1. science
2. chemistry

J. Trop. Agric. and Fd. Sc. 39(2)(2011): 213– 227
C.K. Ngan, A.M. Khairatul and B.S. Ismail
Determination of sample processing uncertainty in bifenthrin
residue analysis in carambola and mango
(Penentuan ketakpastian pemprosesan sampel dalam analisis residu bifenthrin dalam
belimbing dan mangga)
C.K. Ngan*, A.M. Khairatul* and B.S. Ismail**
Keywords: uncertainty, sample processing, bifenthrin, pesticide residue analysis, Averrhoa
carambola, Mangifera indica
Abstract
The uncertainty of bifenthrin residue concentration due to sample processing
of carambola and mango were determined by analysing the fruits which had
been spiked with the pesticide. The fruits were subsequently homogenised to
yield analytical portions which were analysed in 15 g and 150 g masses. The
uncertainty of sample processing for carambola for 15 g and 150 g were 15.4%
and 5.2% respectively. For mango, the uncertainty of sample processing were
26.6% (15 g) and 9.8% (150 g). The uncertainty values obtained indicated
that the sample processing uncertainty was significantly larger as the mass of
analytical portion decreased. The derived empirical function for the uncertainty
of sample processing for carambola within 15–150 g range is CVSP = (4.1/W)0.5
with sampling constant, KS = 4.1 kg. However, the empirical function of
the uncertainty of sample processing for mango could not be derived due to
falsification of the null hypothesis in F-test analysis.
Introduction
Samples of raw agricultural commodities,
known as laboratory samples according to
Codex definition (CAC 1999), received by
laboratories for pesticide residue analysis
are always subjected to two main procedures
to transform the laboratory samples into a
condition which is ideal for proper method
analysis. The two procedures are sample
preparation and sample processing.
According to Hill and Reynolds (1999),
the definition of sample preparation is to
convert laboratory sample into analytical
sample by removal of parts (soil, stones,
bones) that are attached to the commodity
during harvesting or packaging. Sample
processing on the other hand, can be
described as a procedure to transform
the prepared sample into acceptable
homogeneous portions with respect to
residue distribution, prior to sub-sampling
of sample (known as analytical portion) for
method analysis.
Sample preparation is the first step
where certain parts of laboratory sample
was brushed, peeled or cut off from the
commodity. The removal of the parts is
necessary because they do not constitute
*Strategic Resources Research Centre, MARDI Headquarters, Serdang, P.O. Box 12301,
50774 Kuala Lumpur, Malaysia
**School of Environmental and Natural Resource Sciences, Science and Technology Faculty, Universiti Kebangsaan
Malaysia, 43600 UKM Bangi, Selangor, Malaysia
Authors’ full names: Ngan Chai Keong, Khairatul Azmah Mohamed and Ismail bin Sahid
E-mail: [email protected]
©Malaysian Agricultural Research and Development Institute 2011
213
Sample processing uncertainty in bifenthrin residue analysis
the part of commodity that Codex identifies
(FAO 2000) in food residue analysis.
Because of that, sample preparation
is assumed to exert no influence on
combined uncertainty of pesticide residue
concentration.
Processing of laboratory samples
is one of the components in method of
analysis that is always being overlooked in
the estimation of pesticide residues. Since
the early years of method development for
pesticide residue analysis in food, it was
assumed that sufficient homogenisation was
achieved. Sub-sampling of the homogenised
sample as required by the analytical method
can introduce significant errors if the
homogenisation process is not efficient.
Ideally a perfect homogenisation will result
in a more uniform distribution of residue in
the homogenised sample. Any sub-sampling
from that perfectly homogenised sample will
yield the same residue level in the different
replicates.
According to Ambrus et al. (1996),
the main component of errors in pesticide
residue analysis can be grouped into three
main errors in terms of variance which are
described in the following equation:
SR2 = SS2 + SSP2 + SA2
Equation 1
Where SR = Residue concentration error
SS = Residue concentration error due to
field sampling error
SSP = Residue concentration error due to
sample processing error
SA = Residue concentration error due to
analytical error
Equation 1 can be redefined as relationship
among coefficient of variation, CV as shown
below:
CVR2 = CVS2 + CVSP2 + CVA2 Equation 2
Where CVR = Coefficient of variation of
concentration (or recovery)
CVS = Coefficient of variation of
concentration (or recovery) due to
field sampling error
CVSP = Coefficient of variation of
concentration (or recovery) due to
sample processing error
214
CVA = Coefficient of variation of
concentration (or recovery) due to
analytical error
CV can be also identified as uncertainty
parameter, therefore Equation 2 can be
used to quantify relationship between the
components of uncertainties.
Since 1990s there were a few
studies which have shown significant
uncertainty values due to sample processing.
Homogenisation of food utilises a number
of food processors of different brands.
Presently, there is no internationally agreed
procedure for testing the efficiency of
sample processing and analysts assume
that the processed sample is sufficiently
homogeneous. Contrary to that assumption,
some preliminary results indicated that the
uncertainty of sample processing can be as
large as 57% and 88% when an analytical
portion of 5 g and 2 g of apple were
analysed respectively (Ambrus 1999). The
uncertainty of sample processing increases
with decreasing amount of analytical
portion. For instance, the uncertainty of
sample processing for 5 g of apple was
found to be 56%, but the residues in 30 g
portions of apples and tomatoes may have
uncertainty of sample processing at 23% and
25% respectively (Maestroni et al. 2000b).
