.25±0.04b
Titrable acidity (%)
0.17±0.02a
0.39±0.02b
0.14±0.02a
pH
3.55±0.0.0ab
3.78±0.11a
3.52±0.02b
Moisture (%)
18.6±0.08b
18.2±0.12b
19.11±0.20a
Total soluble solids (°B)
79.7±0.10a
80.03±0.15a
79.16±0.25b
Total solids (%)
81.40±0.08a
81.8±0.12a
80.89±0.55a
HMF(mg/kg)
5.49±0.07b
6.79±0.10b
22.64±0.30a
Water activity
0.507±0.00c
0.523±0.00b
0.566±0.00a
ABS450 (mAU)
188.05±2.30b
525.39±2.98a
148.31±3.07c
Color (mmpfund)
43.32±1.36b
143.04±3.10a
39.16±1.45b
L*
61.43±1.60a
15.49±0.3c
54.64±0.81b
a*
4.94±0.08a
2.25±0.08c
3.49±0.29b
b*
12.32±0.07a
5.28±0.10c
9.39±0.30b
Diastase Number (DN)
15.51±1.40b
25.99±2.79a
14.93±2.10b
Invertase Number (IN)
9.40±1.27b
15.83±2.82a
12.58±1.94b
Proline (mg/kg)
292.02±2.65b
570.95±2.15a
168.05±3.66b
Reducing Sugars (%)
66.24±3.05b
60.6±3.94a
72.81±2.97c
b
Results are expressed as mean values±standard deviations. Means in a row with same superscripts are not significantly different (P<0.05), n=number
of samples.
Codex Alimentarius [2001] electrical conductivity value for
the nectar honey must be less than 0.80 mS/cm while according to the EU, minimum electrical conductivity for pine honey
should be near 0.80 mS/cm. The results of electrical conductivity were consistent with reported results of Escuredo et al.
[2012] in blossom and honeydew honeys. Positive and strong
correlation (r=0.90) between ash content and electrical conductivity (Table 3) indicated that higher ash content resulted
in higher electrical conductivity.
Tosi et al. [2002] reported that hydroxymethylfurfural
(HMF) as a quality parameter to check the honey freshness
and high temperature processing. All the three honey varieties showed an HMF level lower than the limit (40 mg/kg),
recommended by the Codex Alimentarius [Codex Alimentarius, 2001] and statistical analysis revealed a significant difference (p<0.05) among the varieties. The maximum content
of HMF was found in multifloral source (22.64 mg/kg) followed by pine honeydew (6.79 mg/kg) and minimum in acacia honey (5.49 mg/kg) as shown in Table 1. These results
suggested that all the samples from three varieties of honey
were raw and unprocessed. The HMF values of the analysed
samples were consistent with the values reported by Yilmaz
& Kufrevioglu [2000] and Duman et al. [2008]. The water
activity is an important factor for stability of food by preventing microbial growth. The water activity of the honey samples
was in the range of 0.507 to 0.566 and these values were statistically different from each other (p<0.05) (Table 1). Our
results were quite similar to those of Greek honeys and Indian
honeys [Lazaridou et al., 2004; Saxena et al., 2010].
The acceptability of honey by the consumer and its market price mainly depends on colour of honey. According to
the classification of honey based on Pfund mm values set
by USDA [1985] colour of honey ranges from water white
(<8 mm) to dark amber (>114 mm). Based on this classification, the pine honeydew variety (143.04 mm) was classified as dark amber while multifloral honey (39.16 mm)
and acacia honey (43.32 mm) as extra light amber. It was
reported that colour of honey was affected by chemical
composition, primarily due to the presence of pigments such
as chlorophylls, carotenoids, flavonoids and polyphenols
[Lazaridou et al., 2004; Finola et al., 2007; Juszczak et al.,
2009]. The honeys with light colour usually contained low
ash content and low antioxidant activity while those with
dark colour showed reverse of these values [Gheldof et al.,
2002; Marghitas et al., 2009]. There was high positive correlation found between colour and ash content (r=0.98)
and colour values of all samples from different sources were
significantly different (p<0.05). Colour intensity (ABS450)
is an index for confirming the presence of pigments such as
carotenoids and some flavonoids. ABS450 values of the tested samples ranged from 148.31 to 525.39 mAU and were
statistically different (P<0.05) (Table 1). All obtained values
were in the range as reported by Beretta et al. [2005] in different commercial honey varieties of Italy.
104
Quality Characterization of Honey from Kashmir
TABLE 2. Statistical analysis of mineral content (mg/kg) of honey from different sources.
Sources
Cu
Mn
Fe
Pine honeydew
0.12±0.01
1.08±0.01
Acacia honey
0.12±0.01b
Multifloral honey
0.38±0.02a
b
Zn
Pb
Cd
2.11±0.01
1.04±0.02
0.21±0.02
0.19±0.02a
0.95±0.01b
1.42±0.03b
0.06±0.03b
0.04±0.04b
0.07±0.02b
1.01±0.02a
1.26±0.03b
0.07±0.03b
0.09±0.02b
0.08±0.03b
a
a
a
a
Results are expressed as mean values±standard deviations. Means in a column with same superscripts are not significantly different (P<0.05).
TABLE 3. Correlation among some physicochemical parameters and minerals (Pearson correlation coefficients, p<0.05).
pH
Ash
Color
L*
a*
b*
Cu
Mn
Fe
Zn
Pb
Cd
pH
1.00
–
–
–
–
–
–
–
–
–
–
–
–
Ash
0.99
1.00
–
–
–
–
–
–
–
–
–
–
Color
0.66
0.90
1.00
–
–
–
–
–
–
–
–
–
–
L*
-0.65
-0.89
-0.98
1.00
–
–
–
–
–
–
–
–
a
a
EC
-0.51
-0.76
-0.81
0.87
1.00
–
–
–
–
–
–
–
–
*
-0.54
-0.81
-0.87
0.92
0.95
1.00
–
–
–
–
–
–
–
Cu
0.66
0.78
0.51
-0.80
-0.04
0.08
1.00
–
–
–
–
–
–
Mn
0.59
0.77
0.77
-0.82
-0.89
-0.84
-0.08
1.00
–
–
–
–
–
Fe
0.64
0.90
0.99
-0.95
-0.72
-0.81
-0.62
0.69
1.00
–
–
–
–
Zn
0.64
0.91
1.00
-0.99
-0.83
-0.90
-0.47
0.80
0.98
1.00
–
–
–
Pb
0.64
0.74
0.89
-0.91
-0.85
-0.90
-0.25
0.70
0.84
0.89
1.00
–
–
Cd
0.66
0.70
0.87
-0.87
-0.75
-0.82
-0.32
0.58
0.85
0.86
0.92
1.00
–
EC
0.66
0.90
0.98
-0.97
-0.81
-0.88
0.88
0.79
0.96
0.98
0.88
0.85
1.00
*
b
a
EC= Electrical Conductivity
a
The L*, a*, b* values obtained for three different varieties of honeys are shown in Table 1. According to L* values,
Gonzalez-Miret et al. [2005] classified honey into two groups:
light honey with L*>50 and the dark honey with L*<50. On
the basis of this classification, the pine honeydew was placed
in group of dark category while acacia and multifloral honey
in light category. It is evident from the Table 1 that acacia
honey (61.43) and multifloral floral (54.64) honeys showed
higher L* values than pine honeydew (15.49). It established
that acacia honey and multifloral floral were of light colour
while pine honeydew was of dark colour. In all the honey samples from three varieties of honey, a* values were ranged from
2.25 to 4.94 while b* values from 5.28 to 12.37. These values
were helpful in concluding that all the honey samples possessed both red and yellow components. Similar results were
reported for Slovak honeys [Kasperova et al., 2012].
The diastase activity, indicator of high temperature exposure of the analysed samples ranged from 14.93 DN (multifloral honey) to 25.99 DN (pine honeydew) (Table 1). All
the honey samples showed the values within the Codex Standard i.e. more than 8 DN. Similar results were published
by Meda et al. [2005] for Burkina Fasan honey, and by Saric et al. [2008] for Croatian honeys. The presence of high
diastase number in the analysed samples might be due to
moderate climate of Kashmir valley. According to the Codex
standard, invertase number, indicator of honey freshness,
should be more than 4. The invertase number ranged from
9.40 IN (acacia honey) to 15.83 IN (pine honeydew). Thus
both IN were >4 and DN>8 which determined that all honey
samples were fresh and unprocessed. Our results were consistent with reported results of Hasan et al. [2013] for Iraqi
honeys and Dinkov et al. [2014] for acacia, sunflower and tilia honeys. All the honey samples had a high proline content
ranging from 268.05 to 570.95 mg/kg (Table 1) thus could
be considered as ripened and unadulterated ones. Similar results were published for Algerian honeys, Malaysian honeys
and Indian honeys [Ouchemoukh et al., 2007; Saxena et al.,
2010]. The total reducing sugars value in the analysed samples ranged from 60.6 to 72.81% (Table 1), which is in agreement with the standards proposed by EU Directive 2001/110,
which should be more than 60% for blossom honeys and at
least 45% for honeydew honey. Similar results were reported
by Khalil et al. [2012] for Algerian blossom honeys.
Mineral analysis
The minerals found in the analysed honey samples are
expressed in Table 2. Statistically significant differences
(p<0.05) were observed among all analysed samples. Copper (Cu) content was found highest in multifloral honey
(0.38 mg/kg) and lowest in both acacia honey and pine hon-
105
G.A. Nayik & V. Nanda
eydew (0.12 mg/kg). The analysed honey varieties showed low
concentration of Cu as compared to honey studied by Conti
et al. [2007], Nanda et al. [2009] and Kamboj et al. [2013], but
similar concentration of copper was found in Brazilian honey
[Liberato et al., [2013]. The manganese (Mn) concentration
of all analysed samples ranged from 0.95 mg/kg to 1.08 mg/kg
with highest concentration in pine honeydew (1.08 mg/kg).
Similar findings were reported by Nanda et al. [2009]. Among
the other minerals, iron (Fe) content was found in higher concentration in all samples with highest value in pine honeydew
(2.11 mg/kg) and lowest in multifloral honey (1.26 mg/kg).
The concentrations of zinc (Zn), lead (Pb) and cadmium
(Cd) were found very low (<1mg/kg) except zinc in pine honeydew (1.04 mg/kg) (Table 2). The results for Mn, Fe, Zn, Pb
and Cd were in agreement with those reported by Liberato
et al. [2013] for Brazilian honey of Apis mellifera from different floral origins. The mineral content of the honey depends on geographical area, climatic conditions and soil type
of the floral source. It was reported that soil in Kashmir valley
of India did not contain high amount of minerals like Cu, Fe,
Zn and Co [Bhat et al., 2011; Yatoo et al., 2011] which might
be the reason of low level of mineral content in the studied
samples of honey varieties.
Strong and positive correlations were found between all
minerals and colour (pfund) while strong negative correlations with L* value, which indicated that honey with dark colour contained high amount of ions (Table 3). All minerals
also showed strong positive correlation with electrical conductivity and ash, which demonstrated that higher mineral
content, resulted in higher ash content and electrical conductivity. A positive correlation was also found among all minerals, electrical conductivity and pH (Table 3), which showed
that both pH and conductivity were dependent on the amount
of ions in honey. Similar correlations were observed by Acquarone et al. [2006] between total mineral content and electrical conductivity.
Multivariate analysis
In our study, the PCA was applied to achieve a reduction
of original data matrix while retaining the maximum amount
of variability present in data. The factor loading obtained for
the first three components (PCs) and the percentage of variance along with cumulative variance is shown in Table 4.
The first three principal components accounted for 86.92%
of the variance in the honey samples analysed. The first, second and third principal components (PC1, PC2, and PC3)
explained 65.72%, 16.26% and 4.93% of the variance, respectively (Table 4). According to loading matrix (Table 5),
it was observed that 65.72% variability explained by PC1
was positively correlated with variables viz. colour intensity,
colour (mmpfund), iron, zinc, proline, electrical conductivity and negatively with colour L*, a* and b* values (Figure 1)
which could be specified as indicators of colour and minerals.
Thus such variables could be used to distinguish pine honeydew from acacia honey. Figure 1 also illustrated that all acacia
samples located on the left side of PC1 were linked to L*, a*
and b* values while all pine honeydew samples positioned on
its upright were linked to minerals and electrical conductivity.
The second component (PC2=16.26%) was positively corre-
TABLE 4. Principal component analysis
Total variance explained
PC
Initial Eigen values
% of variance
Cumulative %
1
15.77
65.72
65.72
2
3.90
16.26
81.98
3
1.18
4.93
86.92
TABLE 5. Principal component analysis. Loading of the first three components.
Principal components
Factor loading 1
2
3
pH
0.660
-0.014
0.424
Moisture
-0.657
0.481
-0.163
Total solids
0.657
-0.481
0.163
Color Intensity
0.994
-0.027
-0.048
Total soluble solids
0.333
-0.317
0.700
HMF
-0.479
0.840
0.111
Ash
0.903
0.042
-0.137
Titrable acidity
0.923
-0.082
0.092
Color (mmpfund)
0.993
0.034
-0.043
Water activity
-0.279
0.912
0.192
Color (L*)
-0.978
-0.159
0.025
Color (a*)
-0.746
-0.577
0.026
Color (b )
-0.872
-0.440
-0.008
Cu
-0.524
0.782
0.104
Mn
0.746
0.490
-0.245
Fe
0.985
-0.102
-0.063
Zn
0.990
0.078
-0.075
Pb
0.901
0.258
0.208
Cd
0.880
0.130
0.250
Diastase number
0.934
0.000
-0.080
Invertase number
0.674
0.435
-0.069
Proline
0.994
-0.002
-0.053
Reducing sugar
0.658
-0.307
-0.420
Electrical conductivity
0.978
0.072
-0.036
*
lated with water activity and HMF, which could be interpreted
as an indicator of the maturity of honey. Such variables could
be used to distinguish multifloral honey from acacia honey
and pine honeydew. The third component with less contribution (PC3=4.93%) was positively correlated with TSS and pH
but negatively with reducing sugar. Similar results were reported by Kadar et al. [2010] for colour (mmpfund), CIE L*
a* b* and conductivity, while Saric et al. [2008] published simi-
106
Quality Characterization of Honey from Kashmir
0.0
0.5
4
0.6
0.4
2
PC 2: 16.26%
0.2
0
0.0
- 0.2
-2
- 0.4
- 0.6
-4
0
5
PC 1: 65.72%
FIGURE 1. Projections of the variables on the factor plane for the three honeys (Botanical origins: A: Acacia, PH: Pine Honeydew, MF: Multifloral).
TABLE 6. Classification result of LDA of variables in three types of honeys.
From/to
Acacia honey
Pine honeydew
Multifloral honey
Total
% Correct
Acacia honey
10
0
0
10
100.00
Pine honeydew
0
10
0
10
100.00
Multifloral honey
0
0
10
10
100.00
Total
10
10
10
30
100.00
lar results on Croatian honey varieties from three harvesting
seasons in which he concluded that in all seasons PC1 was
mainly dominated by conductivity, proline and diastase number. Similar results for HMF were obtained by Isopescu et al.
[2014] for Romanian honeys. As shown in Figure 1, a natural
separation between honeys of different botanical origin was
obtained. All the three analysed honey samples from different botanical origins were correctly classified (100%) by using
LDA (Table 6).
CONCLUSION
The analysed three honey varieties from Kashmir valley of India revealed that floral origin significantly affects
all the physico-chemical parameters, enzymatic properties,
mineral content and colour characteristics except total solids.
The concentration of minerals found in all three honey varieties was low as compared to other varieties of honey of Indian origin. Application of multivariate techniques confirmed
the validity of physico-chemical analysis as a tool for classifi-
cation and characterization of honey obtained from different
botanical sources. PCA revealed 86.92% of the variance with
the first three principal components with minerals, colour
and electrical conductivity dominating variables. LDA proved
to be an effective tool which classified the all honey samples
100% correctly.
ACKNOWLEDGEMENTS
The first author is very much thankful UGC New Delhi
India for providing financial assistance in the form of MANF2013–14 and also to local beekeepers of Kashmir valley of India for providing raw honey samples.
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DOI: 10.2478/pjfns-2013-0012
http://journal.pan.olsztyn.pl
Original article
Section: Food Technology
Spray Drying of Honey: The Effect of Drying Agents on Powder Properties
Katarzyna Samborska*, Paulina Gajek, Anna Kamińska-Dwórznicka
Department of Food Engineering and Process Management, Warsaw University of Life Sciences
(WULS-SGGW), Faculty of Food Sciences, Nowoursynowska 159c, 02–776 Warsaw, Poland
Key words: maltodextrin, gum Arabic, hygroscopicity, bulk density, apparent density, wettability
The aim of this study was to investigate the possibility of honey spray drying with addition of maltodextrin and gum Arabic as drying agents.
The influence of the concentration of the solution subjected to drying, the type and content of the drying agents upon the physical properties of obtained powders was examined. An attempt was undertaken to obtain powder with a honey content of more than 50% d.b. Spray drying of multifloral
honey with the addition of maltodextrin and gum Arabic was carried out at inlet air temperature of 180°C, feed rate of 1 mL/s and rotational speed
of a disc atomizer of 39,000 rpm. The properties of obtained powders were quantified in terms of moisture content, bulk density, Hausner ratio, apparent density, hygroscopicity and wettability. Using gum Arabic it was possible to obtain a product with a higher content of honey (67% solids) than
in the case of maltodextrin (50% d.b.). However, the powders obtained with gum Arabic were characterised by worse physical properties: higher hygroscopicity and cohesion, and longer wetting time.
INTRODUCTION
Honey is consumed because of its unique taste and aroma as well as its numerous health-promoting properties.
The treatment involving bee products constitutes a science-based branch of medicine referred to as Apitherapy [Alvarez-Suarez et al., 2010]. However, honey in its natural form has
several disadvantages as a result of high viscosity and density which cause difficulties in transportation and dosage
[Cui et al., 2008; Hebbar et al., 2008]. It can also change its
properties as a result of crystallization, which may contribute to the development of osmophile yeast and fermentation
[Bhandari et al., 1999; Hebbar et al., 2008]. The use of powdered honey significantly reduces these problems. Honey
powder, like powders formed after drying of fruit juices, may
be intended for direct consumption, used as an additive to
a range of food products such as yoghurts, drinks, sauces,
edible coatings, as well as dietary supplements and therapysupporting preparations [Rodriguez-Hernandez et al., 2005;
Gabas et al., 2007; Shrestha et al., 2007; Hebbar et al., 2008].