Some studies on determination of
sample processing uncertainty applied
radioactive-labelled (14C) pesticides in
their methodologies partly due to simpler
and shorter time in obtaining measurement
of residue concentration through Liquid
Scintillation Counter (LSC) (Tiryaki and
Baysoyu 2006). Application of radioactivelabelled pesticides in estimating sample
processing uncertainty poses a challenge
to some laboratories in Malaysia in terms
of resources devoted to purchase, handle,
manage and dispose of radioactive materials.
In principle, it is possible to use nonradioactive pesticides to estimate uncertainty
of sample processing although this approach
would result in larger uncertainty of
measurement and longer time in measuring
C.K. Ngan, A.M. Khairatul and B.S. Ismail
residue concentration due to extra work in
sample extraction and instrumental analysis
(GC or HPLC).
Carambola and mango are among
important fruit export commodities. There
were incidences of rejection of carambola
consignments in the European’s entry port
due to MRL (Maximum Residue Limit)
violations in terms of pesticide residues.
Information on results of residue analysis
in every export consignment is produced
as part of the market access requirement.
Usually importers would demand that
test reports on residue analysis should be
generated from ISO/IEC 17025 certified
laboratory. As stated in clause 5.4.6.2 of
the ISO/IEC 17025 requirements (ISO/IEC
2005) for the competence of testing and
calibration laboratories, significant sources
of uncertainty influencing the accuracy
and precision of the test result, should be
identified and reasonably estimated by the
laboratory. Known contribution of sources
of uncertainty in pesticide residue analysis
would provide confidence and validity of the
test results.
The main objective of the study was to
determine sampling processing uncertainty
of bifenthrin in carambola and mango using
non-radioactive approach. The second
objective of the study was to determine the
possibility of establishing empirical function
of sample processing uncertainty within
15 – 150 g analytical portions for carambola
and mango based on non-radioactive
approach.
Materials and methods
The method used to determine uncertainty
of sample processing and establish its
empirical function was based on study by
Maestroni et al. (2000b). Basically there
were two main relevant procedures in the
study, i.e. sample processing and method
of analysis. In sample processing, the fresh
fruits were chopped into smaller pieces
and homogenised in the food processor.
For analysis, the analytical method by
Ma et al. (2005) was used in the study,
in which the method has been validated
for analysis of cypermethrin (synthetic
pyrethroid pesticide). The analytical method
is detailed in Sample extraction and Gas
chromatograph analysis section.
Pesticide standards
Bifenthrin was used in the study because
of its stability and consistent recoveries
inferred from the author’s laboratory longterm quality control data. Some pesticides
such as chlorothalonil and maneb are
known to degrade during sample processing
(Hill et al. 2000). Therefore, it is vital
to select stable pesticide in this study so
that uncertainty due to stability will not
interfere in estimation of uncertainty of
sample processing. Bifenthrin was purchased
from the pesticide standard supplier,
Dr. Ehrenstorfer Laboratories GmbH,
Germany. The pesticide standard was
used for spiking of fruit surfaces and Gas
Chromatograph (GC) calibration (residue
quantification).
Sample processor
The food processor (Robot Coupe Blixer
5 V.V.) used for sample processing in the
study is shown in Plate 1.
Sample preparation
Fruits purchased from the local market
were tested by the analytical method (Ma
et al. 2005) for presence of bifenthrin prior
to commencement of the experiment. Fruit
Plate 1. Food processor Robot Coupe Blixer 5 V.V.
215
Sample processing uncertainty in bifenthrin residue analysis
batches with no detected bifenthrin were
subsequently used in the study. Any nonanalysed materials attached to fruits such as
stems, dirt, etc., were removed prior to fruit
surface treatment and sample processing.
Fruit surface treatment
Each replicate consisted of 5–8 fruits,
with a total weight of about 1 kg. The
weight of each fruit was recorded. The
amount of bifenthrin standard to be applied
on the surface of each fruit to give total
homogenous concentration was determined
by multiplying the weight of each fruit with
the spiked concentration, which was 0.5
mg/kg. For carambola, 75.8–124.8 μl of
1000 μg/ml bifenthrin standard solution was
spiked onto each fruit (151.5–249.5 g/fruit)
while for mango, 81.3–124.8 μl of 1000 μg/
ml bifenthrin standard solution was used
(162.6–356.2 g/fruit). The fruit was cut into
half and the cut surface was placed touching
the plastic tray covered with aluminium
foil so that the fruit would not slip during
the fortification process. The calculated
amount of pesticide standard was applied
using a micro syringe on the skin surface
of the fruit making sure that the applied
pesticide standard did not run off from the
fruit surface to the tray. The total required
volume of standard solution was applied
on a single spot on each fruit within one
replicate for the purpose of simulating the
worst case scenario in terms of homogeneity
of residues in fruit units. The surface-treated
fruits were allowed to dry for 15 min.