Production of honey dry powder is, however, difficult mainly
due to the high content of sugars and organic acids [Truong et al., 2005; Rodriguez-Hernandez et al., 2005; Zareifard et al., 2012; Murugesan & Orsat, 2012]. These substances are characterised by low glass transition temperature Tg. At
Tg the form of the components changes from the hard, brittle
“glass” material of a very high viscosity (about 1012 Pa×s)
* Corresponding Author: Tel. +48 22 59 37 569; Fax: +48 22 59 37 576
E-mail: [email protected] (Katarzyna Samborska)
into the soft visco-elastic “rubbery” material with viscosity of about 106–108 Pa×s [Williams et al., 1955; Bhanardi
& Howes, 1999; Shrestha et al., 2007]. The temperature at
which this transformation occurs is characteristic for each
substance and, for substances from the same group, increases
with the molecular weight. In honey, particularly important
is the high content of fructose and glucose because of low Tg
values: 10°C (fructose) and 36ºC (glucose) [Papadakis et al.,
2006; Sahu, 2008]. Juszczak & Fortuna [2006] report the Tg
value of multifloral Polish honey at -50.7ºC (moisture content
17.7 % d.b.). According to other researchers [Lazaridou et al.,
2004], this value is -37.2°C in the case of the Greek multifloral honey (moisture content 14.1 % d.b.), and between -33.6
and -51.1°C in the Indian nectar honey from different plant
species (total solids from 76 to 88% d.b.) [Ahmed et al., 2007].
The form in which the material occurs during spray
drying results from the relationship between product temperature Tp and the temperature of glass transition Tg.
[Noel et al., 1990]. The amorphous material has to appear
in a glassy form in order to obtain a free-flowing powder.
These relationships are shown in Figure 1 [Samborska et al.,
2011]. During spray drying of the material with a high content of sugars, material temperature in the drying chamber
Tp1 is usually higher than the glass transition temperature Tg1.
As a result, the material exists in the visco-elastic rubbery
state, namely in the form of syrup or sticky particles adhering to the walls of the chamber. Drying such a material into
a powder form is not possible [Bhanardi & Howes, 1999;
Adhikari et al., 2003; Hebbar et al., 2008]. To enable the drying of this kind of material it is necessary to modify the pa-
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
110
Honey Spray Drying
GUM
GUM
Tg2
Tp1
GLASS
Tp2
GLASS
Tg1
FIGURE. 1. The influence of product temperature (Tp) and glass transition temperature (Tg) on the state of material during spray drying (descriptions in the text) [Samborska et al., 2011].
rameters of drying or to prepare the material in such a way
that Tg>Tp. At the glass transition temperature Tg1 the material will occur in the glassy state during drying if its temperature does not exceed the Tp2. In practice this is difficult
to achieve due to a low value of Tg1. The second option is to
modify the composition of the material in a way to retain
the temperature Tp1 and achieve a higher value of glass transition temperature Tg2 (where Tg2>Tp1). The way to increase
the glass transition temperature of a material subjected to
drying is to add substances such as starch, maltodextrin or
gum Arabic [Truong et al., 2004; Gabas et al., 2007; Sahu,
2008; Jittanit et al., 2010].
The available literature contains a few examples regarding the drying of honey and usually authors do not present
the physical properties of honey powders, so in a current paper the results are compared and discussed also with the results regarding spray dried fruit juices. In most cases, the produced dried honey preparations contain up to 50% of honey
in solid material. Cui et al. [2008] suggested a method of a microwave-vacuum drying of honey. The authors found that
the best parameters for drying, resulting in the material with
a water content of 3%, was the pressure of 30 mbar and film
thickness below 8 mm. The content of sugars and aromatic
substances remained unchanged after the drying process. Unfortunately, the authors did not specify the physical properties of the resultant product. Because of layer drying, it can
be hypothesised that the final form of the material resembled
hard solidified blocks rather than powder, which is not a beneficial form for further trade and use. Sahu [2008] vacuum
dried liquid honey with three additives (maltodextrin, glycerol
monostreate, tricalcium phosphate at different concentrations), spreading the mixes to a thickness of 3 mm. Vacuum
(710–750 mm Hg) and temperature of 70°C were applied.
Powder form was obtained by grounding in a hammer mill.
Powders hygroscopicity (values between 4.32 and 12.3%)
increased by decreasing the amount of maltodextrin and increased by increasing glycerol monostearate and tricalcium
phosphate. There was a decrease in degree of caking with an
increase in the amount of all the three ingredients.
A beneficial form of dried honey may be obtained after
spray drying with the addition of suitable drying agents [Hebbar et al., 2008; Sahu, 2008; Samborska & Czelejewska, 2014;
Samborska & Bieńkowska, 2013]. Spray drying is a method
which, in addition to removing water (its primary objective),
also allows the micro-encapsulation of substances vulnerable
to external conditions, and there are many such biologically-active compounds present in honey. However, it is necessary
to minimise the addition of drying agents so that the product
is as close to natural honey as possible.
Yoshihide & Hideaki [1993] spray dried honey with the addition of antioxidants, carriers, partial dispersants, and dispersants, adjusting feed pH between 6.5–7.5 in order to reduce
the thermoplasticity of sugary material during drying. Inlet
and outlet air temperatures were between 120 and 200°C
and between 70 and 120°C, respectively. The powder with
the honey content of about 50% d.b. had good physical properties, pleasant flavour and taste. Samborska & Czelejewska
[2014] used Arabic gum as a drying agent (honey solids: carrier
solids, 1:1) and obtained powders of good physical properties:
low water content and medium or good flowability. Samborska & Bieńkowska [2013] characterised the properties of spray
dried honey preparations produced with the use of maltodextrin and dextrin as drying additives – those obtained with
dextrin had higher hygroscopicity and worse solubility than
those produced with maltodextrin. Takashi [1984] spray dried
honey with the use of waxy starch as a carrier material at inlet
temperature between 140 and 150°C (outlet 90–95°C). The resultant powder of final honey content less than 50% had a tendency to melt when exposed to air, hence the use of high barrier packaging materials was suggested. Hebbar et al. [2002]
developed an improved method for the production of spray
dried honey powder of honey content <52% solids, characteristic honey flavour, acceptable colour and of a free-flowing
properties. Additives such as dextrin, maltose, and anticaking
agent were used. Inlet and outlet temperatures (115–125°C
and 80–85°C respectively) were lower than the conditions employed in the studies described above.
The aim of this study was to investigate the possibility of honey spray drying with the addition of maltodextrin
and gum Arabic as processing agents. The impact of the concentration of the solution subjected to drying, the kind
and content of the drying agent material upon the physical
properties of resultant powders was examined. An attempt
was also made to produce honey powder with a honey content of more than 50% d.b.
MATERIALS AND METHODS
Materials and preparation
Multifloral honey obtained directly from a local bee-keeper with a total solid mass concentration of 79.0±0.1% d.b.
(water content 19.6±0.1% w/w) was used in the study. Maltodextrin 10 DE (MD, Peppees, Poland) and gum Arabic (GA,
Hortimex, Poland) were used as drying agents. Experimental
plan for spray drying of honey with maltodextrin, gum Arabic
and the mixture of both drying agents is presented in Table 1.
111
K. Samborska et al.
Taking into account the solids content of honey and drying
agents powders, 500 mL of solutions with the desired total
solids concentration and the desired ratio of honey solids/
drying agent solids were prepared. To obtain desired values
it was necessary to dilute honey with water. The total concentration of solids in the solutions was 20 and 30% d.b.. The ratio of honey solids/MD solids was 1:2 and 1:1 (honey content
33 and 50% d.b.), while with the use of GA the ratio of honey
solids/drying agent solids was 1:1 and 2:1 (honey content 50
and 66% d.b.). These values were selected in a preliminary
study as the ratios at which it is possible to dry honey/drying
agent/water solution, different for each additive substance.
Additional experiments were also performed with the mixture
of both drying agents MD+GA (1:1): total concentration
of solids in the solutions was 20 and 30% d.b, ratio of honey
solids/drying agent solids 1:1. Two runs were performed for
each sample.
Viscosity analysis
Measurements were carried out on 16 mL samples using
BROOKFIELD digital rheometer model DV-III (Brookfield
Engineering Laboratories Inc., USA) in a range of shear stress
corresponding to shear rates from 0 to 200 s-1. The rheometer
was equipped with a spindle type ULA that rotated in the sample-containing chamber at 25°C. All viscosity measurements, expressed in mPa×s, were performed in duplicate and averaged.
Spray drying
The drying was performed in a laboratory spray dryer Lab
S1 (Anhydro, Denmark) with 1 m internal diameter, equipped
with a rotating disc, using the following process parameters:
inlet air temperature 180°C, raw material feed rate 1 mL/s,
the rotational speed of a disc atomizer 39,000 rpm. During
drying, the inlet and outlet air temperature was controlled
and both values remained constant.
Powder analysis
Moisture content
Duplicate powder samples (approximately 1 g each)
were dried at 105°C for 4 h [Chegini & Ghobadian, 2005].
The change in weight after treatment caused by water loss
was expressed in percent by weight. The average of duplicate
samples was calculated.
Bulk density
The powder loose (dl) and tapped (dt) bulk densities were
measured using an automatic tapper STAV 2003 (Engelsmann AG, Germany) by determining the volume occupied
by 100 g of powder (tapped density after 100 taps).
Cohesiveness
Cohesiveness of the powders was evaluated based on
Hausner ratio (HR), calculated from the loose (db) and tapped
(dt) densities of the powder: HR = dt/dl.
Apparent particle density
Apparent density of the spray dried powders was analysed using helium pycnometer Stereopycnometer (Quantachrome Instruments, USA). The pycnometric particle den-
sity was determined by measuring the volume occupied
by a known mass of powder which is equivalent to the volume
of displaced helium gas.
Hygroscopicity
Approximately 1 g samples of powder were placed
in a desiccator under the following conditions: 25°C and 75%
relative humidity (saturated NaCl solution) [Greenspan,
1977]. The gain in weight of the samples was measured during 2 h in 30 min intervals. Hygroscopicity has been expressed
as the amount of water adsorbed by 100 grams of powder
[Goula & Adamopoulos, 2010].
Wettability
Wettability was determined according to Jinapong et al.
[2008] with some modifications. 100 mL of distilled water was poured into 800 mL beaker. A glass funnel was
placed inside the beaker, with the height between the bottom
of the funnel and the water surface of 100 mm. The lower
opening of the funnel with the diameter of 40 mm was blocked
by a test tube. The powder sample (10 g) was placed around
the test tube and then the tube was lifted while the stop watch
was started at the same time. The time necessary to wet all
powder particles was recorded (visually assessed as when all
the powder particles penetrated the surface of the water).
All analyses were performed in duplicate immediately after drying and the averages of these measurements were recorded.
Statistical methods
Analysis of variance was performed in order to determine
whether the differences between obtained values were statistically significant. If the P-value of the F-test was less than
0.05, there was a statistically significant difference between
the means at the 95.0% confidence level. To determine which
means are significantly different from others the Multiple
Range Tests were performed.
RESULTS AND DISCUSSION
Viscosity
Viscosity measurements showed that all the feed solutions
followed a Newtonian behaviour, no shear-thickening or shear-thinning effects were observed under the applied measurement
conditions (25°C, spindle type ULA, range of shear stress corresponding to shear rates from 0 to 200 s-1). The estimated viscosity values ranged from 1.9 to 50.4 mPa×s, and were higher
for GA mixtures than MD at the same concentration (Table 1).
Solutions of lower solid concentration and higher honey content had lower viscosity. In most of the published papers, honeys are reported to be Newtonian fluids [Bhanardi et al., 1999].
Popek [2002] presented the dynamic viscosity of aqueous 20%
solutions of honeys belonging to the variety types: the values
varied between 1.525 and 1.755 mPa×s. It is also generally
recognized that dispersions of maltodextrin and gum Arabic
are Newtonian in nature at concentrations of >10% [Meer,
1980; Dokic et al., 1998; Mothe & Rao, 1999]. In the work presented by Shi et al. [2013], an increase in MD total solid ratio
in the feed solution (water/MD/Capilano Natural Australian
112
Honey Spray Drying
honey solution) from 0 to 39.5% led to a negligible increase
in viscosity from 6.00 to 8.00 mPa×s.
Production of honey powder
As described in the theoretical part, due to very low glass
transition temperature of natural honey, the spray drying
of honey without any additives is impossible. The first drying
agent used in order to facilitate the drying of honey through
increasing Tg was 10 DE maltodextrin (MD). Based on literature data the experiments began with drying of an aqueous
solution of honey and MD in which the ratio of solids derived
from honey to the solids originating from the drying agent
was 1:2 and the overall dry matter concentration was 30
and 20 % d.b. These dryings proceeded smoothly and once
they had been completed a significant amount of powder
was obtained directly in the cyclone. It was also possible to
recover the remaining powder from the walls of the drying
chamber. Next, the MD addition in relation to honey solids
was reduced so that the honey/drying agent solids ratio was
1:1. After these dryings, the quantity of powder in the cyclone was much smaller, the main bulk was recovered after
cleaning the walls of the chamber. Also much greater sticking of chamber walls occurred and this level of MD was considered minimal to obtain powdered honey. Similar observations were presented by Papadakis et al. [2006] who spray
dried raisin juice concentrate with the addition of MD 21
DE, 12 DE and 6 DE. Decreasing the content of MD in relation to the honey solids led to the reduction in the recovery
of feed solids in the produced powder. In case of maltodextrin 12 DE the minimum content of the drying agent was
50% d.b., like in the current study. Shrestha et al. [2007] presented a relationship between Tg and product recovery after
spray drying of orange juice with MD 6 DE. An increase
in MD level from 50 to 60 parts resulted in a significant
increase in product recovery (from 22 up to 78%), which
was also accompanied by a huge jump in Tg from 66.4 to
7.0-8.0
6.0-7.0
5.0-6.0
4.0-5.0
3.0-4.0
2.0-3.0
8.0
8.0
MD
7.0
Water content (%)
86.4ºC. The authors suggested that the maximum concentration of orange juice that can be dried in conjunction with
MD (6 DE) is 40%.
In the following experiments the drying tests were carried out with solutions of honey with GA. Tonon et al. [2009]
and Telis & Martinez-Navarrete [2009] have presented the Tg
of spray dried açai juice and grapefruit juice powder obtained
with MD and GA. Values obtained with the use of GA were
higher than with MD. Based on these data, suggesting better
GA properties as an anti-sticky agent, it was decided to start
experiments with the GA from 1:1 level of drying agent content in relation to honey solids. Observations of the drying
process, the quantities of powder in the cyclone and the material remaining on the walls of the chamber confirmed
these assumptions, because it looked similar to drying with
the MD at the honey/drying agent ratio of 1:2. Subsequently,
the honey content was increased in relation to GA content
to the honey/drying agent ratio of 2:1. As in the case of MD,
it resulted in a reduction of the obtained powder quantity
and an increase of the material remaining on the chamber
walls. However, in all cases the amount of powder was sufficient to carry out further analysis of their physical properties. To sum up, using GA it was possible to obtain a honey
preparation with a higher content of honey (67% d.b.) than
using MD (50% d.b.).
Since the GA is more expensive than the MD, it was decided
to carry out an experiment designed to replace part of the GA
in a solution of MD, drying the samples containing honey, at
MD and GA in the ratio of 2:1:1. Overall, the ratio of honey
to the drying agent in these solutions was 1:1 and the overall
concentration of dry substance was 30 and 20 g/100 g like
before. The course of drying and the amount of powders obtained did not deviate from the samples dried using the GA
in a 1:1 ratio to honey, which showed the feasibility of replacing parts of GA with MD without reducing the efficiency
of the process.
6.0
6.0
5.0
5.0
4.0
4.0
3.0
3.0
2.0
30
1:2
Honey:MD
1:1
20
GA
7.0
lids ion
So ntrat
e
nc (%)
co
2.0
30
1:1
Honey:GA
2:1
20
lids ion
So ntrat
e
nc (%)
co
FIGURE 2. Surface plot showing the effect of honey content and solids concentration on water content of powders obtained after spray drying of honey
solution with maltodextrin (MD) and gum Arabic (GA).
K. Samborska et al.
Physical properties of produced powders
Moisture content of powders
Water content of the obtained powders ranged from 2.7
to 8.6% (Figure 2). The differences between the obtained
values were statistically significant (Table 2). The derived
values were typical for powders obtained by spray drying
of materials such as: multifloral honey (3.8 to 5.5% of water) [Ram, 2011], multifloral and rape honey spray dried
with GA (7.1 and 7.3% of water) [Samborska & Czelejewska,
2014], juice from passion fruit with the addition of a mixture
of MD (10 DE) and lactose (2.4 to 9.4% of water) [RuizCabrera et al., 2009]. Vacuum dried honey powders obtained
without the use of any additives had moisture contents values
of 2.5% or less [Cui et al., 2008].
Powders obtained using the MD usually contained
less water than those containing GA. For example, the water content in experiment 4 with the use of MD was 2.7%
and after the change of drying agent into GA (experiment 6)
it increased to 7.2%. This relationship was also confirmed
in the case of the pairs of experiments 3 and 5, which also had
the same quantitative composition of the dried solutions but
a different drying agent. The difference in the water content
between these variants was statistically significant. A similar relationship was shown by Telis & Martinez-Navarrete
[2009] who examined the properties of freeze-dried grapefruit juice. The same authors and Gabas et al. [2007] who
studied the sorption properties of vacuum dried pineapple
pulp found that the water content in monolayer was higher
in case of dried material without additives, and after adding
GA it was higher than in case of MD. Gabas et al. [2007]
found that the presence of additives in pineapple pulp probably modified the balance of hydrophilic/hydrophobic sites,
promoting a smaller amount of sorbed water. In the current work, this phenomenon was also observed, because
the amount of water in powders obtained with MD was lower
than in case of GA. This relationship can be connected with
the viscosity of feed solutions, which affects the size of droplets produced during atomization, and thus the drying rate:
the higher the liquid viscosity the larger the droplets and powder particles [Masters, 1991; Tonon et al., 2008]. MD/honey
solutions had usually lower viscosity than GA/honey solutions, so it can be assumed that the droplets were smaller,
which could result in a higher drying rate leading to lower water contents in MD/honey powders. However, the correlation
between the feed viscosity and water content was not always
so clear, because the reduction of viscosity after decreasing
of solid concentration from 30 to 20 g/100 g did not lead to
the reduction of water content in the powders.
Powders obtained using the same drying agent with
the same weight ratio of honey to the drying agent had
a very similar water content regardless of the concentration
of dry substance in the solution. Most of the results were laid
in pairs, which is also clearly seen in Figure 2 This dependence has also been confirmed by the statistical analysis –
in most cases there was no statistically significant difference
between the water content of powders obtained from solutions of the same weight ratio of honey to the drying agent but
with different concentrations of dry substance in a solution
designed for drying.