Sample processing and sampling of
analytical portion
The surface-treated and non-treated fruits
were chopped into smaller pieces and
transferred into the food processor (Robot
Coupe Blixer 5 V.V.) for homogenisation
for 2 min until a smooth sample matrix
was achieved. Two masses of analytical
portions, i.e. 15 g and 150 g were taken
from the homogeneous mixture. Two
masses of analytical portions with a
difference of a factor of 10 were chosen in
216
the study with the purpose of establishing
empirical sampling uncertainty function
within 15 – 150 g range (Maestroni et al.
2000b) [refer to F-test statistical analysis
for establishing empirical function KS =
W x (CVSP)2]. For each mass of analytical
portion, five replicates were taken from the
homogeneous mixture. The whole procedure
(surface-spiking, sample processing and
sampling of analytical portion) was treated
as one trial. There were five trials conducted
for carambola and mango, respectively.
Sample extraction
All analytical portions were subjected
to sample extraction method by Ma et
al. (2005) with modifications. Analytical
portions of 15 g homogenised sample were
weighed in 250 ml borosilicate bottles. In
each bottle, 30 ml of ethyl acetate, 2.5 g
of natrium hydrogen carbonate and 15 g of
natrium sulphate were added. The mixture
was blended for 2 min by Ultra-Turrax
T25 homogeniser (IKA, Germany). The
bottle containing the extract was shaken
in an orbital shaker for 2 h at 150 rpm.
Then 10 ml of supernatant was decanted
into a 15 ml centrifuge tube and 2.5 g of
magnesium sulphate (to absorb remaining
water in the extract) and 0.125 g of Primary
Secondary Amines (PSA) powder (cleanup sorbent) were added. This later step
was replicated thrice. The tubes were
centrifuged at 2000 rpm for 2 min. After
centrifugation, 5 ml of the centrifuged
extract was decanted and concentrated to
dryness by a gentle stream of nitrogen.
The extract was reconstituted in 1 ml of
hexane prior to residue determination by
gas chromatography (GC). For the 150 g
analytical portions, adjustments were made
to the solvent volume, natrium hydrogen
carbonate, natrium sulphate, magnesium
sulphate and PSA powder mass used
proportionately to the sample mass.
Gas chromatograph analysis
All sample extracts were analysed using
the gas chromatograph (Hewlett-Packard
C.K. Ngan, A.M. Khairatul and B.S. Ismail
6890 model) equipped with micro-Electron
Capture Detector (µECD) and DB-5MS
column (30 m long, 0.32 mm internal
diameter and 0.25 µm film thickness) to
determine the bifenthrin. The flow of carrier
gas, helium, was set at 2 ml/min at constant
flow mode. The injection mode was splitless.
Injection volume of sample was set at 1 µl.
The injector and detector temperatures were
set at 250 °C and 320 °C, respectively. The
oven temperature programme was set at an
initial temperature of 60 °C, maintained for
1 min and then raised to 320 °C at the rate
of 30 °C/min, which was maintained for
3 min.
Calculation of uncertainty of sample
processing
All recoveries data (respectively for
carambola and mango) were used to
calculate variance of all recoveries, VT with
(h x n) – 1 degree of freedom. Average
of variance of analysis, VAve (average
of variances of analytical portions) was
calculated with h x (n – 1) degree of
freedom. One-tailed F-test was applied
at 95% confidence level to test if VT is
significantly larger than VAve. If Fcalculated
(=VT/VAve ) > Fcritical then F-test reveals that
VT is significantly larger than VAve, therefore
the following equation is valid:
VSP = VT – VAve
Equation 3
Where VSP = Variance of sample processing
Co-efficient of variation of sample
processing or uncertainty of sample
processing, CV­SP can be calculated from the
following equation:
CVSP = (VSP)1/2/R
Equation 4
Where R
= Mean recovery of all recovery
replicates
F-test statistical analysis for establishing
empirical function
An empirical function of uncertainty of
sample processing (CVSP) in relation to
sample (analytical portion) mass could
be used to derive uncertainty of sample
processing of any sample mass within a
certain mass range. This concept originated
from work done by Ingamells and Switzer
(1973) in which sampling constant is defined
as the weight of single increment in sample
size withdrawn from a well-mixed sample
to hold relative sampling (sample processing
and withdrawal) uncertainty within 1% with
68% level of confidence. The empirical
relationship between uncertainty of sampling
and sample size can be derived from the
following equation:
KS = W x (CVSP)2
Equation 5
Where KS = Sampling constant
W = Weight of analytical portion
(sample size)
CVSP = Uncertainty of sample processing
In this approach, the sampling constant
(KS) can be established if a high degree of
homogeneity can be determined by F-test
analysis of variance of measurements from
two sets of sample (analytical portion) mass
in which the difference between the two
sample masses differed by a factor of 10
(as for this study, two sample masses, 15 g
and 150 g were chosen). The following
equations describe equation 5 in terms of
15 g and 150 g analytical portions:
KS(15g) = W15g x (CVSP(15g))2 Equation 6
KS(150g) = W150g x (CVSP(150g))2 Equation 7
Where KS(15g) = Sampling constant due to 15 g
analytical portion
KS(150g) = Sampling constant due to 150 g
analytical portion
= Analytical portion mass of 15 g
W15g
W150g
= Analytical portion mass of 150 g
CVSP(15g) = Uncertainty of sample processing
for 15 g analytical portion
CVSP(150g) = Uncertainty of sample processing
for 150 g analytical portion
If sample processing results in a very well
mixed processed sample, equations 6 and 7
should be equivalent. Therefore:
KS(150g)
=KS(15g)
Equation 8
W150g x (CVSP(150g))2=W15g x (CVSP(15g))2
Equation 9
(CVSP(150g))2
=(CVSP(15g))2 x W15g/W150gEquation 10
217
Sample processing uncertainty in bifenthrin residue analysis
It should be noted that (CVSP)2 is actually
a variance of sample processing, VSP.