113
The water content of the powders depended on the ratio
of drying agent to the honey solids and thus on the content
of the drying agent in a dried solution. The higher the content of the drying agent in the solution the higher the final
water content of the obtained powders (compare e.g. experiments 1 with 3 and 5 with 7) what can be connected with increased viscosity of feed solutions at increased drying agent
content. Similar results were achieved by Tonon et al. [2008]
who studied the spray dried açai (Euterpe oleracea Mart.)
juice and by Papadakis et al. [2006] who spray dried raisin
juice concentrate with the addition of MD. Goula & Adamopulus [2010] also found that the water content in the resulting powder increased along with an increasing MD content
in the solution. This relationship was explained by the fact
that for small water molecules it is difficult to diffuse through
the larger maltodextrin molecules in the solution. Papadakis et al. [2006] spray dried a concentrated raisin juice at
inlet air temperature of 110°C using the MD of varying dextrose equivalent (6 DE, 12 DE and 21 DE) and claimed that
the maximum ratio of the content of the concentrated raisin
juice to the added MD 6 DE was 67/33. Under these conditions, it was possible to obtain a stable powder with satisfactory physical properties.
Powders obtained after drying with mixed drying agents
were characterised by water content at a level equal to or higher than recorded in powders with GA at the content of 1:1
in relation to the honey. The water content of powder derived
from a solution of 30% d.b. was significantly higher than that
of a 20 % d.b. solution.
Powders loose and tapped bulk density
Loose bulk density of powders obtained with MD ranged
from 0.33 to 0.55 g/cm3, with GA – from 0.31 to 0.47 g/cm3,
and with MD+GA – from 0.47 to 0.58 g/cm3. Samborska &
Bieńkowska [2013] after spray drying of multifloral honey
with MD and dextrin reported loose bulk density of powders
between 0.41 and 0.56 g/cm3, while in a study by Goula & Adamopoulos [2010] the bulk density of powders obtained after
spray drying of orange juice with the addition of MD ranged
from 0.14 to 0.41 g/cm3.
The type of the drying agent had a significant impact on
the bulk density of powders. Comparison of the samples with
the same quantitative composition but different drying agent
type (e.g. in the following pairs of experiments: 3,5 and 4,6) reveals that a lower bulk density was observed in those samples
which were dried using GA in comparison to those dried with
MD. As shown in the Table 1, GA/honey mixtures had higher
viscosity than MD/honey, which could lead to the production
of larger droplets and particles, and this parameter is known
to be negatively correlated with the bulk density: as typically,
bulk density increases upon particle size decrease [Goula &
Adamopoulos, 2005; Grabowski et al., 2006].
In the case of powders dried using the same drying agent
these results were correlated with water content, i.e. powders
with lower water content had higher bulk density. Janiszewska et al. [2008] argued that the higher water content in powders causes their gathering into larger aggregations, which
causes more empty voids between particles. This, in turn, results in a reduction of bulk density.
114
Differences in the loose density of powders with the same
ratio of honey to the drying agent content but different initial
concentrations of the solutions were not statistically significant.
The relationship of honey to the drying agent had the greatest impact on the value of loose bulk density – the lesser drying
agent (in terms of weight) to honey ratio in the dried solution,
the higher the bulk density of powders. This relationship can
be seen by comparing samples 1, 2 with samples 3, 4 (MD) or
samples 5, 6 with 7, 8 (GA). According to Goula & Adamopoulos [2010], the more sticky nature of a powder is associated with a high bulk density, thus the powders of smaller drying
agent content, being more sticky, are characterised by a higher bulk density. In addition, increasing the amount of a drying
agent may cause increasing amounts of air trapped between
the particles, which is associated with skin-forming properties of MD. Kwapinska & Zbicinski [2005] found that particles of material with skin-forming properties often contain
air bubbles from raw materials or adsorbed during spraying.
Therefore, the greater the content of the drying agent with
air bubbles the lower the bulk density of powders. Results reported by Shrestha et al. [2007] also confirm this relationship
– the powder obtained by spray drying of orange juice was
characterised by increasing bulk density along with decreasing amount of drying agent.
Goula & Adamopoulos [2010] observed that a loose bulk
density is strongly linked to the water content of the powder.
The higher the water content of the powder the more particles
are combined into larger clusters resulting in voids occurring
between them, which in turn results in a loose bulk density reduction. Results obtained in our study support this conclusion.
Bulk density of powders obtained after drying using a mixture of drying agents was in homogenous group b (20% d.b.)
and group c (30% d.b.), i.e. it remained at medium and high
level. These values were similar to those obtained after drying
with individual drying agents with their lower content in relation to honey – 1:1 ratio in the case of MD and 2:1 ratio
in the case of GA.
Cohesiveness
Hausner ratio (HR) values of the produced powders
ranged from 1.05 to 1.29 (Table 2). According to the classification given by Geldart et al. [1984], powders with HR values
below 1.25 are referred to as free flowing with low cohesiveness, while powders with HR values within the range of 1.25–
–1.4 are of average cohesiveness, and powders with HR higher
than 1.4 are highly cohesive. Cohesiveness of powders determines their consistency and flow properties – the lower the cohesiveness the better the flowability of powders [Domian &
Poszytek, 2005]. These results agree with previous studies on
honey spray drying: Samborska et al. [2011] (multifloral honey with MD, HR value 1.26), and Samborska & Czelejewska
[2014] (multifloral and rape honey with GA, HR values 1.29
and 1.24).
The Hausner ratio depended significantly on the type
of drying agent content and the weight ratio of honey to
the drying agent in the dried solution. By comparing 1:1 samples obtained from MD with those 1:1 from GA both with
30 and 20% d.b., it can be concluded that the powders with
MD were characterised by a Hausner ratio lower than that
Honey Spray Drying
TABLE. 1. Experimental plan for spray drying of honey with maltodextrin, gum Arabic and the mixture of both drying agents.
Experiment
1
Honey/
Solids
Drying agent:
drying agent concentration
MD, GA
ratio
(% d.b.)
MD
1:2
30
Viscosity
(mPa×s)
8.7±0.2d1
2
MD
1:2
20
2.2±0.3ab
3
MD
1:1
30
4.5±0.4bc
4
MD
1:1
20
1.9±0.4a
5
GA
1:1
30
50.4±0.9h
6
GA
1:1
20
13.2±0.5e
7
GA
2:1
30
23.8±0.3g
8
GA
2:1
20
5.0±0.3c
9
MD+GA (1:1)
1:1
30
19.1±1.7f
10
MD+GA (1:1)
1:1
20
4.1±0.1bc
MD – maltodextrin, GA – gum Arabic
with GA, which indicates their better flowability. Reduction
of the drying agent content in dried solutions affected the HR
reduction of powders obtained from both MD and GA, i.e.
powders with a higher content of honey had better flowability. Two powders of the worst flowability were those obtained
with the use of GA – honey/ drying agent ratio 1:1 (experiments 5 and 6) – HR value higher than 1.25
The concentration of solids in dried solutions had no significant effect on the Hausner ratio because most samples
dried using the same drying agent (at the same weight ratio)
were in the same homogeneous group regardless of the concentration of solids in the initial solution.
On the basis of these results, it can be concluded that
in terms of flowability the best powders were those obtained
with the use of MD – honey/MD ratio 1:1 (experiments 3
and 4), followed by those obtained with the use of GA – honey/GA ratio 2:1 (experiments 7 and 8). Those were samples
with a higher content of honey in dried solutions.
The comparison of Hausner ratio values of the obtained
powders with their other physical properties reveals a clear
relationship between HR and water content in the powders.
The comparison of powders with the same drying agent
showed clearly that the greater the water content in the powders the higher was the Hausner ratio. The Hausner ratio was
also correlated with loose bulk density of the powders. Higher loose bulk density of powder corresponded with a lower
Hausner ratio. This indicates that the powders with higher
loose bulk density were the ones with lower cohesiveness
and better flowability.
Powders obtained after drying using a mixture of drying
agents in comparison to other powders were characterised
by the average level of cohesiveness. However, the HR value
below 1.25 indicated a low level of cohesiveness and good
flowability of these powders.
Hygroscopicity
Hygroscopicity of the analysed powders was high
and ranged from 65 to 145 g/100 g powder. Statistical analysis
showed a division of values into three homogeneous groups.
In the first group, with the lowest values of hygroscopicity,
115
K. Samborska et al.
Experiment1
Outlet air
temperature (°C)
Water content
(%)
Loose bulk density
dl (g/cm3)
Tapped bulk density
dt (g/cm3)
Hausner Ratio HR
Apparent
density (g/cm3)
Hygroscopicity
(g/100 g)
Wetting time
(s)
TABLE. 2. Outlet air temperature during drying and physical properties of dry product.
1
66.0±0.5
5.4±0.2c
0.33±0.01a
0.41±0.03a
1.24±0.03c
1.53±0.01de
85±4a
54±3b
2
67.0±0.7
5.4±0.1c
0.33±0.01a
0.38±0.02a
1.18±0.01b
1.45±0.01b
72±3a
57±2b
3
69.0±0.5
3.7±0.4
0.55±0.01
0.57±0.02
1.06±0.02
1.48±0.01
65±4
6±0a
4
71.0±0.5
2.7±0.2a
0.55±0.02c
0.57±0.04c
1.05±0.01a
1.41±0.02a
75±16a
4±0a
5
70.0±0.7
7.2±0.1
0.34±0.02
0.39±0.04
1.26±0.09
1.61±0.01
c
145±7
120±1c
6
72.0±0.7
7.2±0.3d
0.32±0.01a
0.43±0.04ab
1.29±0.04c
1.58±0.02fg
120±8b
120±1c
7
69.0±0.7
4.7±0.3
0.45±0.02
0.49±0.04
1.11±0.01
1.57±0.01
b
115±23
9±0a
8
69.0±0.7
5.0±0.1c
0.47±0.03b
0.54±0.04b
1.15±0.06b
1.55±0.01ef
122±13b
9±0a
9
70.0±1.4
8.6±0.4
0.58±0.01
0.70±0.01
1.20±0.04
1.50±0.01
130±7
5±0a
10
71.0±1.4
6.9±0.2d
0.56±0.05b
1.21±0.01b
1.50±0.01bc
132±14bc
17±0a
b
d
c
e
c
a
b
c
0.47±0.02b
c
a
b
c
a
c
ab
b
bc
g
f
cd
a
bc
Experiments descriptions as presented in Table 1. a-g Means with the same superscript within same column are not significantly different (P<0.05).
1
were the powders obtained using MD. The powders produced
with the addition of GA and GA + MD were enrolled into
two consecutive groups characterised by significantly higher
values of hygroscopicity.
Hygroscopicity values obtained are much higher than
in other studies regarding fruit powders. For example, Rodriguez-Hernandes et al. [2005], who spray dried cactus pear
juice with the addition of 18 or 23% MD obtained the hygroscopicity of powders varying from 36.3 to 48.9% d.b. Goula
& Adamopoulos [2010] analysed the powder obtained after
spray drying of orange juice with the addition of MD 12 DE
in a ratio of 1:1 and 1:4 (orange juice: MD) and obtained
the hygroscopicity of 4.2–5.3 g/100 g of solids depending on
the drying temperature. Values in the current study regarding
powders obtained with the use of MD (65–85 g/100 g powder)
are higher than those presented above, which may indicate
much higher hygroscopicity of other ingredients of the powders derived from honey.
Tonon et al. [2008] found that MD concentration was
the variable which affected the hygroscopicity of açai powders
produced by spray drying. The lowest hygroscopicity values
were obtained when the highest MD concentrations were used
and this correlated with the fact that MD is a material of low
hygroscopicity. Rodriguez-Hernandes et al. [2005] observed
the same relationship working with spray dried cactus pear juice.
However, Papadakis et al. [2006] did not observe a clear pattern
for the differences between the powders of spray dried raisin
juice concentrate and different juice:MD ratios. In the current
study, these differences were also statistically insignificant.
Gabas et al. [2007] have investigated the water sorption
properties of pineapple powder obtained by vacuum drying
with the addition of MD and GA. The monolayer water content was of particular interest, since it could be taken as an indicator of the volume of water that can be strongly adsorbed
to specific sites at the food surface. Samples containing MD
resulted in the value of the monolayer moisture content Xm
between 5.8 and 6.9% (dry basis), whereas samples with gum
Arabic showed Xm between 6.7 and 7.9% (dry basis). Lower
values of monolayer moisture content reported in the case
of the powder containing MD indicate that this material was
characterised by lower hygroscopicity than GA, which corresponds to the results shown in the present paper. Telis &
Martinez-Navarrete [2009] studying the sorption properties
of grapefruit juice powder also found that for samples with
additives the Xm values were lower than for pure grapefruit
juice, being also lower for samples with MD than with GA. According to Perez-Alonso et al. [2006], this phenomenon is attributed to a combination of factors such as the conformation
and topology of molecule and the hydrophilic/hydrophobic
sites adsorbed at the interface.
Hygroscopicity of powders obtained through drying with
MD+GA was at the same level as for the powders obtained
with GA.
Apparent (absolute) density
Apparent (absolute) density of the obtained powders ranged
from 1.41 to 1.61 g/cm3 (Figure 3). Both the drying agent type
and the ratio of honey to the drying agent in the dried solution
had a significant effect on the apparent density of powders and,
in the case of MD, also the initial concentration of the dried
solution (Table 2). The value of apparent density shows the internal porosity of particles – the more “compact” a particle
is the higher the apparent density. Janiszewska & Witrowa-Rajchert [2007] found that the higher the initial concentration
of the dried solution the greater the values of apparent density
of powders (lower internal porosity). Powders discussed in this
paper show such a relationship but the differences were statistically significant only in the case of powders obtained with
MD. For both drying agents, the apparent density was correlated with the viscosity of feed solutions.
116
Honey Spray Drying
1.40-1.43
1.43-1.46
1.52-1.55
1.55-1.58
1.58-1.61
1.64
Apparent density (g/cm3)
1.49-1.52
1.46-1.49
1.64
MD
1.61
1.58
1.58
1.55
1.55
1.52
1.52
1.49
1.49
1.46
1.46
1.43
1.43
1.40
1:2
Honey:GA
1:1
20
30
s on
d
i
l
i
So ntrat
e )
c
n (%
co
GA
1.61
1.40
30
1:1
Honey:GA
2:1
20
FIGURE 3. Surface plot showing the effect of honey content and solids concentration on apparent density (g/cm3) of powders obtained after spray
drying of honey solution with maltodextrin (MD) and gum Arabic (GA).
Abadio et al. [2004] obtained very similar values of apparent density of pineapple juice spray dried with MD (from 1.47
to 1.58 g/cm3). However, they found that the increasing content of MD leads to a reduction in apparent density of powders, which has not been confirmed in the present study.
Tonon et al. [2010] determined the absolute density of açai
juice powdered by spray drying with addition of MD (10 DE
and 20 DE), GA and tapioca starch, yielding values ranging
from 1.49 to 1.53 g/cm3. In contrast to the current research,
the samples produced with GA showed absolute density lower
than these produced with MD 10 DE.
The powders dried with MD were characterised by significantly lower apparent density than those dried with GA. This
conclusion can be reached by comparing samples 3 with 5
and 4 with 6. Janiszewska & Witrowa-Rajchert [2009] observed
a similar relationship after microencapsulation of rosemary
aroma by spray drying method. This indicates that the powders
dried with MD were characterised by higher internal porosity.
Samples dried using MD + GA were characterised by an
apparent density close to the apparent density of samples
dried using MD. Powders with a higher content of honey had
a lower apparent density.
Wettability
Wettability of the obtained powders ranged from 4.5 to 120 s
(Table 2). Both the drying agent type and the honey content
in the dried solution had a significant effect on this parameter.
MD powders had better wettability than those with GA. In general, it can be also seen that wettability increased significantly
(it means the wetting time was shorter) with an increase of honey content in the powder. It may be due to the higher sugar
content in the powder, which causes that the moisture was adsorbed more rapidly [Jinapong et al., 2008].
Wettability of the powders dried with mixed drying agents
did not differ significantly from that recorded in powders with
MD:honey content ratio of 1:1.
CONCLUSIONS
Spray drying of honey and production of powders using MD and GA as drying agents was conducted. It was
found that the behaviour of GA as a drying agent was more
beneficial because it allowed obtaining powders of higher
honey content (67% of honey in powder solids) than MD
(50% of honey in powder solids.). However, it was found that
the powders dried with GA were characterised by less favourable physical properties: higher water content, higher hygroscopicity and wetting time, higher cohesiveness indicating
poorer flowability. Replacing half of the GA with MD helped
to maintain high drying efficiency and thus obtaining powders with better flowability and wettability than those with
GA. However, their hygroscopicity remained at equally high
level. The aim of further research will be to produce honey
powder with a higher content of honey and also more favourable physical properties.
ACKNOWLEDGEMENTS
This research was funded by The National Science Centre
(Poland) under Grant No. N 312 267 140.
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Submitted: 4 February 2014. Revised: 25 April 2014. Accepted:
12 May 2014. Published on-line: 15 December 2014.
Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 119–126
DOI: 10.1515/pjfns-2015-0031
http://journal.pan.olsztyn.pl
Original article
Section: Food Quality and Functionality
Effect of Microwave Treatment on Microbial Contamination of Honeys
and on Their Physicochemical and Thermal Properties
María de la Paz Moliné1,2,5*, Natalia Jorgelina Fernández1,4,
Sandra Karina Medici1,6, Diana Fasce3, Liesel Brenda Gende1,2,4
Centro de Investigación en Abejas Sociales, Departamento de Biología, Facultad de Ciencias Exactas y Naturales,
Universidad Nacional de Mar del Plata, Mar del Plata, Argentina
2
Departamento de Química, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata,
Mar del Plata, Argentina
3
INTEMA, Instituto de Investigaciones en Ciencia y Tecnología de Materiales, UNMDP-CONICET, Mar del Plata,
Argentina
4
CONICET, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
5
CIC, La Plata, Buenos Aires, Argentina
6
Laboratorio Fares Taie, Mar del Plata, Argentina
1
Key words: honeys, microwave, microbial count, physicochemical and thermal properties, Paenibacillus larvae
In recent years, microwave heating has become a common method for pasteurization and sterilization of food. Honey is a sweet substance produced by worker honeybees from nectar of flowers. The major microbial contaminants include moulds and yeasts, as well as the spore-forming bacteria, being their counts indicative of honeys’ commercial quality and safety. Paenibacillus larvae is also of interest since it causes American foulbrood
(AFB) in honeybee larvae. The main quality factors that are used in the honey international trade are moisture, hydroxymethylfurfural content (HMF),
and enzymatic indices. Moreover, honey exhibits several thermal events, the most important being the glass transition temperature (Tg). The aim of this
work was to evaluate microwave effect (800 watts during 45 and 90 seconds) on microbial content in particular over P. larvae spores retained in honey,
and on physicochemical and thermal properties. Microwave promoted a decrease of microbial count with time of exposure, including P. larvae. Moisture content diminished after treatment, while Tg increased linearly, and acidity decremented in the majority of cases. Honeys darkened and HMF
exceeded the permissible value. Diastase and glucose-oxidase enzymes were totally inactivated by microwave treatment.