Therefore equation 10 can be reduced to:
VSP(150g) = VSP(15g) x W15g/W150g Equation 11
For the purpose of the second objective
of the study, a two-tail F-test at 90%
confidence was applied to check that
VSP150g and VSP15g x W15g/W150g are
not significantly different. Therefore in
F-test, the null hypothesis of the study was
summarised as:
Ho = there is no significant difference
between VSP(150g) and VSP(15g) x
W15g/W150g
H1 = there is significant difference
between VSP(150g) and VSP(15g) x
W15g/W150g
If the sample processing is efficient
(perfectly homogeneous sample matrix),
there should not be any significant difference
(Fcalculated < Fcritical) between VSP150g and
VSP15g x W15g/W150g. Fcalculated is ratio
of VSP150g to VSP15g x W15g/W150g. Then
empirical function of sample processing
uncertainty from large analytical portion
(150 g) based on equation 5 can be used to
calculate uncertainty of sample processing,
CVSP within 15–150 g mass range.
Results and discussion
Recovery of bifenthrin in carambola and
mango of two analytical portion masses
are shown in Tables 1 and 2, respectively.
Generally, the larger analytical portion
(150 g) had fewer variations of calculated
F values between trials as compared to the
15 g analytical portion. For carambola, all
trials of 15 g analytical portions showed
larger Fcalculated than the Fcritical whereas in
150 g analytical portions, three out of five
trials yielded smaller Fcalculated values than
the critical F values (Table 1). All trials of
15 g and 150 g analytical portions of mango
have Fcalculated exceeding the critical values
(Table 2).
If the Fcalculated is smaller than the
critical value, then VT is not significantly
different from VAve. This outcome renders
estimation of uncertainty of sample
processing by equations 3 and 4 impossible.
The observed inconsistency between trials
even at 150 g analytical portion levels
indicated that the homogenisation process
did not give uniform results. This maybe
Table 1. Tabulated recoveries of bifenthrin in carambola and F-test analysis of each trial
Analytical Trial Replicate, h
portion (g)
15
1
2
3
218
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
Mean of Fcritical Fcalculated
recovery = VT/ VAve
(%)
1
105.0 99.3 104.0 9.263 6.319 40.990 110.953 2.860 6.486
2
107.0 105.0 105.0 1.333
3
117.0 115.0 120.0 6.333
4
109.0 112.0 116.0 12.333
5
117.0 118.0 115.0 2.333
1 92.6 93.2 95.7 2.703 7.069
384.939 108.420 2.860 54.452
2 96.0 103.0 104.0 19.000
3
144.0 146.0 144.0 1.333
4 96.7 93.5 93.6 3.310
5
111.0 108.0 105.0 9.000
1 99.0 100.0 98.7 0.463 1.262 55.253 91.347 2.860 43.782
2 79.5 76.4 76.4 3.103
3 89.2 91.2 91.4 1.480
4 93.5 94.1 94.1 0.120
5 95.0 96.8 94.9 1.143
(cont.)
C.K. Ngan, A.M. Khairatul and B.S. Ismail
Table 1. (cont.)
Analytical Trial Replicate, h
portion (g)
4
5
150
1
2
3
4
5
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
Mean of Fcritical Fcalculated
recovery
= VT/VAve
(%)
1 91.5 84.1 86.5 14.253 3.816 37.891 88.827
2 79.5 76.4 79.4 3.103
3 89.2 91.2 91.4 1.480
4 93.5 94.1 94.1 0.120
5 93.5 93.8 94.2 0.123
1
103.0 98.3 94.1 19.823 6.718
352.421 101.807
2 90.1 89.6 85.2 7.270
3 79.0 76.3 77.5 1.830
4
121.0 124.0 122.0 2.333
5
121.0 124.0 122.0 2.333
1 94.2 93.8 99.2 9.053 3.547 16.928 97.453
2 95.9 94.3 93.1 1.973
3 98.4 96.2 94.2 4.413
4 97.6 96.1 94.8 1.963
5
104.0 105.0 105.0 0.333
1 97.1 94.6 93.1 4.083 9.203 13.291 92.447
2 93.6 94.3 98.5 7.023
3 86.9 90.9 91.2 5.763
4 93.0 88.5 96.3 15.330
5 85.3 92.1 91.3 13.813
1 99.3 91.8 93.3 15.750 11.688 27.378 96.013
2
103.0 92.2 95.0 31.413
3 94.9 97.1 97.1 1.613
4 90.8 88.6 89.1 1.330
5
101.0 106.0 101.0 8.333
1 88.5 87.5 85.0 3.250 7.230 51.449 90.607
2 89.6 90.3 91.7 1.143
3 87.7 89.2 80.7 20.583
4 85.1 89.3 84.5 6.840
5
105.0 104.0 101.0 4.333
1 92.2 83.2 89.0 20.813 6.654 7.634 90.073
2 91.8 95.2 92.3 3.370
3 91.2 86.9 91.9 7.330
4 89.9 89.9 88.9 0.330
5 88.2 90.4 90.1 1.423
2.860 9.929
2.860
52.459
2.860 4.772
2.860 1.444
2.860 2.342
2.860 7.116
2.860 1.147
VA = Variance of replicate analysis of each analytical portion
VT = Variance of recovery of all replicates of each trial
Degree of freedom of VAve = h x (n – 1) = 5 x (3–1) = 10
Degree of freedom of VT = (h x n) – 1 = (5 x 3) – 1 = 14
h = Number of replicates of analytical portion
n = Number of replicate analysis of each analytical portion replicate
Fcritical = F(14,10) (95% confidence level) = 2.860
219
Sample processing uncertainty in bifenthrin residue analysis
Table 2. Tabulated recoveries of bifenthrin in mango and F-test analysis of each trial
Analytical Trial Replicate, h
portion (g)
15
1
2
3
4
5
150
1
2
3
4
220
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
1 82.3 85.2 80.8 5.003 6.731 81.010
2
101.0 98.0 98.2 2.813
3 73.0 75.8 79.7 11.323
4 87.5 86.4 87.6 0.443
5 72.7 76.6 80.2 14.070
1 65.5 64.8 64.6 0.223 22.318
484.173
2
133.0 114.0 121.0 92.333
3 82.7 80.3 80.2 2.003
4 90.8 95.1 98.8 16.030
5
113.0 112.0 114.0 1.000
1 71.9 80.9 75.5 20.520 6.959 81.054
2 58.7 64.7 60.6 9.403
3 78.7 76.0 76.9 1.890
4 55.6 55.7 54.9 0.190
5 67.5 70.8 69.6 2.790
1 56.4 60.6 57.9 4.530 4.282 59.994
2 72.4 68.1 73.5 8.143
3 61.1 60.9 64.1 3.213
4 48.9 46.5 50.4 3.870
5 60.4 60.0 58.0 1.653
1 41.6 45.3 44.4 3.723 1.942 68.989
2 50.2 48.2 50.3 1.403
3 65.7 67.8 68.0 1.623
4 55.1 56.2 55.2 0.370
5 58.6 56.7 59.9 2.590
1 91.3 92.0 93.8 1.663 4.340 16.597
2 94.7 92.1 95.1 2.653
3 93.3 96.0 98.7 7.290
4 92.3 95.8 96.6 5.230
5
100.8 105.1 102.1 4.863
1 79.2 80.8 82.7 3.070 3.871 24.112
2 90.4 86.5 89.2 3.990
3 92.1 93.1 92.8 0.263
4 81.8 83.2 81.1 1.143
5 86.6 83.3 80.0 10.890
1 90.4 87.5 94.1 10.943 4.225 17.069
2 84.0 84.0 84.1 0.003
3 91.2 87.4 90.1 3.823
4 94.7 94.6 92.5 1.543
5 96.3 92.1 93.1 4.813
1 77.0 79.3 78.8 1.463 3.693 19.974
2 82.5 85.3 83.2 2.123
3 81.8 88.0 84.7 9.623
4 73.5 74.6 73.9 0.310
5 80.3 84.2 84.1 4.943
Mean of Fcritical Fcalculated
recovery = VT/ VAve
(%)
84.333
2.860
12.036
95.320
2.860
21.694
67.867
2.860
11.648
59.947
2.860
14.011
54.880
2.860
35.525
95.980
2.860 3.824
85.520
2.860 6.228
90.407
2.860 4.040
80.747
2.860 5.409
(cont.)
C.K. Ngan, A.M. Khairatul and B.S. Ismail
Table 2. (cont.)
Analytical Trial Replicate, h
portion (g)
5
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
Mean of Fcritical Fcalculated
recovery = VT/ VAve
(%)
1 79.2 79.4 79.5 0.023 2.563 39.766 75.673
2 73.4 77.2 73.8 4.360
3 65.3 70.1 69.3 6.613
4 72.3 70.9 69.9 1.453
5 85.5 84.3 85.0 0.363
2.860
15.518
VA = Variance of replicate analysis of each analytical portion
VT = Variance of recovery of all replicates of each trial
Degree of freedom of VAve = h x (n – 1) = 5 x (3–1) = 10
Degree of freedom of VT = (h x n) – 1 = (5 x 3) – 1 = 14
h = Number of replicates of analytical portion
n = Number of replicate analysis of each analytical portion replicate
Fcritical = F(14,10) (95% confidence level) = 2.860
due to the results of the occurrence of
random errors as the repeated trials were not
conducted on the same day.