INTRODUCTION
In industrial food processing, microwave energy has been
considered a faster process of pasteurization and sterilization
of food than conventional methods. Consequently, microwave radiation is regarded as an alternative method for killing
bacteria because of its effectiveness, commercial availability,
and lower cost compared with other technologies [Celandroni et al., 2004]. However, the effect of microwave on bacteria
depends on food composition and type of microorganism
[Castro et al., 1997].
Honey is a sweet product elaborated by worker honeybees
from nectar and secretion of flowers. It is collected and transformed by combining with specific substances, then stored
in honeycombs until maturation. Chemical composition involves a complex mixture of fructose (average 38.4 g/100 g),
glucose (average 30.3 g/100 g), sucrose (average 1.3 g/100 g),
* Corresponding Author: Tel: +549223 5468496;
E-mail: [email protected]
other carbohydrates (around 12 g/100 g), minerals (average
0.169 g/100 g) and proteins (enzymes and amino acids) (average 169 mg/100 g), with a moisture content of 17.2 g/100 g
[White et al., 1963]. It also contains organic acids such as
gluconic (0.43 g/100 g), and in a minor amount, acetic, citric,
lactic, succinic and formic acids, aromatic substances, pigments, wax and pollen grains [Desalegn, 2013]. Honey has
own flavor and aroma, and its color varies from colorless to
dark amber. Color has been frequently attributed to presence
of polyphenols, tannins, among others [Acquarone, 2004].
Due to its natural properties the number and variety of microorganisms present in honey is expected to be low. Low water activity (aw: 0.56 – 0.62), low pH (3.5 – 4.5), high organic
acids content, presence of hydrogen peroxide, phytochemicals
and/or antioxidants [Estrada et al., 2005; Coll Cardenas et al.,
2008] inhibit microorganisms growing. Handling and manipulation post harvest are the main sources of contamination,
usually by yeasts (e.g. Saccharomyces, Schizosac-charomyces
and Torula) and spore-forming bacteria (e.g. Aerobic Bacillus and anaerobic Clostridium spores). Spores and small
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
120
fragments of moulds may appear [Frazier & Westhoff, 1978;
Kedzia et al., 1996]. Among these, microorganisms that cause
honeybee diseases are of special interest. Paenibacillus larvae,
a spore-forming bacterium, is one of the major pathogens
of Apis mellifera responsible for an infection known as American foulbrood (AFB) [Alippi et al., 2002; Lauro et al., 2003].
Also, the species Paenibacillus alvei and Melisococcus pluton
in association may cause European foulbrood disease (EFB),
which is less important [Coll Cardenas et al., 2008]. The Argentine Food Code (AFC) requires total absence of coliforms
and Salmonella-Shigella, and less of 10 CFU/g of moulds
and yeasts. Other countries include a limit of 103 CFU/g for
aerobic mesophilic bacteria [Peréz et al., 2009].
According to AFC [1969], honey must meet the following requirements: maximum moisture of 18 g/100 g, an
acidity of 40 meq/kg, a minimum of 8 diastase units (Gothe
scale) and a maximum of 40 mg/kg of hydroxymethylfurfural
(HMF).
Moisture is the major factor that influences the keeping
quality or storability of honey especially concerning the risk
of spoilage due to fermentation [White, 1975]. The control
of moisture is relevant to prevent microbial growth. Acidity
is due to the presence of organic acids that contribute not only
to honey flavour, but also to stability against microbial spoilage [Coll Cardenas et al., 2008].
Several enzymes are present in honey, such as invertase,
diastase, glucose-oxidase, catalase and acid phosphatase.
Invertase, glucose-oxidase and catalase are derived mostly
from the honeybees. Their activities depend on the intensity of the nectar flow and the amount of nectar processed
by the honeybees [Bachmann, 2007]. Enzyme activities are
usually measured to evaluate heating or storage effects.
The activity of diastase enzyme decreases with time of storage
and heating [Kowalski et al., 2012]. Glucose-oxidase is more
sensitive to temperature than diastase, so that its determination constitutes a good complement in thermally-treated
honey’s studies [Montenegro et al., 2006].
HMF is formed through the acid catalyzed dehydration
of hexoses (glucose and fructose) as well as from the decomposition of 3-deoxyosone in Maillard reaction [Belitz et al.,
2004]. Its content in fresh honey is very low, and it depends on
the floral origin and the chemical properties of honey [Singh
& Bath, 1998; Fallico et al., 2004; Zappala et al., 2005]. Its
concentration increases during storage in relation to pH
and time [Bath & Singh, 1999], but also due to honey heating [Bath & Singh, 1999; Bartakova et al., 2011]. The level
of HMF in food depends on the equilibrium between formation from precursors and destruction by oxidation [Morales
et al., 1997].
Honey is a supersaturated solution composed mainly
of sugars and water and can be thermally characterized
by its glass transition temperature (Tg). This is the temperature at which an amorphous material changes from rubbery to glassy state upon cooling. As Tg is very sensitive to
water content as well as to chemical composition [Bhandari
& Howes, 1999], it is a very useful tool to detect adulteration
[Cordella et al., 2003].
The consequences of microwave treatment over honey properties, such as moisture content, acidity [Dranca
Effect of Microwave Treatment on Honeys
& Oroian, 2013], HMF and enzyme activities [Kowalski et al.,
2012], as well as over mould and yeasts [Hebbar et al., 2003]
and bacteria spores have already been reported. Microwave
radiation cannot be thought as simple heating from the inside;
the E-field would produce effects on the biological molecules
affecting spore structure, different from those attributable only
to heat [Celandroni et al., 2004]. Gamma radiation method
was used to control this disease, in order to inactivate P. larvae,
although significant effect on chemical parameters and enzyme activities of honey were observed [Baggio et al., 2005].
At present, the effect of microwave radiation on P. larvae
has not been studied yet. So, the aim of this study was to evaluate the microwave impact on microbial content, in particular over P. larvae spores, and on physicochemical and thermal
properties.
MATERIALS AND METHODS
Samples
Eight honeys were obtained from different geographic
sites of Argentina. Five from Corrientes province (27°816 S;
58°5025 W), one from Entre Rios province (32°251.72 S;
60°1651.6 W) and the other two from Buenos Aires province (37°5259 S; 57°3609 W). They were kept in hermetic
containers and stored at 20–27ºC and 50% relative humidity [AOAC, 2000a]. Superficial layer was removed and honey was homogenized with sterile scoop. Microbial content
and physicochemical and thermal properties of honeys were
determined.
Microwave thermal treatment
Microwave heating studies were conducted in a microconvective oven with turntable attachment (Sharp R-3A55,
maximum power of 800 watts; Thailand). Experiments were
carried out at high microwave power of 800 watts, and for
heating periods of 45 and 90 seconds [Kohn’s, 1998]. Microbial content and physicochemical and thermal properties
of honeys were anew determined. The samples temperature
was measured at the end of the each heating period (Mean
temperature at 45 seconds: 83°C; mean temperature at
90 seconds: 108°C).
Analysis of microbial content
Aerobic mesophilic bacteria, moulds and yeast and sulphite-reductive Clostridium were counted. Also, P. larvae was
determined. Untreated honeys were used as a control.
In microbial counts of aerobic mesophilic bacteria
and moulds and yeast, ten grams of each sample were homogenized into 90 mL of sterile phosphate-buffered dilution
water (0.25 mol/L KH2PO4 adjusted to pH 7.2 with NaOH).
Decimal dilutions were made into the buffer. Aerobic mesophilic bacteria were counted onto standard plate count agar
(PCA) incubated at 37°C for 24–48 h [ICMSF, 1983]. Moulds
and yeasts were counted onto standard yeast extract–glucose–chloramphenicol (YGC) agar incubated at 22–25°C for
3–5 days [ICMSF, 1983]. Microbial counts were expressed as
colony-forming units per gram of honey (CFU/g).
Sulphite-reductive Clostridium count was determined with
horizontal method in anaerobes on Agar Sulphite Iron (TSC)
121
M. de la Paz Moliné et al.
with virgin cape to avoid contact between oxygen and microorganism. Plates were incubated in anaerobe jar at 37°C for
24–48 h [ISO 15213, 2003].
In order to quantify viable spore abundance of P. larvae,
five grams of honey were homogenized with 5 mL of sterile water [Alippi, 1995]. Serial dilutions (1:2 and 1:10)
of the honey solution were prepared and their aliquots
(100 μL) were plated on MYPGP agar [Dingman & Stahly,
1983], supplemented with 9 μg/L nalidixic acid to inhibit
Paenibacillus alvei growth, and incubated at 37ºC in microaerophilia (5–10 g/100 g CO2). The plates were checked after
2 and 4 days to count the number of P. larvae-like colony
forming units (CFU) for each dilution, which permitted to
calculate the number of viable spores per honey gram. Colonies were identified by catalase test [Heyndrickx et al., 1996],
Gram staining [Beveridge, 2001] and phenotypic characterization [Alippi, 1992].
All microbial tests were performed in triplicate.
Analysis of physicochemical properties
Moisture content, acidity, color, diastase, glucose-oxidase
and HMF were determined for all the samples. Untreated
honeys were used as a control.
Determination of moisture content was based on the refractometric method [AOAC, 2000b]. The refractive index
of samples was measured with a refractometer (Abbe; Science Instrumentation, Argentina) and results were converted
with the Chataway Table. Free acidity was determined by titrimetric method with a pH-meter (HANNA Instruments,
HI 2211 pH, pH/ORP meter; Rumania, Europe) according
to AOAC [2000c] technical, readings were corrected to meq
of acid/kg of honey. Measurements of color are based on a visual comparison with a colored glass reference. It was measured in the Pfund colorimeter (HANNA Instruments, C22f
digital, The United States) [Tabera et al., 2002].
Diastase catalyzes the transformation of starch to maltose.
It was determined quantitatively by official method in AOAC
[2003] and qualitatively according to Bianchi [1990]; both
methods are based in diastase action on starch solution,
using iodine-iodized solution to visualize the hydrolysis.
In qualitative determination diastase was analysed together
with glucose-oxidase. Glucose-oxidase activity determination
is based in action of glucose-oxidize on glucose, to produce
gluconic acid and hydroxide peroxides [Bianchi, 1990].
The HMF was measured by spectrophotometric method
suggested by White [1979] using Carrez solutions. The absorbance of the solutions at 284 and 336 nm was determined
using a spectrophotometer (Helios Unicam Gamma, England). The quantitative value of HMF was determined using
the proposed formula for the method reported by IHC [1999].
Analysis of thermal properties
Glass transition temperature (Tg) was determined for
all the samples. Untreated honeys were used as a control.
Tg was analyzed by differential scanning calorimetry (DSC)
using a Pyris 1, Perkin Elmer (Massachusetts, USA). Samples
were previously homogenized with a mechanical device (IEC
CC31R Multispeed Centrifuge Thermo Scientific, United
States) for 20 min, then placed in aluminum pans and heated
at a rate of 10ºC/min from -65ºC to 25ºC under N2 atmosphere. Tg was determined as the onset of the step decrease
in heat flow plot [Cordella et al., 2003].
Statistical analysis
It was performed with RStudio software, and data were
considered as binomial distribution and inflated zero. Widespread Linear Model was used for analyzed differences between microbial content and treatment time. Pearson’s correlation was used to analyze chemical data. Pearson’s linear
correlation coefficient (r) was calculated to the relationship between moisture content and Tg, and between HMF
and color. For all analyses, =0.05 was the level of statistical
significance.
RESULTS AND DISCUSSION
Microbial content
Honey may be expected to contain a small number
and a limited variety of microorganisms. Treated honeys presented a lower count of aerobic mesophilic bacteria, moulds
and yeast and P. larvae than virgin honeys (Table 1). Sulphite-reductive Clostridium was not observed.
In this study, aerobic mesophilic bacterial count was lower
than those obtained by Iurlina & Fritz [2005]. Microwave reduced significantly (p<2 × 10–16) count in response to the exposure time. Untreated honeys presented higher microbial variety in relation to treated ones. White colonies with radial or
spiral forms and yellow colonies were observed. All presented
positive-Gram coloration with basilar or coco-basilar structure. Wakita et al. [2001] and Iurlina & Fritz [2005] reported
colonies of Bacillus with concentric ring and irregular border.
Mould and yeast counts ranged between 0 to 766 UFC/g
in virgin honeys, whereas, Iurlina & Fritz [2005] observed
a lower values within a narrower range (164–191 UFC/g) for
honeys of southeast region of Buenos Aires province, Argentina. Microwave treatment resulted in a total reduction of these
counts after 90 seconds of exposure (p=0.015). Similar results were achieved by Hebbar et al. [2003].
P. larvae has the ability to generate extremely resilient
and long-lived spores [Shimanuki, 1997]. They can be transmitted by beekeepers unwittingly, shifting honeycombs between colonies, or by the honeybees themselves through robbing of colonies weakened by AFB [Fries & Camazine, 2001;
de Graaf et al., 2001]. It is widely recognized that honey will
retain spores of P. larvae [Sturtevant, 1936; Hansen & Rasmussen, 1986], and that inter-colony transmission of spores
in contaminated honey does occur [Fries & Camazine, 2001;
de Graaf et al., 2001]. In this study, P. larvae count was on
average 37 CFU/g, data according to Iurlina & Fritz [2005].
The microwave treatment reduced the count of P. larvae, but
this decrease was not statistically significant, a different result from gamma radiation method was observed [Baggio
et al., 2005].
Physicochemical and thermal properties
The moisture content in honeys depends on several factors, such as harvesting season, degree of maturity and climatic factors. The values determined for virgin honeys are
122
Effect of Microwave Treatment on Honeys
TABLE 1. Counts of aerobic mesophilic bacteria, moulds and yeasts and P. larvae in the analysed honeys.
Sample
Aerobic mesophilic bacteria
(CFU / g of honey)
Moulds and yeasts
(CFU / g of honey)
P. larvae
(CFU / g of honey)
Unt.
45’’
90’’
Unt.
45’’
90’’
Unt.
45’’
90’’
1
87
23
15
19
ZC
ZC
133
5
ZC
2
45
40
20
766
10
ZC
5
ZC
ZC
3
140
62
25
269
ZC
ZC
ZC
ZC
ZC
4
188
115
63
293
ZC
ZC
59
5
ZC
5
124
59
5
35
10
ZC
ZC
ZC
ZC
6
85
28
10
45
ZC
ZC
50
ZC
ZC
7
24
5
ZC
ZC
ZC
ZC
49
ZC
ZC
8
30
19
4
ZC
ZC
ZC
ZC
ZC
ZC
2.5 ± 4.6b
0b
37 ± 46.5a
1.3 ± 2.3a
0a
Mean ± SD 90.3 ± 57.7a
p
43.8 ± 34.7b
17.8 ± 20.1b 178.4 ± 265.7a
< 2 x 10–16
0.015
0.97
Unt. (untreated), 45’’ (treated honey 45 seconds at 800 watts). 90’’ (treated honey 90 seconds at 800 watts). ZC. Zero Count. p significance value
(widespread lineal model with binomial distribution and inflated zero). The same letter indicates no significant difference at the 95% confidence level.
TABLE 2. Moisture content, glass transition temperature (Tg) and acidity of the analysed honeys.
Sample
Moisture (g/100 g)
Tg (°C)
Acidity (meq/kg)
Unt.
45’’
90’’
Unt.
45’’
90’’
Unt.
45’’
90’’
1
19.6
19
18.8
-52.9
-51.7
-47.8
29
33
44
2
19.6
17.6
16.6
-50.4
-46.3
-44.2
42
35
35
3
21.4
19.8
16.2
-55.3
-53.3
-46.3
46
34
43
4
20.2
18.6
17.6
-53.4
-52.2
-47.3
36
41
30
5
19
17.2
16.6
-52.5
-48.8
-47.9
39
30
35
6
19.4
18.4
17.6
-51.1
-48.5
-44.9
36
31
32
7
21.8
20.8
18.4
-56.9
-56.2
-53.3
36
27
27
8
17.6
17.4
16.8
-47.1
-47.9
-45.3
32
29
32
19.8 ± 1.3
18.6 ± 1.2
17.3 ± 0.9
-52.5 ± 3
-50.6 ± 3.3
-47.1 ± 2.8
–
–
–
-0.9
-0.9
-0.6
–
–
–
–
–
–
Mean ± SD
r
Unt. (untreated), 45’’ (treated honey 45 seconds at 800 watts). 90’’ (treated honey 90 seconds at 800 watts). Pearson correlation coefficient (r) between
moisture and Tg.
higher than others reported for Argentine honeys [Acquarone,
2004; Iurlina & Fritz, 2005; Silvano et al., 2014] and Spanish
ones [Bentabol et al., 2014]. But, these values were similar to
those reported by Karabagias et al. [2014] for unifloral Greek
honeys. Meanwhile, the values were close to or even higher
than those permitted by international food codes as Codex
Alimentarius standard for honey 12–1981 (20 g/100 g)
and the AFC (18 g/100 g). After microwave treatment, moisture content diminished (Table 2), as it was expectable because of the sample temperature rise during the exposure to
microwave radiation.
Glass transition temperatures (Tg) of honeys from different botanical and geographic origins have already been reported [Cordella et al., 2003; Lazaridou et al., 2004; Costa
et al., 2011] but there is no available information about Argentine honeys. From the results it can be seen that Tg shifted to higher values after treatment, which is consistent with
the reduction in moisture content. It is well known that water acts as a plasticizer lowering the Tg [Bhandari & Howes,
1999; Ahmed et al., 2007]. Moreover, a linear relationship between Tg and moisture content was found (r=-0.94), which
is in agreement with other authors [Cordella et al., 2003].
Acidity is produced in honey, principally, by the action of the enzyme glucose-oxidase on glucose. The values
of most virgin honeys analyzed were below the limit permitted
by the Argentine Food Code (40 meq/Kg) and were similar to
these described by Aqcuarone [2004]. Bentabol et al. [2014]
reported similar results for this parameter, these values did
123
M. de la Paz Moliné et al.
TABLE 3. Color changes and hydroxymethylfurfural content in the analysed honeys.
Color (mm pfund)
Sample
HMF (mg / kg)
Unt.
45’’
90’’
Unt.