As inconsistent results were reported
for separate trials of 150 g analytical portion
of carambola, the values from the repeated
trials were combined and single values of
VT and VAve were calculated for carambola
and mango (Tables 3 and 4) for both large
and small analytical portions, respectively.
The values of Fcalculated for both 15 g and
150 g of carambola (Table 3) were bigger
than the critical F value (48.427 and 4.018
> 1.520), thus VT is significantly larger
than VAve. Therefore uncertainty of sample
prosessing, CVSP can be determined from
Equations 3 and 4. The same can be
concluded for mango since the Fcalculated
(combining all trials) for both analytical
portions are greater than the critical F value
(44.972 and 19.664 > 1.520) (Table 4).
Uncertainties of sample processing
from the combined repeated trials which are
estimated based on Equations 3 and 4 are
shown in Table 5. Estimated uncertainties
of sample processing for carambola
at 15 g and 150 g analytical portions
were 15.4% and 5.2%, respectively. For
mango, the uncertainties calculated were
26.6% (15 g analytical portion) and 9.8%
(150 g analytical portion). The range of
uncertainty obtained (%) is within ranges
reported in previous studies using radiolabelled pesticide. Uncertainty of sample
processing for apple was found to be
25.3% (30 g analytical portion) and 6.9%
(400 g analytical portion) in a study using
14C-labelled chlorpyrifos (Maestroni et
al. 2000b). Tiryaki and Baysoyu (2006)
estimated sample processing uncertainty
in cucumber to be 8.0% and 4.5% for
analytical portions of 5 g and 50 g,
respectively using 14C-labelled chlorpyrifos.
Ambrus (2004) claimed that CV for sample
processing can be as high as 100%, which
can be a significant component of the
combined uncertainty of the results.
From Table 5, the sample processing
uncertainty range for 150 g analytical
portion of carambola and mango was
5.2–9.8% whereas the sample processing
uncertainty range for 15 g analytical portion
of carambola and mango was 15.4–26.6%.
This finding is in accordance with the study
by Maestroni et al. (2000b) in which the
larger analytical portion yields smaller
sample processing uncertainty. In terms of
comparison of sample processing uncertainty
between fruit type, carambola has lower
uncertainty of sample processing (15.4% for
15 g and 5.2% for 150 g) as compared to
mango (26.6% for 15 g and 9.8% for 150 g)
221
Sample processing uncertainty in bifenthrin residue analysis
Table 3. Tabulated recoveries of bifenthrin in carambola and F-test analysis of combined, whole trials
Analytical Trial Replicate, h
portion (g)
15
1
2
3
4
5
150
1
2
3
4
222
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
Mean of Fcritical Fcalculated
recovery = VT/ VAve
(%)
1
105.0 99.3 104.0 9.263
2
107.0 105.0 105.0 1.333
3
117.0 115.0 120.0 6.333
4
109.0 112.0 116.0 12.333
5
117.0 118.0 115.0 2.333
1 92.6 93.2 95.7 2.703
2 96.0 103.0 104.0 19.000
3
144.0 146.0 144.0 1.333
4 96.7 93.5 93.6 3.310
5
111.0 108.0 105.0 9.000
1 99.0 100.0 98.7 0.463
2 79.5 76.4 76.4 3.103
3 89.2 91.2 91.4 1.480 5.037
243.922 100.311 1.520 48.427
4 93.5 94.1 94.1 0.120
5 95.0 96.8 94.9 1.143
1 91.5 84.1 86.5 14.253
2 79.5 76.4 79.4 3.103
3 89.2 91.2 91.4 1.480
4 93.5 94.1 94.1 0.120
5 93.5 93.8 94.2 0.123
1
103.0 98.3 94.1 19.823
2 90.1 89.6 85.2 7.270
3 79.0 76.3 77.5 1.830
4
121.0 124.0 122.0 2.333
5
121.0 124.0 122.0 2.333
1 94.2 93.8 99.2 9.053
2 95.9 94.3 93.1 1.973
3 98.4 96.2 94.2 4.413
4 97.6 96.1 94.8 1.963
5
104.0 105.0 105.0 0.333
1 97.1 94.6 93.1 4.083
2 93.6 94.3 98.5 7.023
3 86.9 90.9 91.2 5.763
4 93.0 88.5 96.3 15.330
5 85.3 92.1 91.3 13.813
1 99.3 91.8 93.3 15.750
2
103.0 92.2 95.0 31.413
3 94.9 97.1 97.1 1.613 7.664 30.792 93.319 1.520 4.018
4 90.8 88.6 89.1 1.330
5
101.0 106.0 101.0 8.333
1 88.5 87.5 85.0 3.250
2 89.6 90.3 91.7 1.143
3 87.7 89.2 80.7 20.583
4 85.1 89.3 84.5 6.840
5
105.0 104.0 101.0 4.333
(cont.)
C.K. Ngan, A.M. Khairatul and B.S. Ismail
Table 3. (cont.)