45’’
90’’
1
55
73
73
25
55
101
2
66
89
86
0
42
51
3
68
125
90
0
8
49
4
71
117
90
5
13
80
5
70
90
92
4
21
67
6
51
68
66
0
37
84
7
53
68
63
30
19
50
8
73
81
107
108
87
110
63.4 ± 9
88.9 ± 21.6
83.4 ± 14.9
22 ± 36.9
35.3 ± 26.2
74 ± 23.7
0.25
0.1
0.26
–
–
–
Mean ± SD
r
Unt. (untreated), 45’’ (treated honey 45 seconds at 800 watts). 90’’ (treated honey 90 seconds at 800 watts). Pearson correlation coefficient (r) between
color and HMF.
TABLE 4. Diastase index, diastase activity and glucose-oxidase activity in the analysed honeys.
Samples
Diastase index (DU)
Diastase activity
Glucose-oxidase activity
Unt.
45’’
90’’
Unt.
45’’
90’’
Unt.
45’’
90’’
1
22
0
UM*
+
–
–
+
–
–
2
11
0
UM*
+
–
–
+
–
–
3
31
0
UM*
+
–
–
+
–
–
4
18
2
UM*
+
–
–
+
–
–
5
15
2
UM*
+
–
–
+
–
–
6
5
0
UM*
+
–
–
+
–
–
7
9
0
UM*
+
–
–
+
–
–
8
7
0
UM*
+
–
–
+
–
–
Unt. (untreated), 45’’ (treated honey 45 seconds at 800 watts), 90’’ (treated honey 90 seconds at 800 watts), UM*: unmeasured, + (activity present),
– (activity absent).
not exceed the maximum established in the European Legislation for free acidity (50 meq/Kg). Microwave caused, in most
cases, reduced acidity probably due to volatile acids evaporation. This was in disagreeing with Dranca & Oroian [2013].
The color of honey has not been legislated, but can
be considered as another quality criterion. In this study a little
range of color was observed. It is recognized that the color
varied according to the melliferous areas, our results agreed
with data previously obtained by Aloise [2010] for the same
geographic zone. Similar range was observed in Greek unifloral honey [Karabagias et al., 2014].
One of the effects of thermal treatments of honey is the acceleration of Maillard reaction [Ibarz et al., 2000; Wong
& Stanton, 1989]. These reactions would be associated to
the non-enzymatic chemical changes of browning, leading to
the formation of a variety of brown pigments, and formation
of intermediate products as the HMF. In honey, Maillard reac-
tions are the sugars condense with free amino acids producing a variety of brown pigments. The difference in browning
rate of honey could be explained by differences in its amino
acid and reducing sugar contents. Other factors that would
influence the kinetics of Maillard browning could be the type
and thermal stability of amino acids and reducing sugars
which participate in the reaction [Turkmen et al., 2006].
In this study, a darkening of honeys was observed
and HMF amount incremented rapidly (r=0.3) (Table 3).
The trends in the variation of HMF content of the samples
clearly depicted the sensitivity of honey to the period of heating and temperature. The same was observed by Kowalski
et al. [2012], the increase caused by microwaves of the HMF
is not gradual and its formation did not take place in the individual honeys in the same way, depending on floral origin.
On the other hand, sample eight showed high concentration
of HMF before treatment, associated to, probably, adultera-
124
tion or previous overheating. Excessive amount of HMF has
been observed as evidence of overheating. Also, HMF is toxic
and cancerogenic [Michail et al., 2007].
Diastase index values of virgin honeys were in agreement
with reported values of Argentine honeys [Aquarone, 2004].
In Greek unifloral honeys, the range of diastase index values
was a little more wide [Karabagias et al., 2014]. It this thought
that the amount of enzyme activity is influenced by nectar
thickness and sugar content and by the fact how many times
it is transferred from honeybee to honeybee before nectar
is turned into honey [Bonvehi et al., 2000].
The parameter of diastase activity is regulated by international codes, while there are no requirements for the activity
of glucose-oxidase. Diastase index dropped below the acceptable value (Table 4), meanwhile, Hebbar et al. [2003]
obtained similar results. Kowalski et al. [2012] showed that
a short microwave treatment (0–2 min) with a low power
level (63 watts) did not influence the honey quality estimated
by means of diastase number.
In qualitative determination of both enzymes it was not
observed activity neither of diastase (blue color) nor glucose-oxidase (colorless) in treated honeys (Table 4). Glucose-oxidase data were in agreement with the study by Kretavictus
et al. [2010], where the authors observed a decrease of its
activity after exposure to a conventional thermal treatment.
CONCLUSIONS
Microwave treatment was found very effective in reducing
aerobic mesophilic bacteria, moulds, yeasts and P. larvae too.
This was the first register about the effect of microwave on
P. larvae.
Concerning to physicochemical and thermal properties variation, the treatment led to several undesirable consequences such as darkening, increase of HMF content
and enzymes inactivation. Despite this, from this study it can
be concluded that microwave treatment could be successfully
applied by properly selecting power and exposure time to control benefits and disadvantages.
ACKNOWLEDGEMENTS
To Laboratorios Fares Taie (Mar del Plata). This work
was financed for El Concejo Interuniversitario Nacional
(CIN) and for PICT 1625 for LB.
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Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 127–135
DOI: 10.1515/pjfns-2015-0019
http://journal.pan.olsztyn.pl
Original article
Section: Food Chemistry
Effects of Honey Addition on Antioxidative Properties of Different Herbal Teas
Gamze Toydemir1, Esra Capanoglu2*, Senem Kamiloglu2,
Ebru Firatligil-Durmus2, Asli E. Sunay3, Taylan Samanci3, Dilek Boyacioglu2,3
Department of Food Engineering, Faculty of Engineering & Architecture, Okan University, Akfirat-Tuzla,
34959, Istanbul, Turkey
2
Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University,
34469 Maslak, Istanbul, Turkey
3
Scientific Bio Solutions LLC., Maslak, Istanbul, Turkey
1
Key words: herbal tea, honey, temperature, phenolics, flavonoids, antioxidant capacity
Tea and herbal infusions are among the major contributors of phenolic compounds, specifically flavonoids, in our daily diet. Honey is another
antioxidant-rich food that is widely used as a natural sweetener. In this work, the effects of honey addition on antioxidant properties of different herbal
teas were investigated. For this purpose, 2 different types of honey (flower and pine honey) were added into 9 different herbal teas (melissa, green tea,
rosehip, sage, echinacea, fennel, linden, daisy, and ginger) at 4 different temperatures (55˚C, 65˚C, 75˚C, and 85˚C), and the changes in the content of total pheolics, total flavonoids, and total antioxidant capacity were determined. The total phenolic content and the total antioxidant capacity
of the honey-added-tea samples were found to be increased (up to 57% for both), especially with pine honey and at higher temperatures of honey
addition. The findings of this study supported the use of honey as a natural sweetener in tea in order to be able to benefit from the health-enhancing
antioxidative properties of these two promising food products.
INTRODUCTION
Tea and herbal infusions, which are popular, socially accepted, and economical, drinks [Trevisanato & Young-In
Kim, 2000], can be prepared from any part of various plants,
i.e. roots, flowers, seeds, berries, or bark, depending on
the solubility of the active constituents included [Apak et al.,
2006]. It is well-documented that these infusions, prepared
from valuable parts of herbs, are among the major contributors of phenolics in our diet [Shahidi, 2000]. Flavonoids, as
the leading polyphenol group present in herbs, have been indicated to provide protection against several forms of cancer
and cardiovascular diseases, as well as enhance the function
of the immune system [Craig, 1999]. Brewing tea leaves in hot
water has been reported to release 69–85% of the bioactive
flavonoids within 3–5 minutes [Keli et al., 1996] which contributes to the intake of 80 mg flavonoids per 100 mL of tea
consumption [van Dokkum et al., 2008].
The majority of the plant materials, that include phytochemicals possessing health-promoting antioxidant activity, are also used by the bees to collect honey nectar, leading to the transfer of these bioactive components into honey
[The National Honey Board, 2002]. Honey is a natural sweet* Corresponding Author: Tel: +90 212 2857340; Fax: +90 212 2857333
E-mail: [email protected] (E. Capanoglu)
ener produced by honeybees from the nectar of blossoms
(floral (nectar) honey) and from secretions of living parts
of plants or excretions of plant-sucking insects on the living
part of plants (honeydew honey) [Persano Oddo et al., 2004].
Honey is reported to be an important source of antioxidants,
including flavonoids, phenolic acids, carotenoid derivatives,
organic acids, Maillard reaction products, etc. [Gheldof
& Engeseth, 2002; Aljadi & Kamaruddin, 2004]. Various literature studies pointed out the antimicrobial [Alvarez-Suarez
et al., 2013; Al-Waili et al., 2011; Alzahrani et al., 2012; Chang
et al., 2011; Israili, 2014], antioxidant [Alvarez-Suarez et al.,
2012a, b; Alzahrani et al., 2012], antiinflammatory, and antitumoral properties [Alvarez-Suarez et al., 2013] of honey,
as well as its potential use in combination with conventional
therapy as a novel antioxidant in the management of chronic
diseases that are mostly related to the oxidative stress [Erejuwa et al., 2012].
The use of honey can be suggested to sweeten tea as
a healthier way of tea consumption with the preferred sweet
taste. However, based on our current literature search, there
is no data on how the antioxidant potential of herbal infusions is affected by the addition of honey. Therefore, the aim
of the present work was to determine and compare the influences of flower honey (nectar honey) and pine honey (honeydew honey) addition on the total phenolic and total flavonoid
contents, as well as total antioxidant capacities of different
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
128
Antioxidative Properties of Honey-Added Herbal Teas
herbal tea samples. In addition, the effect of the infusion temperature, at which honey was added, was also investigated.
MATERIALS AND METHODS
Honey and herbal tea samples
Flower honey (from Marmaris, Mugla region of Turkey)
and pine honey (from Eastern Anatolia region of Turkey)
samples were collected, in duplicates in 2013, directly from
beekeepers in Turkey, and tested for their total phenolic contents and total antioxidant activities before use. In order to
establish the botanical origins of honey (floral, pine) samples,
microscopic analysis, pollen and spore determination, conductivity, acidity, humidity, diastase and sugar profile analysis were performed along with sensory testing. Nectar honey
samples were multifloral. Herbal tea samples, including melissa, green tea, rosehip, sage, echinacea, fennel, linden, daisy, and ginger teas, were supplied from a tea manufacturer
in Turkey in the form of tea bags.
Sample/extract preparation
Herbal tea infusions were prepared by adding 200 mL
of freshly boiled deionised water on a tea bag (2 g), and brewing for 3 min (taking the instructions on the package into
consideration) without additional heating. Tea bags were removed and subsequently, flower honey or pine honey samples
were added to these herbal tea infusions at 85˚C, 75˚C, 65˚C,
and 55˚C of infusion temperatures, measured using a thermometer (ISOLAB Laborgerate GmbH, Germany), and at
a concentration of 7.5 g honey/100 mL tea. Infusions without
any honey addition were used as controls. For both the honey-added extracts and the controls, the analyses of total phenolics, flavonoids and antioxidant capacity were conducted after
cooling the samples to room temperature. All honey-added
and control infusions were prepared in triplicates.
Analytical protocols
Hydroxymethylfurfural (HMF) content of honey samples was determined using the spectrophotometric method
described in Turkish Honey Standard [TS 3036, 2002].
The method was based on the colorimetric reaction among
p-toluidine, barbituric acid and HMF forming a red colored complex. The absorbance was measured at 550 nm
and the HMF was quantified using the following formula:
HMF(mg/kg) = A550 × 192
where A550 is the absorbance measured at 550 nm and 192
is a theoretical value linked to the molar extinction coefficient
of HMF.
Total phenolic (TP) content was determined according to
the Folin-Ciocalteau method described previously by Velioglu
et al. [1998]. In brief, 0.1 mL of sample was added to 0.75 mL
of Folin-Ciocalteau reagent. The mixture was allowed to
stand for 5 min and then 0.75 mL of 6% sodium carbonate
solution was added to the mixture. After 2 h of incubation at
room temperature, absorbance was read at 725 nm. The results were expressed as mg gallic acid equivalent (GAE)/L tea.
Total flavonoid (TF) content was measured using the colorimetric assay developed by Zhishen et al. [1999]. At time
zero, 1 mL of sample was mixed with 0.3 mL of 5% NaNO2
solution. After 5 min, 0.3 mL of 10% AlCl3 was added. At
the 6th min, 2 mL of 1 mol/L NaOH was added to the mixture.
Immediately, 2.4 mL of distilled water was added and the absorbance was read at 510 nm. The results were given as mg
catechin equivalent (CE)/ L tea.
Total antioxidant capacity (TAC) was estimated using two
in vitro tests in parallel. The DPPH (1,1-diphenyl-2-picrylhydrazyl) method was performed as described by Kumaran
& Karunakaran [2006]. 0.1 mL of each sample extract was
mixed with 2 mL of 0.1 mmol/L DPPH in methanol. After
30 min of incubation at room temperature, the absorbance
of the mixture was measured at 517 nm. The CUPRAC (Cupric Reducing Antioxidant Capacity) method was applied using the protocol reported by Apak et al. [2004]. 0.1 mL of extract was mixed with 1 mL of 10 mmol/L CuCl2, 7.5 mmol/L
neocuproine and 1 mol/L NH4Ac (pH:7). Immediately, 1 mL
of distilled water was added to the mixture to make the final
volume of 4.1 mL. After 60 min of incubation at room temperature, absorbance was read at 450 nm. The results were
given as Trolox (6-hydroxy- 2,5,7,8-tetramethylchroman2-carboxylic acid) equivalent (TE)/ L tea.
Statistical analysis
Statistical analysis was applied to the data obtained from
the samples that were subjected to each assay in triplicates.
Minitab software (version 16.1.0) was used for the one-way
ANOVA and pairwise comparisons between the treatments
(honey varieties and temperatures) were done using Tukey
test with a 95% confidence level. The correlation coefficients
(R2) for results of the two spectrophotometric assays were
calculated using Microsoft Office Excel 2011 software (Microsoft Corporation, Redmond, WA, US).
RESULTS
Hydroxymethylfurfural (HMF) content of honey samples
The HMF contents of the flower and pine honey samples were determined to check for an acceptable quality
of the honey samples that were subjected to high temperatures. The maximum value for HMF content of honey after
processing and/or blending, is fixed as 40 mg/kg by the Codex
standard [Codex Stan 12–1981, Rev 2 2001]. The concentrations found in the current study were very low (below the Codex limit) both in flower and pine honey samples, ranging
between 4.6–8.1 mg/kg.
Changes in total phenolic and total flavonoid contents
of tea samples added-with-honey
The results obtained for TP and TF contents were represented in Table 1. Melissa, green tea, and rosehip were the first
three teas determined to have the highest TP contents (549,
465, and 397 mg GAE/L tea, respectively) than their controls,
followed by sage, echinacea, fennel, linden, daisy, and ginger,
respectively (71–268 mg GAE/L tea). The flower honey led to
significant increases (p<0.05) in TP content of sage tea at all
129
G. Toydemir et al.
TABLE 1. The changes in total phenolic and total flavonoid contents of 9 different herbal teas with flower honey and pine honey addition at 4 different
tea temperatures*
Honey–temperature
Total phenolic content (mg GAE/L)
Melissa
Green tea
Rosehip
Sage
Echinacea
Fennel
Linden
Daisy
Ginger
Control
549±70a
465±42b
397±63b
268±28b
266±61c
123±21b
86±7b
73±17b
71±9cd
Flower-55˚C
596±41a
533±78ab
493±43ab
343±26a
389±85ab
163±27ab
134±29ab
55±13b
58±2d
Flower-65˚C
584±96a
543±36ab
521±54a
327±23a
449±101a
152±38ab
116±10ab
58±17b
60±2d
Flower-75˚C
601±53a
547±68ab
525±61a
343±19a
361±64abc
174±28a
107±27ab
60±25b
57±4d
Flower-85˚C
600±65a
563±63a
436±77ab
333±23a
378±63ab
166±33ab
144±16a
73±25b
93±10bc
Pine-55˚C
659±101a
544±61ab
530±59a
336±8a
291±61bc
155±18ab
135±15a
134±33a
127±25a
Pine-65˚C
680±111a
544±37ab
476±108ab
336±17a
279±51bc
145±38ab
140±16a
132±28a
128±11a
Pine-75˚C
640±57a
552±21a
460±56ab
313±23a
293±65bc
134±22ab
136±32a
141±28a
111±16ab
Pine-85˚C
584±72a
553±32a
417±68ab
340±12a
308±53bc
160±34ab
152±33a
138±22a
132±11a
Honey–temperature
Total flavonoid content (mg CE/L)
Melissa
Green tea
Rosehip
Sage
Echinacea
Fennel
Linden
Daisy
Ginger
Control
3705±170c
726±40a
962±88a
1421±121b
799±87a
219±35b
179±29c
145±7de
323±43a
Flower-55˚C
4104±135ab
550±40de
743±74cd
1675±122ab
627±75bcd
157±9c
181±23bc
131±11e
249±10bcd
Flower-65˚C
4018±72abc
541±63e
702±51d
1583±74ab
644±66bcd
162±9c
200±31abc
145±7cde
258±16bcd
Flower-75˚C
3876±156bc
571±51cde
803±97bcd
1703±53a
575±103d
166±17c
216±34abc
146±8cde
244±7cd
Flower-85˚C
3858±447bc
563±64cde
686±76d
1650±279ab
623±99cd
164±6c
181±20bc
154±11bcd
223±17d
Pine-55˚C
4128±107ab
629±28bcd
846±48abc
1768±161a
780±79ab
240±25ab
220±32abc
175±12a
293±23abc
Pine-65˚C
4271±262a
637±22bc
900±58ab
1851±203a
762±84abc
262±12a
262±49a
170±13ab
290±46abc
Pine-75˚C
4171±129ab
682±29ab
880±87abc
1564±197ab
759±81abc
245±22ab
262±44a
162±9abc
308±34ab
Pine-85˚C
3977±211abc
658±22ab
786±36bcd
1756±141a
777±46abc
235±22ab
243±44ab
162±8abc
304±48abc
* Data represent average values ± standard deviation of three independent samples. Different letters in the columns represent statistically significant
differences (p<0.05). Control samples were tea samples with no added-honey. GAE: gallic acid equivalent; CE: catechin equivalent.
infusion temperatures of honey addition. On the other hand,
flower honey-added green tea and linden tea gave significantly higher (p<0.05) values at 85˚C, rosehip tea at 65˚C
and 75˚C, echinacea tea at 55˚C, 65˚C, and 85˚C, and fennel tea at 75˚C of honey addition temperatures. Flower honey
did not result in any significant changes (p>0.05) in TP content of melissa, daisy, and ginger tea samples.
The pine honey addition into sage, linden, daisy, and ginger tea resulted in significantly higher (p<0.05) TP contents,
in comparison to their controls, at all temperatures. While significant increases were obtained in TP contents of green tea at
75˚C and 85˚C and rosehip tea at 55˚C, pine honey addition
did not make a significant change in TP contents of melissa,
echinacea, and fennel tea samples, at any temperature.
The TF contents of melissa, sage, and rosehip tea were
found to be the highest among the analysed tea samples
(3705, 1421, and 962 mg CE/L tea, respectively), followed
by echinacea, green tea, ginger, fennel, linden, and daisy
teas, respectively (145–799 mg CE/L tea). The TF content
results obtained for the flower honey addition indicated significant increases (p<0.05) in TF content of melissa tea at
55˚C and sage tea at 75˚C, only. On the other hand, flower
honey-added green tea, rosehip, echinacea, fennel, and ginger tea samples were determined to have significantly reduced
(p<0.05) TF contents at any temperature of honey addition.
TF contents of daisy and linden tea did not change significantly with flower honey.
The TF content measurements for pine honey-added tea
samples revealed that pine honey addition resulted in significant increases (p<0.05) in TF contents of melissa tea at
55˚C, 65˚C, and 75˚C, sage tea at 55˚C, 65˚C, and 85˚C,
fennel tea at 65˚C, linden tea at 65˚C, 75˚C, and 85˚C,
and daisy tea at all 4 infusion temperatures of honey addition. On the other hand, significant reductions were obtained,
with pine honey addition, in green tea at 65˚C and in rosehip
tea at 85˚C. TF contents of echinacea and ginger tea were not
affected significantly with the addition of pine honey.
Changes in total antioxidant capacity of tea samples
added-with-honey
The changes in total antioxidant capacity (TAC) values
of different tea samples, with flower honey and pine honey ad-
130
Antioxidative Properties of Honey-Added Herbal Teas
TABLE 2. The changes in total antioxidant capacity of 9 different herbal teas with flower honey and pine honey addition at 4 different tea temperatures.
Honey–temperature
Total antioxidant capacity, DPPH Method (mg TE/L)
Melissa
Green tea
Rosehip
Sage
Echinacea
Fennel
Linden
Daisy
Ginger
Control
1111±179ab
962±162b
684±113a
441±29b
273±51abc
78±22b
64±13c
43±12c
100±15b
Flower-55˚C
989±114ab
1069±207ab
613±137a
522±67ab
261±28abc
82±8ab
105±14ab
23±9c
133±32ab
Flower-65˚C
975±123ab
1106±121ab
629±135a
541±21ab
220±13c
87±14ab
97±12ab
33±9c
125±19ab
Flower-75˚C
940±126b
1100±68ab
637±140a
545±59ab
220±13bc
97±13ab
89±14abc
44±10bc
157±17a
Flower-85˚C
937±94b
1181±181a
586±119a
642±72a
231±24bc
77±8b
76±9bc
37±9c
124±16ab
Pine-55˚C
1058±157ab
1131±43ab
594±32a
544±51ab
316±28ab
107±9a
85±20abc
60±5ab
98±15b
Pine-65˚C
1102±132ab
1079±30ab
584±65a
575±46a
348±80a
98±13ab
102±11ab
61±4ab
100±17b
Pine-75˚C
1179±130ab
1114±81ab
604±47a
527±48ab
313±54ab
107±11a
110±34a
70±6a
106±19b
Pine-85˚C
1221±124a
1055±76ab
519±56a
544±58ab
287±73abc
104±16a
89±21abc
63±7ab
102±17b
Honey–temperature
Total antioxidant capacity, CUPRAC Method (mg TE/L)
Melissa
Green tea
Rosehip
Sage
Echinacea
Fennel
Linden
Daisy
Ginger
Control
2212±84
1813±277
1424±182
911±126
610±48
238±34
215±21
219±40
256±47b
Flower-55˚C
2300±105a
1859±176ab
1484±82ab
1202±84a
660±35ab
299±24a
283±41ab
265±57abc
276±26ab
Flower-65˚C
2390±78a
1895±281ab
1428±96ab
1119±100a
667±57ab
294±41ab
281±27ab
267±49abc
280±29ab
Flower-75˚C
2262±106a
1852±104ab
1611±107a
1217±67a
661±67ab
358±23a
252±24bc
266±80abc
276±49ab
Flower-85˚C
2329±28a
1810±314ab
1326±100ab
1155±112a
674±39ab
308±34a
272±26abc
251±53bc
271±27ab
Pine-55˚C
2161±285ab
1929±188ab
1382±124ab
1167±78a
730±29a
339±39a
322±51a
339±18a
314±45ab
Pine-65˚C
2145±171ab
1892±166ab
1428±260ab
1142±111a
706±67a
345±41a
339±54a
309±25ab
297±42ab
Pine-75˚C
2155±202ab
2067±70ab
1389±176ab
1103±123a
702±38a
333±49a
337±49a
301±26ab
339±44a
Pine-85˚C
1956±224b
2199±103a
1214±242b
1128±85a
729±29a
303±30a
302±34ab
278±21abc
296±62ab
a
b
ab
b
b
b
c
c
* Data represent average values ± standard deviation of three independent samples. Different letters in the columns represent statistically significant
differences (p<0.05). Control samples were tea samples with no added-honey. DPPH: 1,1-diphenyl-2-picrylhydrazyl; Cupric Reducing Antioxidant
Capacity; TE: Trolox (6-hydroxy- 2,5,7,8-tetramethylchroman-2-carboxylic acid) equivalent.
dition at 4 different infusion temperatures (55˚C, 65˚C, 75˚C,
and 85˚C), were determined using 2 in vitro tests in parallel, which
were DPPH and CUPRAC methods (Table 2). The highest TAC
values, determined with DPPH method, were measured for melissa, green tea, and rosehip tea samples (1111, 962, and 684 mg
TE/L, respectively), followed by sage, echinacea, ginger, fennel,
linden, and daisy tea samples, respectively (43–441 mg TE/L)
(Table 2). The results of DPPH method indicated that flower
honey led to a significant increase (p<0.05) in TAC of green tea
and sage tea at 85˚C, linden tea at 55˚C and 65˚C, and ginger
tea at 75˚C. There was no change observed in TAC of flower
honey-added melissa, rosehip, echinacea, fennel, and daisy tea
samples, in comparison to their control infusions.
The differences in TAC of pine-honey added tea infusions,
measured using DPPH method, indicated significantly higher
values (p<0.05) in pine honey-added sage tea at 65˚C, fennel
tea at 55˚C, 75˚C, and 85˚C, linden tea at 65˚C and 75˚C,
and daisy tea at all four temperatures of honey addition,
in comparison to their control samples. Whereas, the TAC
of melissa, green tea, rosehip, echinacea, and ginger tea did not
show any significant change with the inclusion of pine honey.
The highest TAC values, determined using CUPRAC
method, were again in melissa, green tea, and rosehip tea
(2212, 1813, and 1424 mg TE/L), followed by sage, echinacea, ginger, fennel, daisy, and linden, respectively (215–
911 mg TE/L). Flower honey increased the TAC of sage tea
significantly (p<0.05) at all infusion temperatures. In addition, substantial increases were also obtained for flower honey-added fennel tea at 55˚C, 75˚C, and 85˚C, and linden tea
at 55˚C and 65˚C. The TAC values of the remaining 6 tea
samples, analysed with CUPRAC method, were not found to
be affected significantly with flower honey addition at different temperatures.
Pine honey-added sage, echinacea, fennel, and linden
tea samples, analysed using CUPRAC method, were found
to have significantly higher (p<0.05) TAC values at all
temperatures of honey addition, compared to their control
samples. Moreover, pine honey also provided significant increases in TAC of green tea at 85˚C, daisy tea at 55˚C, 65˚C,
and 75˚C, and ginger tea at 75˚C. The TAC of rosehip tea
did not change significantly with the inclusion of pine honey
at 4 different infusion temperatures, whereas melissa tea was
G. Toydemir et al.
measured to have reduced TAC values when pine honey was
added at 85˚C.
The correlations between spectrophotometric assay results
The linear correlation coefficients (R2) were calculated
for plots of TP versus TF, TP versus DPPH, TP versus CUPRAC, TF versus DPPH, TF versus CUPRAC, and DPPH
versus CUPRAC assay results. The lowest correlation was
observed between TP and TF content values (R2=0.30118),
while a highly linear relationship was determined between
the results of DPPH and CUPRAC methods (R2=0.90073)
(Figure 2A). Additionally, the linear curves obtained for CUPRAC versus TP (R2=0.73632) (Figure 2B) and CUPRAC
versus TF (R2=0.54313) (Figure 2C) results had higher correlation coefficients than those observed for DPPH versus TP
(R2=0.70238) and DPPH versus TF (R2=0.44324) results.
DISCUSSION
The effect of different types of honey
Flower honey and pine honey addition either led to significant increases (p<0.05) or did not significantly change the TP
contents of the honey-added tea samples compared to their
controls. Flower honey provided a significantly higher (p<0.05)
TP content value in echinacea tea at 65˚C in comparison to
the value obtained with pine honey addition at the same temperature. On the other hand, pine honey-added daisy and ginger
teas were measured to be significantly higher in their TP contents, at all temperatures of honey addition, compared to their
flower-honey added counterparts (Table 1). It could be linked
to the fact that honeys with darker color, as in the case of pine
honey, have been reported to possess higher amounts of total
phenolic compounds in recent research studies [Alvarez-Suarez
et al., 2010; Escuredo et al., 2013; Kus et al., 2014 Wilczynska,
2010]. Kus et al. [2014] determined the TP contents of lighter
honeys investigated in their study (black locust, goldenrod,
rapeseed, and lime) to range in between 142.8–192.5 mg GAE/
kg; while this range for darker honeys (heather and buckwheat)
was found to be from 306.2 to 1113.0 mg GAE/kg. In another
study, the highest TP contents were again measured for darker
colored honeys, including chestnut honey (1313 mg GAE/kg)
and heather honey (1789 mg GAE/kg) [Escuredo et al., 2013].
The TP contents of the flower and pine honeys that we used
in our work were 510 and 680 mg GAE/kg, respectively.
The reason for not obtaining the same effect of pine honey
for all the analysed tea samples could be related with the different phenolic profiles of the herbal tea samples or the lack
of the specificity of the Folin-Ciocalteau method for phenolic
compounds [Capanoglu et al., 2008]. The Folin-Ciocalteau
method was reported to be suffering from a number of interfering substances, including specifically sugars, aromatic
amines, ascorbic acid [Box, 1983], and amino acids and proteins [Meda et al., 2005] that can also react with Folin-Ciocalteau reagent. Thus, it was strongly suggested that corrections
for those interfering substances should be made in order to
establish a uniformly acceptable method of TP to compare
the obtained results rationally [Prior et al., 2005].
The addition of pine and flower honey, at 4 infusion temperatures, was determined to affect the TF contents of differ-
131
ent tea samples in different ways, including the effects of all
significant increases/decreases or not any significant changes
(p<0.05). It was remarkable that flower honey addition led
to significant decreases (p<0.05) in TF contents of five (out
of nine) tea samples, including green tea, rosehip, echinacea,
fennel, and ginger, at all temperatures of honey addition.
On the other hand, pine honey addition did not significantly
change or even increased the TF contents of tea samples (except for green tea at 55˚C and 65˚C, and rosehip tea at 85˚C)
(Table 1). Silici et al. [2013] reported catechin and epicatechin as the only compounds that were determined as the kind
of flavonoids in honeydew honey samples, which were also
determined to constitute the largest content (53% of detected
total phenolics) of total phenolics in the analysed honeydew
honey samples. On the other hand, the contribution of catechin and epicatechin components to the TP content of nectar
honeys was found to be 33% of the detected phenolics [Silici
et al., 2013]. This could have an influence on these higher TF
contents of pine honey-added-tea samples in our study, since
the results for TF content analysis have been expressed as catechin equivalents (Table 1).
The results obtained by DPPH method, for the changes
in TAC of different tea samples added-with-flower honey revealed significant increases (p<0.05) in TAC of green tea (at
85˚C), sage tea (at 85˚C), linden tea (at 55˚C and 65˚C),
and ginger tea (at 75˚C). On the other hand, again with
the same method pine honey was observed to lead to significant increases (p<0.05) in TAC of sage tea (at 65˚C), fennel
tea (at 55˚C, 75˚C, and 85˚C), linden tea (at 65˚C and 75˚C)
and daisy tea (at all temperatures). When the flower honey
and pine honey were compared for their influences on TAC,
at the same temperature of honey addition, pine honey was
found to differ from flower honey with its significantly higher
(p<0.05) contribution to the TAC of echinacea tea (at 65˚C),
fennel tea (at 85˚C), and daisy tea (at all 4 temperatures). For
the other tea samples, pine honey and flower honey did not
significantly differ (p>0.05) (Table 2).
The TAC values measured with CUPRAC method indicated significantly increased (p<0.05) TAC by the effect
of pine honey addition in five (out of nine) tea samples,
including sage, echinacea, fennel, linden, and daisy, independent from the infusion temperatures tested (except for
the daisy tea at 85˚C). Flower honey was found to contribute
significantly to the TAC of sage tea (at all temperatures), fennel tea (at 55˚C, 75˚C, and 85˚C), and linden tea (at 55˚C
and 65˚C). In addition, the comparison of the flower honey-added and pine honey-added tea samples, at the same temperature of honey addition, revealed no significant differences
regarding their TAC measured by CUPRAC method (except
for melissa tea at 85˚C and linden tea at 75˚C). On the other
hand, pine honey had a greater contribution to the TAC values of tea samples in comparison to their respective control
tea samples (Table 2). These relatively higher TAC values provided by pine honey could be explained based on the findings
of other studies, which have pointed out that honey samples
that are darker in their color have higher antioxidant capacities in general [Alvarez-Suarez et al., 2010; Kus et al., 2014;
Wilczynska, 2010] since honey color depends on the potential
alkalinity and ash content, as well as on the antioxidatively
132
The effect of different infusion temperatures of honey
addition
The results obtained for TP contents of honey-added-tea
samples pointed out that the highest values, although not all
were statistically different from control samples, were generally obtained with the addition of flower/pine honey at infusion temperatures of 75˚C and 85˚C (except for echinacea tea
for flower honey addition, and melissa and rosehip teas for
pine honey addition). The TF contents of flower honey-added-tea samples were again mainly higher at 75˚C and 85˚C
of honey addition temperatures compared to the other infusion temperatures of honey addition. However, it should
be emphasized that these relatively higher values obtained
at 75˚C/85˚C, in comparison to the other infusion temperatures, of flower honey addition were mostly significantly
lower or were not significantly different from the respective
control tea samples (Table 1). On the other hand, when pine
honey was added into tea samples at 65˚C and 75˚C of infusion temperatures, it provided relatively higher TF contents
in comparison to the other infusion temperatures of honey
addition. Whereas some of these TF content values, obtained
for 65˚C/75˚C of pine honey addition temperatures, were
still lower than the values obtained for respective control tea
samples (including green tea, rosehip, and ginger tea samples) (Table 1).
The TAC of flower/pine honey added-tea samples, determined using DPPH method, were again found to be higher
at 75˚C and 85˚C of honey addition temperatures compared to the other infusion temperatures of honey addition.
On the other hand, the measurement of TAC values with
CUPRAC method gave higher values at 65˚C and 75˚C
of infusion temperatures for flower honey addition, and at
55˚C and 65˚C of infusion temperatures for pine honey addition (Table 2).
When all the results were evaluated in general, it could
be concluded that the addition of flower/pine honey into different tea samples at 75˚C and (to a lesser extent) at 85˚C
gave relatively high values of TP and TF contents, as well
as TAC, in comparison to the other tested temperatures.
The percent changes in TP content (Figure 1A) and TAC
values, determined using CUPRAC method (Figure 1B),
obtained with flower/pine honey addition at 75˚C are given
in Figure 1 as the representative graphs. The flower and pine
honey additions into tea samples at 75˚C were determined
to lead up to 41% and 57% increases in TP contents (Figure
1A), and up to 50% and 57% increases in TAC values, determined using CUPRAC method (Figure 1B), respectively.
These higher values at higher temperatures may depend on
the formation of Maillard reaction products, melanoidins,
which have been reported to act as antioxidants [Brudzynski
160
% change, TP
100
ab a
a
a
ab
a
ab
bc
a
a
b
b
ab
a
abc
a
120
a
a
A
140
b
c
ab
b
b
b
cd
b
d
daisy
ginger
80
60
40
20
0
melissa
160
greentea
roschip
sage
echinasea
fennel
linden
a
B
a
a
140
ab
a
ab
a
120
ab
a
abc
a
a
ab
ab
% change, CUPRAC
active pigments, such as carotenoids and flavonoids [Frankel
et al., 1998]. Alvarez-Suarez et al. [2010] reported the TAC
values of honeys tested in their study to range in between
1035 and 2945 μmol TE/kg which was linearly correlated
with the color range of the honeys changing from light to amber. Accordingly, the TAC of the darker-colored pine honey
(4075 mg TE/kg), used in this work, was higher in comparison to the TAC of the lighter flower honey (3545 mg TE/kg).
Antioxidative Properties of Honey-Added Herbal Teas
100
a a ab
b ab
melissa
greentea
ab
ab
b
b
b
b
c
b
80
60
40
20
0
control
roschip
sage
echinasea
fennel
flower honey-added
linden
daisy
ginger
pine honey-added
FIGURE 1. The percent changes in (A) Total phenolic (TP) contents
and (B) Total antioxidant capacity (TAC) values, determined using Cupric Reducing Antioxidant Capacity (CUPRAC) assay, of the analysed
tea samples with flower and pine honey addition at 75˚C. Different letters on the columns represent the statistically significant (p<0.05) differences observed with flower or pine honey addition into a herbal infusion
at 75˚C. (See Table 1 (for TP content data) and Table 2 (for TAC data
obtained via CUPRAC method) for the complete statistical data).
& Miotto, 2011a,b,c; Turkmen et al., 2006]. Turkmen et al.
[2006], who studied the effect of heating honey to 50˚C,
60˚C, and 70˚C on the antioxidant activity and brown pigment formation due to Maillard reaction, determined that
both of the measured values increased with the increased
temperature. The authors evaluated that the increase
in brown pigment formation, due to the formation of Maillard reaction products, was accompanied with the increase
in antioxidant activity, which was more remarkable in heated honey samples at 70˚C than those at 50˚C and 60˚C
[Turkmen et al., 2006]. In addition, these Maillard reaction
products were also reported to react with Folin-Ciocalteau reagent [Verzelloni et al., 2007] which could explain
the higher TP content values in honey added tea samples,
specifically at higher temperatures of honey addition. In another study, Brudzynski & Miotto [2011a] hypothesized
that phenolics in honey may be components of melanoidin
structure, and they tested the melanoidin fractions of unheated and heat-treated honey samples for their total phe-
133
G. Toydemir et al.
3000
nioti & Zervalaki, 2001; Fallico et al., 2004]. HMF formation in honey could be influenced by the chemical properties of honey, including pH, total acidity, mineral content,
etc., which are dependent on the floral source from which
the honey sample has been extracted [Anam & Dart, 1995;
Bath & Singh, 1999]. So, the inclusion of honey samples
obtained from different floral sources can provide different
levels of HMF contents.