Analytical Trial Replicate, h
portion (g)
5
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
Mean of Fcritical Fcalculated
recovery = VT/ VAve
(%)
1 92.2 83.2 89.0 20.813
2 91.8 95.2 92.3 3.370
3 91.2 86.9 91.9 7.330
4 89.9 89.9 88.9 0.330
5 88.2 90.4 90.1 1.423
VA = Variance of replicate analysis of each analytical portion
VT = Variance of recovery of all replicates
Degree of freedom of VAve = h x (n – 1) = 25 x (3–1) = 50
Degree of freedom of VT = (h x n) – 1 = (25 x 3) – 1 = 74
h = Number of replicates of analytical portion
n = Number of replicate analysis of each analytical portion replicate
Fcritical = F(74,50) (95% confidence level) = 1.520
Table 4. Tabulated recoveries of bifenthrin in mango and F-test analysis of combined, whole trials
Analytical Trial Replicate, h
portion (g)
15
1
2
3
4
5
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
1 82.3 85.2 80.8 5.003
2
101.0 98.0 98.2 2.813
3 73.0 75.8 79.7 11.323
4 87.5 86.4 87.6 0.443
5 72.7 76.6 80.2 14.070
1 65.5 64.8 64.6 0.223
2
133.0 114.0 121.0 92.333
3 82.7 80.3 80.2 2.003
4 90.8 95.1 98.8 16.030
5
113.0 112.0 114.0 1.000
1 71.9 80.9 75.5 20.520
2 58.7 64.7 60.6 9.403
3 78.7 76.0 76.9 1.890 8.446
4 55.6 55.7 54.9 0.190
5 67.5 70.8 69.6 2.790
1 56.4 60.6 57.9 4.530
2 72.4 68.1 73.5 8.143
3 61.1 60.9 64.1 3.213
4 48.9 46.5 50.4 3.870
5 60.4 60.0 58.0 1.653
1 41.6 45.3 44.4 3.723
2 50.2 48.2 50.3 1.403
3 65.7 67.8 68.0 1.623
4 55.1 56.2 55.2 0.370
5 58.6 56.7 59.9 2.590
Mean of Fcritical Fcalculated
recovery = VT/ VAve
(%)
379.831 72.469
1.520
44.970
(cont.)
223
Sample processing uncertainty in bifenthrin residue analysis
Table 4. (cont.)
Analytical Trial Replicate, h
portion (g)
150
1
2
3
4
5
Recovery
VA
VAve
VT
of replicate
(meanVA)
analysis, n (%)
R1 R2 R3
Mean of Fcritical Fcalculated
recovery
= VT/ VAve
(%)
1 91.3 92.0 93.8 1.663
2 94.7 92.1 95.1 2.653
3 93.3 96.0 98.7 7.290
4 92.3 95.8 96.6 5.230
5
100.8 105.1 102.1 4.863
1 79.2 80.8 82.7 3.070
2 90.4 86.5 89.2 3.990
3 92.1 93.1 92.8 0.263
4 81.8 83.2 81.1 1.143
5 86.6 83.3 80.0 10.890
1 90.4 87.5 94.1 10.943
2 84.0 84.0 84.1 0.003
3 91.2 87.4 90.1 3.823 3.738 73.502 85.665
4 94.7 94.6 92.5 1.543
5 96.3 92.1 93.1 4.813
1 77.0 79.3 78.8 1.463
2 82.5 85.3 83.2 2.123
3 81.8 88.0 84.7 9.623
4 73.5 74.6 73.9 0.310
5 80.3 84.2 84.1 4.943
1 79.2 79.4 79.5 0.023
2 73.4 77.2 73.8 4.360
3 65.3 70.1 69.3 6.613
4 72.3 70.9 69.9 1.453
5 85.5 84.3 85.0 0.363
1.520
19.661
VA = Variance of replicate analysis of each analytical portion
VT = Variance of recovery of all replicates
Degree of freedom of VAve = h x (n – 1) = 25 x (3–1) = 50
Degree of freedom of VT = (h x n) – 1 = (25 x 3) – 1 = 74
h = Number of replicates of analytical portion
n = Number of replicate analysis of each analytical portion replicate
Fcritical = F(74,50) (95% confidence level) = 1.520
irrespective of its analytical portion size.
This maybe due to the high watery content
of carambola as compared to mango, which
makes it easier to be homogeneous.
Treated fruits from the fields are
expected to exhibit certain patterns of
pesticide residue distribution which are:
• Residue distributed over fruit surface.
Residue distribution per unit of
fruit depends on mode of pesticide
application. In some circumstances,
only certain parts of the fruit surface
224
are exposed to foliar spray. In the case
of post harvest treatment (e.g. dipping
of fruit in pesticide solution), residue is
expected to be distributed on the whole
fruit surface.
• Residue distributed within the fruit flesh
or juice. The presence of such residue is
due to translocation or capillary transport
of systemic pesticides.