A
CUPRAC (mg TE/L)
2500
2000
1500
1000
500
R2 = 0.9007
0
0
500
1000
1500
2000
DPPH (mg TE/L)
3000
B
CUPRAC (mg TE/L)
2500
2000
1500
1000
500
R2 = 0.7363
0
0
200
400
600
800
1000
TP (mg GAE/L)
3000
C
CUPRAC (mg TE/L)
2500
2000
1500
1000
500
R2 = 0.5431
0
0
1000
2000
3000
4000
5000
TF (mg CE/L)
FIGURE 2. The linear correlation coefficients (R2) calculated for plots
of (A) 1,1-diphenyl-2-picrylhydrazyl (DPPH) versus Cupric Reducing Antioxidant Capacity (CUPRAC), (B) Total phenolics (TP) versus
CUPRAC, and (C) Total flavonoids (TF) versus CUPRAC assay results.
nolic contents. Their results indicated a significant increase
in the TP content in melanoidin fractions of the heat-treated
honeys as compared to the TP content in melanoidin fractions of their unheated counterparts. This could also explain
the reaction between Maillard reaction products and the Folin-Ciocalteau reagent.
Because of the fact that heating of honey leads to
the formation of HMF (5-hydroxymethylfurfural), as a result of the hexose dehydration in acid media [Belitz & Grosch, 1999], we also checked the HMF contents of the honey
samples that were subjected to high temperatures in our
study, and confirmed that the HMF contents were all below
the limit value (40 mg/kg). HMF is considered as an important quality parameter for honey by means of evaluating
the freshness and the heating and storage history [Karabour-
The relationships between the results of the applied
spectrophotometric assays
The correlation coefficients (R2) calculated between
the applied spectrophotometric methods showed that the results of the CUPRAC assay correlated better with the TP
and TF contents of different herbal tea samples, compared
to the DPPH assay results. Besides, good correlations were
also observed between the results of DPPH and CUPRAC
assays (Figure 2). In accordance with our results, CUPRAC
method was proved to correlate well with ABTS and Folin-Ciocalteau assays in herbal plant infusions [Apak et al., 2006],
apricot [Guclu et al., 2006], and kiwifruit [Park et al., 2006]
extracts. Apak et al. [2006] reported the CUPRAC assay as
the most consistent method of total antioxidant measurement
in relation to Folin reagent-responsive TP content, since this
method is suitable for and reacts with a variety of antioxidant
compounds regardless of chemical type or hydrophilicity.
Additionally, the linear correlation determined between CUPRAC and ABTS assays (R2=0.8) has been linked to the fact
that these methods are similar electron transfer-based antioxidant assays [Apak et al., 2007], which can also be evaluated
for the high correlation found out between DPPH and CUPRAC assays (R2=0.90073) in this present work. However, it is worth to remark that although there are a number
of methods that have been developed to assess the antioxidant capacity of either pure antioxidant compounds or products containing complex mixture of antioxidants, there is still
lack of correlation between the results obtained for the same
compound/product by different assays, as well as by the same
assay in different laboratories [Niki, 2011].
On the other hand, lower correlation coefficients were obtained between TF assay results and the results of the other
three assays. Similarly, Park et al. [2006] and Meda et al.
[2005] reported low correlations between ABTS, CUPRAC
or TP content results and TF contents, which was linked to
the nature of the measurement technique used for total flavonoids. The aluminum chloride (AlCl3) colorimetric test used
for flavonoid analysis has been pointed out to be sensitive
only for flavonoid groups that possess the characteristic chelating functional groups for Al binding (i.e. flavones and flavonols), while this method does not measure the flavonoids
that do not include these functional groups (i.e. flavanones).
This leads to the underestimation of the TF content by using
this aluminum chloride method [Chang et al., 2002].
CONCLUSION
The comparison on the effect of flower and pine honey
addition into 9 different herbal tea samples at 4 different
temperatures revealed that the TP content and TAC values
134
of the honey-added-tea samples were generally higher than
those of the control tea samples, specifically with pine honey
addition and at higher temperatures. These findings support
the use of honey as a natural sweetener in tea drink in order
to be able to benefit from the health-enhancing antioxidative
properties of these two promising food products.
ACKNOWLEDGEMENTS
This project was financially supported by Istanbul Development Agency “Center of Excellence in Bee Products” project (Project Number: TR10/14/YEN/0028). Fatma Damla
Bilen, Diyar Selimoglu, Gulbeyaz Cevik, and Oznur Aydın
are acknowledged for analytical support.
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DOI: 10.1515/pjfns-2015-0006
http://journal.pan.olsztyn.pl
Original article
Section: Food Quality and Functionality
Influence of Sweetness and Ethanol Content on Mead Acceptability
Teresa Gomes, Teresa Dias, Vasco Cadavez, João Verdial, Jorge Sá Morais, Elsa Ramalhosa*, Letícia M. Estevinho
Mountain Research Centre (CIMO), ESA - Polytechnic Institute of Bragança, Campus
de Stª Apolónia, Apartado 1172, 5301–855 Bragança, Bragança, Portugal
Key words: mead, sweetness, ethanol content, fermentations, sensory analysis
Mead is a traditional alcoholic beverage obtained by fermenting mead wort; however, its production still remains frequently an empirical exercise.
Different meads can be produced, depending on fermentation conditions. Nevertheless, to date few studies have been developed on factors that may
influence mead quality. The main objective of this work was to study the influence of sweetness and ethanol content on mead acceptability. Different
meads were produced with two sweetness levels (sweet and dry meads) and three ethanol contents (18, 20, 22% (v/v)), adjusted by brandy addition.
Afterwards, meads acceptability was evaluated by sensory analysis through a consumers’ panel (n=108) along with chemical analysis by HPLC-RID
of glucose, fructose, ethanol, glycerol and acetic acid.
The sweet (75 gglucose+fructose/L) and dry (23 gglucose+fructose/L) meads presented glycerol contents equal to 5.10±0.54 and 5.96±0.95 g/L, respectively,
that were desirable since glycerol improves mead quality. Low concentrations of acetic acid were determined (0.46±0.08 and 0.57±0.09 g/L), avoiding
the vinegar off-character. Concerning sensory analysis, the alcohol content of mead had no effect on the sensory attributes studied, namely, aroma,
sweetness, flavour, alcohol feeling and general appreciation. Regarding sweetness, the “sweet meads” were the most appreciated by the consumers
(score of 5.4±2.56), whereas the “dry meads” (score of 2.7±2.23) showed low acceptability. In conclusion, this work revealed that sweetness is a sensory key attribute for mead acceptance by the consumers, whereas ethanol content (18 to 22% (v/v)) is not.
INTRODUCTION
Beekeeping is an important income-generating activity
in several countries. Portugal is no exception. In rural communities of NE of Portugal, honey production is of great importance; however, sometimes local beekeepers face problems
during the sale of their productions due to the low prices practised in international markets. Thus, the development of new
honey products can contribute to overcome this problem, to
strengthen the local economy and to increase honey production activity competiveness.
Mead is a traditional alcoholic beverage obtained by fermenting mead wort and can represent a good solution to honey over-production and a way of valorising honey of lower
quality. Its production has been known since ancient times;
however, it still remains frequently an empirical and traditional exercise. Some problems are often encountered during
mead production, such as: delayed or arrested fermentations,
production of unpleasant flavors, poor quality and inconsistency of the final product [Attfield, 1997; Bisson, 1999]. These
problems are due to the high sugar and low nutrient contents
of honey; its natural antifungal components; and the inability
of yeast strains to adapt to these unfavourable growth conditions [Roldán et al., 2011].
* Corresponding Author: E-mail: [email protected] (Elsa Cristina Dantas
Ramalhosa, Ph.D.)
In order to overcome some of these fermentation problems, several research studies have been conducted by our research group focusing on the influence of mead wort composition [Pereira et al., 2009] and the effect of production scale
and operational conditions on final product quality [Gomes,
2010; Gomes et al., 2010, 2011].
The role of using different Saccharomyces strains [Caridi et al., 1999] and types of honey [Vidrih & Hribar, 2007]
on mead production, immobilized ethanol-tolerant yeasts
[Navratil et al., 2001], must formulation [Mendes-Ferreira
et al., 2010], different heat treatments of honey solutions
[Kime et al., 1991a], application of ultra-filtration of honey
solution [Kime et al., 1991b], and inoculum size and yeast
pitching rate [Pereira et al., 2013], as well as the addition
of black rice grains [Katoh et al., 2011] or pollen [Roldán
et al., 2011], on mead quality and aroma profile have already
been studied by other research groups.
Concerning sensory properties of mead, these are very
important for its acceptance by the final consumer. Until
now, few studies have been performed on this subject. Only
the effects of honey type [Vidrih & Hribar, 2007; Gupta
& Sharma, 2009], heat treatment [Kime et al., 1991a], ultrafiltration [Kime et al., 1991b], pollen [Roldán et al., 2011]
and black rice grains [Katoh et al., 2011] addition on mead
sensory properties have been studied. Nevertheless, sweetness
and ethanol content of beverages are fundamental characteristics for their acceptability by the consumer. Smogrovicova
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
138
et al. [2012] verified that different meads have distinct residual
sugar contents, reporting that South African meads presented
a mean residual sugar content about 70 g/L, whereas higher
concentrations were determined in Slovak meads. Nevertheless, in that work no organoleptic studies were performed
and so until now nothing is known about the role of sweetness and ethanol content on mead acceptability. As fermentation time influences sugar content and alcohol content, longer
times will lead to fewer sugars and higher ethanol concentrations in the final product. Hence, fermentations performed
along different time periods will lead to products with different physico-chemical and sensory properties.
In spite of this, the main objective of this work was to
study the influence of sweetness and ethanol content on mead
acceptability. So, different meads were obtained by allowing
the fermentation process to take place along different times.
Then, the chemical and sensory characteristics of the meads
were evaluated, in order to get knowledge about the role
of sweetness and ethanol content on their acceptability.
MATERIALS AND METHODS
Reagents
All chemicals, namely glucose, fructose, saccharose, ethanol, glycerol, acetic acid and tartaric acid, were of analytical
grade and purchased from Sigma Chemical Co. (St. Louis,
MO, USA). Phosphoric acid was obtained from Fisher Scientific (Porto Salvo, Portugal). Type 2 deionised water was
obtained from a TGI pure water system (USA).
Honey and yeast strain
In this study, honey derived from plants of the Ericaceae
(heather) family (Erica spp.) purchased on the Honey House
of Trás-os-Montes region (NE of Portugal) was used in all
experiments. The yeast strain used was Saccharomyces cerevisiae, from Fermol® Reims Champagne (Pascal Biotech®,
Brescia, Italy), which is recommended for commercial production of white wines.
Fermentation conditions
The fermentation medium was prepared from honey diluted with water (395 g/L), supplemented with commercial
nutrients (90 g/hL) (Enovit®, Brescia, Italy) and 6% (v/v)
of SO2 (8 g/hL). The pH was corrected to 3.5 with tartaric
acid, as described by Gupta & Sharma [2009].
The honey mixtures were inoculated with freeze-dried
yeast cells (30 g/hL), previously hydrated in water with
the addition of saccharose (50 g/L) and incubated at 35ºC for
20 min. The fermentations occurred in cubes of 25 L, using
a working volume of 20 L, at 25ºC. All fermentations were
performed in triplicate. Density and Beaumé degrees were
measured by aerometry at regular intervals.
To produce the “sweet meads”, the fermentation process
was interrupted at 79 hours when the density was approximately 1060 g/mL by the addition of brandy with 77% (v/v)
of alcohol, using the procedure described by Pato [1982].
Mead sweetness was equivalent to 8 ºBeaumé. The mead
was divided and the alcohol content was adjusted to 18, 20
and 22% (v/v) with brandy. For production of “dry meads”,
Mead Characteristics Versus Its Acceptability
fermentations continued until reaching a density of about
1020 g/mL. At the end, brandy was added to obtain the alcoholic contents of 18, 20 and 22% (v/v).
Fermentation parameters analysed
Biomass was determined periodically by optical density at
640 nm (Jenway Genova®, Staffordshire, United Kingdom).
Glucose, fructose, ethanol, glycerol, and acetic acid were
quantified individually, following the methodology described
by Pereira et al. [2009] and using a Varian HPLC system (Agilent, Santa Clara, USA) equipped with a Rheodyne injector
with 20 μL loop, a Supelco Gel C-610H column (300×7.8 mm)
at 35ºC and a refractive index detector RI-4 (Varian, Agilent,
Santa Clara, USA). Isocratic elution was employed with a mobile phase consisting of 0.1% (v/v) phosphoric acid at a flow
rate of 0.5 mL/min. Data were recorded and integrated using
the Star Chromatography Workstation software (Varian, Agilent, Santa Clara, USA). Glucose, fructose, ethanol, glycerol
and acetic acid were quantified by external standard calibration.
Sensory analysis
The sensory attributes of meads (acceptability) were
evaluated by a consumers’ panel randomly selected among
the academic community of our Institution (IPB). Three testing sessions were organised, each one with 36 persons (total
= 108), in a sensory evaluation room equipped with individual cabins. The “sweet meads” and “dry meads”, varying
in the alcohol content (18, 20 and 22%, v/v) were tasted by all
consumers. The samples testing order were randomised in order to remove the effect of sample order presentation from
the consumers’ evaluation. The consumers evaluated the six
meads on a continuous scale from 0 (dislike extremely) to 10
(like extremely) for the following sensory attributes: aroma,
flavour, sweetness, alcohol feeling and general appreciation.
Statistical analysis
The data obtained from the consumers’ sensory evaluation were analysed by the R® software. The effects of sweetness and alcohol content were evaluated by the following
mixed model:
Yijk= μ + ACi + Sj + Tk + eijk
where: Yijk is the sensory appreciation of the k consumer for the mead with i alcoholic content and j sweetness;
μ is the overall mean; ACi is the fixed effect of the alcohol content (i = 1, 2, 3); Sj is the fixed effect of sweetness (j = 1, 2);
Tk is the random effect of the consumer (k = 1, 2, …, 108);
eijk is the random error with zero mean and variance 1.
All interaction terms were removed from the full model
since they revealed as non-significant (p > 0.05) in a preliminary analysis of the data.
RESULTS AND DISCUSSION
Mead fermentations
The rates of yeast growth, sugar consumption, and ethanol,
glycerol and acetic acid productions during mead fermentations
are represented in Figure 1. In sweet and dry mead fermenta-
139
T. Gomes et al.
200
12
Figure 1B represents the fermentation development
for “dry mead”. In this case, ethanol content amounted to
106.8 g/L. This concentration was smaller than that reported
by Ukpabi [2006] for cassava honey mead (12.7–15.0%),
and Vidrih & Hribar [2007] after fermentation of chestnut,
lime and honeydew meads (14.2%). On contrary, our results
of “sweet” and “dry” meads were similar to the ethanol contents (4.6 to 11.8%) reported by Gupta & Sharma [2009] for
home brewed and commercial meads made from soya honey.
Glucose and fructose were metabolised by yeast to values
of 2.5±0.9 g/L and 20.4±6.8 g/L, respectively. In this case,
it was observed that the glucose consumption rate was higher
than that of fructose. The final concentrations of glycerol
and acetic acid (5.96±0.95 and 0.57±0.09 g/L, respectively)
were similar to those obtained in sweet mead fermentations.
The glycerol concentrations reported here were in agreement
with those obtained by Pereira et al. [2009] of 4.2 to 5.7 g/L,
using dark and light honeys enriched with a nitrogen source
during mead production.
Regarding glycerol, the production of this component
is desirable to obtain good quality meads since its presence,
like in wine, improves quality by influencing sweetness, fullness and smoothness. On contrary, the presence of acetic
acid is highly undesirable. Our results were identical to those
described by Pereira et al. [2009] and Mendes-Ferreira et al.
[2010] who reported meads with a volatile acidity from 0.51
to 0.84 g acetic acid/L. The acetic acid contents reported
by Róldan et al. [2011], and Sroka & Tuszynski [2007], when
studying the influence of pollen addition and the use of buckwheat honey, were of 1.4±0.18 g/L and 0.7 to 1.0 g/L, respectively, values above the sensory threshold for table wines
of 0.7 g acetic acid/L.
Ethanol is a primary metabolite that is expected to be produced during the exponential phase; however, in this study,
its production was also observed along the stationary phase
during both “dry” and “sweet” mead productions. This fact
is similar to the reported previously by Pereira et al. [2009]
and Gomes [2010].
In general terms and regarding both experiments (“dry”
and “sweet” meads), the glycerol concentrations obtained
were similar and within the values reported in the literature for
wines [Scanes et al., 1998]. In relation to the acetic acid, similar results were obtained for both types of mead, being the values smaller than the limit of human perception and within
the contents reported for Port wines [Esteves et al., 2004].
A
Brandy
addition
150
8
6
100
4
Concentration (g/L)
O.D. (640 nm)
10
50
2
0
0
0
10
20
30
40
50
60
70
80
90
Time (hours)
200
12
Brandy
addition
10
B
6
100
4
Concentration (g/L)
O.D. (640 nm)
150
8
50
2
0
0
20
40
60
80
100
120 140
160 180 200
0
220 240
Time (hours)
Biomass
Ethanol
Glycerol
Acetic acid
Glucose
Fructose
FIGURE 1. Mead fermentations development. Evolution of biomass,
ethanol, glycerol, acetic acid and reducing sugars are indicated for sweet
(A) and dry (B) meads.
tions (Figures 1A and 1B, respectively), lag phases around
10 hours were observed. The exponential phases followed approximately 45 hours. In sweet mead (Figure 1A), the sugar
consumption percentage corresponded to about 80% of the initial level. Glucose and fructose were simultaneously consumed
from the beginning to 27±4.2 g/L and 48±9.6 g/L, respectively.
As the sweetening power of fructose is higher than glucose (approximately twice), it is expected that these conditions provided
very sweet drinks [Lee, 1987]. At the time that the fermentations
stopped, the ethanol content was equal to 59.5±0.42 g/L. Glycerol and acetic acid were also produced, reaching concentrations of 5.10±0.54 and 0.46±0.08 g/L, respectively. This low
acetic acid content avoided the vinegar off-character [Mendes-Ferreira et al., 2010] that could be developed in the drink.
TABLE 1. Scores given by the consumers’ panel to meads with different alcohol and sweetness contents in relation to aroma, sweetness, flavour, alcohol
feeling and general appreciation.
Aroma
Alcohol content
(% (v/v))
Sweetness
Sweetness
Flavour
Alcohol
General appreciation
18
4.4±2.41
3.8±2.57
3.8±2.61
4.0±2.54
4.1±2.83a
20
4.3±2.43a
3.9±2.56a
4.0±2.63a
4.2±2.37a
4.1±2.70a
22
4.7±2.39a
3.8±2.70a
3.9±2.84a
4.4±2.61a
4.0±2.77a
Sweet
4.9±2.46a
5.2±2.42b
5.1±2.63b
5.0±2.36b
5.4±2.56b
Dry
4.0±2.27b
2.5±2.02a
2.7±2.18a
3.4±2.39a
2.7±2.23a
a
a
Values within columns with the same letter are not significantly different (p>0.05).
a
a
140
Mead Characteristics Versus Its Acceptability
Female
Male
Female
A
0.15
Male
B
0.15
0.10
Density
Density
0.10
0.05
0.05
0.00
0.00
0
5
Aroma
10
0
Male
Female
5
Sweetness
Female
10
Male
C
D
0.15
0.10
Density
Density
0.10
0.05
0.05
0.00
0.00
0
5
Flavor
10
0
Female
5
Alcohol
10
Male
E
0.15
Density
0.10
0.05
0.00
0
5
General appreciation
10
FIGURE 2. Density plots for consumers’ evaluation of meads aroma (A), sweetness (B), flavour (C), alcohol feeling (D) and general appreciation (E)
by gender.
Sensory analysis
The sensory evaluation scores of meads obtained
by the consumers’ panel are shown in Table 1.
The statistical analysis was performed using the full
model presented in the Materials and Methods section. This
analysis showed that the interaction between alcohol degree
and sweetness was non-significant (p>0.05) for all sensory
parameters evaluated. Thus, a reduced model was used without this interaction term.
The alcohol content of mead had no effect (p>0.05)
on the sensory attributes studied, namely, aroma, sweetness, flavour, alcohol feeling and general appreciation. Even
141
T. Gomes et al.
TABLE 2. Correlations among mead sensory attributes.
Variables
General appreciation
General appreciation
Aroma
Sweetness
Flavour
Alcohol content
1
Aroma
0.43
1
Sweetness
0.76
0.44
1
Flavour
0.82
0.51
0.79
1
Alcohol content
0.69
0.40
0.65
0.62
though, three alcohol levels were studied (18, 20 and 22%
(v/v)), no significant differences on consumers’ scores regarding the alcohol feeling of the meads were observed. On
contrary, sweetness had a significant effect on mead sensory evaluation of consumers. The sweet meads had always
higher scores (p<0.05) in aroma, sweetness, flavour, alcohol feeling and general appreciation, than dry meads. Thus,
the meads with the highest sugar content (sweet meads)
were the most appreciated by the consumers’ panel. Taking into account the scale from 0 (dislike extremely) to 10
(like extremely), the mean value obtained for general appreciation of 5.4±2.56 suggested that consumers liked slightly
the “sweet meads”. On other hand, the “dry meads” were
rated significantly (p<0.05) lower, 2.7±2.23, showing their
low acceptability by the consumers.
The correlations among the sensory attributes of meads
varied from 0.40 to 0.82 as shown in Table 2. The general appreciation presented high correlations (> 0.75) with sweetness and flavour, indicating the relative importance of these
two attributes for mead acceptability by consumers. The correlation between flavour and sweetness was high (r=0.79),
showing the important effect of sweetness on mead flavour.
Regarding gender, no differences (p>0.05) were found between males and females for the sensory attributes of meads,
as shown by the density plots (Figures 2A to 2E), where similar distributions were observed for men and women for all
sensory parameters studied.
CONCLUSIONS
Different types of mead with different sweetness and alcohol content were successfully produced by halting the fermentation process at different times and by adding different
quantities of brandy. Thus, this study showed that it is possible to produce meads with different sweetness and alcohol
content by changing the fermentation conditions. Sweetness
influences significantly the sensory properties of mead, unlike
alcohol content on the range of 18 to 22% (v/v). The sweet
meads were the most appreciated by the consumers regardless their alcohol contents, showing that mead sweetness is an
essential requisite for consumers’ acceptability.
In conclusion, the present work showed that the final sugar
content in mead is a key point for guaranteeing its acceptance
by consumers. In order to increase consumers’ overall satisfaction, further studies should be conducted to define the optimum sugar content to be used in future mead production.
1
ACKNOWLEDGEMENTS
We acknowledge the financial support (Project PTDC/
AGR-ALI/68284/2006) from Fundação para a Ciência e Tecnologia (FCT) of the Portuguese Republic Government.
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DOI: 10.1515/pjfns-2015-0025
http://journal.pan.olsztyn.pl
Original article
Section: Food Quality and Functionality
Origin of Synthetic Particles in Honeys
Gerd Liebezeit*, Elisabeth Liebezeit
MarChemConsult, Altjührdener Strasse 6, 26316 Varel, Germany
Key words: honey, contamination, particle, fibre, fragment, inflorescence
A total of 47 honeys and 22 flowering plants was analysed for their load of synthetic fibres and fragments. In all samples investigated foreign
particles were found. These include also black carbon particles which were not enumerated. Fibres and fragments ranged from 10 to 336 kg-1 and 2 to
82 kg-1 honey, respectively. The data of the flowering plants analysed indicate that a major proportion of the particle load may originate from external
sources, i.e. these particles are brought into the beehive by the worker bees during nectar collection.
INTRODUCTION
MATERIAL AND METHODS
Honey, despite it being an almost non-processed aliment,
has been shown to contain contaminants such as trace metals
[e.g. Formicki et al., 2013; Matusevicius et al., 2010], pesticides [Al-Waili et al., 2012], chlorinated compounds [Matusevicius et al., 2010; Rissato et al., 2007] and others [Bogdanov,
2006].
Honey has also been reported to contain filth of natural
origin [Canale et al., 2014]. These authors noted the presence
of carbon particles, other inorganic fragments and fragments
of animal origin. These included insects, their cuticular fragments, mites and mammal hair. The abundance of this foreign matter showed no difference for honeys from both small
and large-sized producers. Liebezeit & Liebezeit [2013] noted
the presence of particles of synthetic origin in honeys available in Germany. Here also no differences were observed between small beekeeping enterprises and large producers.
The latter authors also suggested possible pathways
by which this contamination may reach the final product.
These include external and internal sources. The internal
sources can be related to the diligence shown during honey
extraction and packaging. In this case, the extent of contamination can be controlled by the processing facility. The external source as was shown by the analysis of flowers is related to
the presence of particles in the atmosphere that may deposit
on flowers there being held by the pollenkitt.
In this communication we present more data on the presence of synthetic particles in honeys as well as in external
sources, i.e. flowers.
A total of 47 honey samples was obtained from supermarkets as well as directly from German small-scale beekeepers.
The honeys investigated are given in Table 1. Liquid honeys were mixed with a an equal volume of 40°C water while
solid ones were heated to 40°C until liquified and then diluted. The mixtures were passed through a 40 μm steel sieve.
The material remaining on the sieve was copiously rinsed with
warm deionised water and, after transfer with 30% H2O2 to
a 50 mL wide neck Erlenmeyer beaker and cooling to room
temperature, treated at room temperature for 72 h. The oxidised samples were then filtered over 0.8 μm grey, gridded cellulose nitrate filters, treated several times with a small volume
of 90°C water to remove waxy particles and dried at ambient
temperature. One series of 12 samples was analysed in duplicate.
Inflorescences of 22 species were collected from April 2014
to August 2014 (Table 2). Depending on the size of the inflorescence, between 1 and 25 specimens were completely covered with 30% H2O2 for 24 h. After removal of the larger parts
such as petals the peroxide solution was sieved over a 40 m
steel sieve. After copious rinsing, the remaining particulate
material was filtered over 0.8 m cellulose nitrate filters.
After extensive rinsing with 0.8 m filtered deionised water,
wet filters were covered with 6 mL Rose Bengal (4,5,6,7-tetrachloro-2’,4’,5’,7’-tetraiodofluorescein, 200 mg/L; [Lusher
et al., 2013; Williams & Williams, 1974] to stain natural organic particles. After 5 min, the dye was filtered and the stained
material washed dye-free with filtered deionised water. After
drying at ambient temperature, the samples were analysed
under a dissecting microscope at up to 80x magnification.
No attempts were made to determine fibre lengths or polymer type. Particles that were not stained are regarded as being
* Corresponding Author: Tel.: +49 4451 804852;
E-mail [email protected] (G. Liebezeit)
© Copyright by Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences
© 2015 Author(s). This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs License
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
144
Origin of Synthetic Particles in Honeys
TABLE 1. Summary of honeys analysed.
No.
Type
Origin
State
1
mountain
Switzerland
solid
2
not specified
Switzerland
solid
3
not specified
Switzerland
solid
4
chestnut
Switzerland
solid
5
flower
EU/non-EU
solid
6
flower
EU/non-EU
solid
7
forest
Switzerland
solid
8
forest
Switzerland
liquid
9
flower
Switzerland
liquid
10
spring
Switzerland
solid
11
country
EU/non-EU
solid
12
flower
EU/non-EU
solid
13
flower
EU/non-EU
solid
14
flower
Bulgaria
solid
15
flower
Switzerland
solid
16
not specified
EU/non-EU
solid
17
summer flower
EU/non-EU
solid
18
flower
EU/non-EU
solid
19
not specified
Toscana
solid
20
not specified
Switzerland
solid
21
not specified
Germany
solid
22
Millefiori
Italy
solid
23
chestnut
Italy
liquid
24
sunflower
Italy
solid
25
orange
Italy
solid
26
acacia
Bulgaria
liquid
27
eucalyptus
Spain
liquid
28
honeydew
Italy
liquid
29
chestnut
Spain
liquid
30
linden
Bulgaria
liquid
31
coriander
Bulgaria
liquid
32
Lavendulum
Spain
liquid
33
mountain
Spain
liquid
34
not specified
Latin America/E
Europe
liquid
35
not specified
EU/non-EU
liquid
36
mountain
Spain/France
liquid
37
flower
EU/non-EU
liquid
38
not specified
EU/non-EU
liquid
39
not specified
Latin America
solid
40
mountain
France
solid
41
flower
France
liquid
42
not specified
EU/non-EU
liquid
43
not specified
France
liquid
44
flower
France
liquid
45
flower
Latin America
liquid
46
flower
Germany
liquid
47
fruit blossoms
Germany
solid
of synthetic origin and will be referred to in the following as
microplastics using the definition for this material as being
particles smaller than 5 mm but larger than 1 m.
As the filters used were found to be occasionally contaminated with fibres, presumably from the production process,
they were rinsed with deionised filtered water and checked under the microscope prior to use. To avoid airborne contamination [Liebezeit & Liebezeit, 2013] the filtration unit and all
other glassware used was covered during the whole workup
procedure. Maximum exposure time to the atmosphere including microscopic analysis was 20 to 30 min. Blank filters
carried through the complete scheme gave a maximum of 2 fibres/filter. Laboratory air sucked through a previously rinsed
filter for 1 h at 50 L/min had 2 fibres, 0 fragments and 3 small
granular particles [Liebezeit & Liebezeit, 2014]. Hence, contamination from this source can be neglected. All water used
as well as the peroxide solution were filtered over 0.8 m cellulose nitrate filters prior to use.
Some pollen types and also some string-like fibers were
not stained under the conditions given above. Nevertheless
these can be easily distinguished by their morphology from
the more irregular microplastic particles. The same holds for
chitin which is also not stained by Rose Bengal. Here again
morphology and discolouration can be used to exclude these
particles from the microplastic counts.
As quartz grains were observed in the flowers analysed as
well as in some cases in honey samples only fibre and fragment counts will be reported here.
RESULTS AND DISCUSSION
In all 47 honey samples analysed both fibres and fragments were found ranging from 10 to 336 fibres/kg and 2 to
82 fragments/kg (Figure 1). Fibres are clearly dominating
(Figure 2) with lengths from 40 m up to several millimeters
(Figure 3). Fragments were generally smaller in size reaching
only some ten micrometers. For the 12 honeys investigated
in duplicate (Figure 1) relative variations between samples
ranged from 0 to 57.2% for fibres and 0 and 55.5% for fragments indicative of an inhomogeneous distribution of particles in the packaged product.
In addition, black carbon particles were frequently encountered. Most of these were granular in shape and are
hence not reported in detail here. These findings, however,
corroborate the data by Canale et al. [2014] who also found
large number of carbon particles in their samples of Italian
honeys.
Comparing samples from smaller beekeepers with those
available from large scale producers did not show significant
differences. This indicates that honey harvesting, processing
and packaging do not contribute to a large extent to the foreign
particle load. Canale et al. [2014] reported insects and mammal hair in some of their samples providing evidence for poor
operating conditions during honey processing and packaging. In the present suite of samples no intact insects or insect
remains were found while in two samples one hair each was
noted. This might be due to the fact that the larger producers
screen their products through 200 m sieves, a practice which,
at least in Germany, is also exercised by small beekeepers.
145
G. Liebezeit & E. Liebezeit
In all flowering plants analysed particles were found that
could not be stained by Rose Bengal and hence can be considered to be of synthetic nature (Table 2; Figure 4). Furthermore,
in several filaments of untreated lily blossoms (Lilium sp.) fi400
Total fibres (n/kg)
300
200
100
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
100
Total fragments (n/kg)
80
60
40
20
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
Sample number
FIGURE 1. Fibre and fragment numbers in 47 honey samples. Numbers
refer to Table 1.
400
bres were observed (Figure 4). As in the honeys analysed fibres were dominant also in all plant species (Table 2) making
up on average 77.9±19.0% (n=27). This is direct evidence for
the deposition of foreign particulate matter on flowers there
being held by the pollenkitt and other pollen coatings [Hesse,
2010]. That this foreign material is transferred to the hive
by pollen collecting bees is also evident from the finding of fibres in their pollen load. Here of six investigated specimens
two were carrying fibres.
Our results clearly indicate that fibres and, to a lesser extent, fragments are already present in the bees’ feed and are
transferred from the blossoms to the hive by the insects. Particles in the atmosphere are derived from a variety of sources
including abrasion of clothing. Also synthetic particles used
to improve horticultural soils such as polystyrene peat or hygro peat may become airborne by outblowing after dry periods. The same may hold for synthetic fibres present in sewage
sludge used as fertilizer in agriculture [Habib et al., 1998; Zubris & Richards, 2005].
Little information is available on the content of organic
fibres, both natural and synthetic, in ambient air. Altree-Williams & Preston [1985] found contents from <1000 to 63,000
fibres m-3. Data provided by Schneider et al. [1996], using optical microscopy, support the finding that organic fibers in indoor air occur in concentrations larger than those of “other
inorganic” fibres.
Synthetic organic fibers include polyethylene, polypropylene, polyvinylalcohol, polyester, polyamide and polytetrafluoroethylene fibers [Hodgson, 1993]. Fragmentation of macroplastic litter due to the action of sunlight, oxygen, humidity
and temperature [Barnes et al., 2009] or agricultural films
may lead to the formation of fragments in the sub-millimetre
range. These will, as other particles in this size range, become
easily airborne. Especially for agricultural films the claim
that these are degraded is based on the fact that some physical properties such as tensile strength or elongation at break
change as films age. The formation of final degradation products such as water or carbon dioxide is only rarely assessed.
Total particles (n/kg)
300
200
100
66
26
0
fibres
fragments
FIGURE 2. Median, upper and lower quartiles, 95% values and outliers
for 47 honey samples.
FIGURE 3. Example of fibres and fragments in honey.
146
Origin of Synthetic Particles in Honeys
TABLE 2. Summary of flowering plants analysed.
Species
Aquilegia
Aquilegia vulgaris
Daisy
Bellis perennis
Cornflower
Centaurea cyanus
Cornel cherry
Cornus mas
Crocus
Crocus sp.
Hazelnut
Corylus avellana
Hawthorn
Crataegus sp.
Snowdrop
Galanthus sp.
Hyazinth
Hyacinthus sp.
Magnolia
Magnolia sp.
Daffodil, large
Narcissus sp.
Forgetmenot
Myosotis sp.
Daffodil, small
Narcissus sp.
Primrose
Primula sp.
Plum
Prunus sp.
Sour cherry
Prunus cerasus
Lungwort
Pulmonaria officinalis
Rose
Rosa sp.
Willow
Salix sp.
Dandelion
Taraxacum officinale
Tulip
Tulipa sp.
Inflorescences
analysed
Microplastic particles
fibres
fragments
5
3
5
9
2
3
10
12
2
4
4
28
8
11
4
10
5
0
4
4
50
40
4
5
5
16
6
25
0
1
8
14
0
1
9
2
5
5
1
3
21
4
12
17
4
5
23
6
4
51
6
14
14
2
10
13
2
6
38
4
3
3
39
6
2
4
8
24
3
5
4
2
3
15
7
Particles >10 m, referred to as giant or ultragiant particles [cf. Johnson, 1982] have been reported to be present
in the atmosphere. For mineral particles >75 m long range
transport has been shown by Betzer et al. [1988].
While most studies related to these particles determination for only the elemental composition, de Bock et al. [1994]
reported on the presence of organic constituents in North Sea
particles >20 m but did not investigate their exact nature.
Van Malderen et al. [1992] noted a markedly higher abundance of giant organic particles in air masses with a continental origin.
Black carbon or soot particles which were regularly found
in the present suite of samples originate from combustion
processes such as forest fires or fossil fuel burning. These
particles may be transported over very long distances [e.g.
Ramanathan et al., 2007].
FIGURE 4. Examples of foreign particles in various flowers. The lily filament in the upper right part was photographed directly without further
treatment.
These data suggest that the global environment is affected
to a large extent by microplastic particles either from direct
inputs or from fragmentation of macrolitter.
While an atmospheric source for the particles is proven
by our findings the exact contribution to the overall foreign
particle load in honey is still to be determined. The finding
of Canale et al. [2014] of insects or mammal hair or the data
on fibre content of indoor air suggest that improper handling
of the product or the ubiquitous presence of airborne particles
may also contribute to the overall particle burden of honey.
This can surely be reduced by the beekeeper using adequate
processing techniques.
ACKNOWLEDGEMENTS
We are indebted to Swiss Radio and Television (SRF) for
the permission to use data from an investigation carried out
for SRF.
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Submitted: 30 September 2014. Revised: 20 February 2015.
Accepted: 24 February 2015. Published on-line: 15 April 2015.
Pol. J. Food Nutr. Sci., 2015, Vol. 65, No. 2, pp. 149–150
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