The two scenarios listed above which relates
more to actual samples, are considered as
C.K. Ngan, A.M. Khairatul and B.S. Ismail
Table 5. Estimation of uncertainty of sample processing (combining all trial data) of
bifenthrin analysis in carambola and mango
Analytical portion mass (g)
VT
VAve
VSP
R
Carambola
15
243.922
5.037
238.885
100.311
150 30.792
7.664 23.128 93.319
Mango
15
379.831
8.446
371.385
72.469
150 73.502
3.738 69.764
85.665
CVSP = (VSP)1/2/R
0.154
0.052
0.266
0.098
VT = Variance of recovery of all replicates in Table 3 or Table 4
VAve = Average of variance of replicate analysis of each analytical portion from Table 3
or Table 4
VSP = Variance of sample processing, VSP = VT – VAve
R = Mean recovery of all replicates
CVSP = Uncertainty due to sample processing
Table 6. Results of two-tail F-test at 90% confidence level (combining all trial data) for
determination of KS = W x (CVSP)2
Fruit
VSP150g
VSP15g x W15g/W150g
Fcalculated*
KS = W x (CVSP)2
Carambola
Mango
23.128
69.764
23.886
37.139
0.968
1.878
4.1= W x (CVSP)2
n.a.
*Fcalculated is ratio of VSP150g to VSP15g x W15g/W150g
Fcritical = F(24,24) (90% confidence level) = 1.690
Numerator degree of freedoms = h – 1 = 25 – 1 = 24
Denumerator degree of freedoms = h – 1 = 25 – 1 = 24
n.a. = Not applicable
less extreme in terms of residue distribution
as compared to what is simulated in
this study. Therefore, sample processing
uncertainty of real fruit sample should be
lower than what is reported in this study.
A two-tail F-test at 90% confidence
level (Table 6) revealed that the null
hypothesis is proven true for carambola
in which there is no significant difference
between VSP(150g) and VSP(15g) x W15g /
W150g. Therefore the sub-sampling constant,
KS for carambola is 4.1 kg. The derived
empirical function for the uncertainty of
sample processing for carambola within
15–150 g is CVSP = (4.1 / W)0.5.
In the case of mango, there is
significant difference between VSP(150g)
and VSP(15g) x W15g / W150g. Therefore
the null hypothesis for mango is falsified.
Falsification of null hypothesis indicates
that the current sample processing method
is not fit for establishment of empirical
function of uncertainty of sample processing
of mango. Failure to establish empirical
function described by Equation 5 does not
render estimation of uncertainty of sample
processing impossible for the current sample
processing method. However, a separate
study on particular sample size needs to be
carried out in order to determine uncertainty
of sample processing for the current sample
processing method.
Another interpretation of this outcome
is that the current homogenisation method
using the Robot Coupe Blixer 5 V.V. food
processor is not efficient in yielding a
very well-mixed mango matrix. Therefore
improvements to the current sample
processing method could be achieved
through several approaches. One way is
225
Sample processing uncertainty in bifenthrin residue analysis
to prolong homogenisation time. Double
sample processing and mixing dry ice during
sample processing has been reported to
increase degree of homogeneity or reduce
uncertainty of sample processing (Maestroni
2000a). Double sample processing involves
initial mincing with large capacity chopper
followed by further homogenisation in
Warring Blender (recommended blender
used in pesticide residue analysis).
Conclusion
Uncertainty of bifenthrin concentration in
carambola due to sample processing for
sample size of 15 g and 150 g were 15.4%
and 5.2%, respectively. Whereas in mango,
uncertainty of bifenthrin concentration
due to sample processing for sample size
of 15 g and 150 g were 26.6% and 9.8%,
respectively. The uncertainty values obtained
indicated that the component of sample
processing is significantly larger as the mass
of analytical portion decreases. The derived
empirical function for the uncertainty of
sample processing for carambola within
15 – 150 g range is CVSP = (4.1/W)0.5 where
sampling constant, KS = 4.1 kg. However
empirical function of the uncertainty of
sample processing for mango could not
be derived due to falsification of the null
hypothesis in F-test analysis.
Acknowledgement
The authors wish to thank Ms Jamiah
Jaafar, Ms Siti Rahmah Abdul Hamid
and Ms Catherine Baun Dudong for
their assistance. This study was funded
by ScienceFund Project under Ministry
of Agriculture and Agro-based Industry
(Research Grant No. 05-03-08-SF0020).
226
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Abstrak
Ketakpastian kepekatan residu pestisid (bifenthrin) dalam belimbing dan mangga
akibat pemprosesan sampel ditentukan dengan menganalisis buah-buahan
berkenaan yang telah dibubuhi pestisid bifenthrin. Sampel buah dihomogen untuk
menghasilkan sampel analisis dalam dua jisim iaitu 15 g dan 150 g. Ketakpastian
pemprosesan sampel untuk 15 g dan 150 g belimbing ialah masing-masing 15.4%
dan 5.2%. Bagi mangga, ketakpastian pemprosesan sampel ialah 26.6% (15 g)
dan 9.8% (150 g). Nilai ketakpastian yang diperoleh menunjukkan ketakpastian
pemprosesan sampel adalah lebih besar secara signifikan apabila jisim analisis
sampel berkurang. Fungsi empirik ketakpastian pemprosesan sample (CVSP) yang
diterbitkan untuk belimbing dalam julat 15–150 g ialah CVSP = (4.1/W)0.5 dengan
pemalar pensampelan, KS = 4.1 kg. Namun begitu, fungsi empirik ketakpastian
pemprosesan sampel untuk mangga tidak dapat diterbitkan kerana hipotesis nul
dibuktikan salah dalam analisis ujian-F.
Accepted for publication on 22 June 2011
227