Method Development and Validation of Multiclass
Pesticide Residues and Metabolites in Wheat by GC-ECD and GC-MS
Mahesh K. Saini, Sudeep Mishra,
Samsul Alam*, Lalitesh K. Thakur, Ompal Singh, Syed K. Raza
Institute of Pesticide Formulation Technology,
Sector-20, Udyog Vihar, Opp. Ambience Mall, NH-8, Gurgaon, Haryana-122016,
India
*Corresponding Author E-mail: alam_samsul@rediffmail.com
A method was developed and validated for analysis of
25 multiclass pesticide residues and their metabolites in wheat grains using
GC-ECD for quantification, and GC-MS for confirmation. Samples were treated
through a method developed by slight modification in QuEChERS technique. The
GC-ECD instrument was calibrated at 6 calibration levels (viz. 0.005, 0.010,
0.050, 0.100,0.250, 0.500 mg/Kg) with regression co-efficient (r2)
varying from 0.9993 to 0.9756. Sample preparation include extraction by
Acetonitrile solvent (HPLC grade) and clean up by C-18, PSA and anhydrous MgSO4.
Limit of Detection (LOD) of pesticides in wheat matrix varies from
0.002-0.06µg/g and Limit of Quantitation (LOQ)from 0.004 to 0.2µg/g. Mean
recovery percentage of pesticides at 1 LOQ lies in range of 79.77-128.04 with
RSD below 16.35%. Uncertainty measured at three sources–purity of standards,
weighing and instrument precision. Maximum expanded uncertainty (2U) falls
between the range of 0.0008–0.487.
KEYWORDS:
Pesticide residue; Method development; LOD; LOQ; Wheat.
INTRODUCTION:
Cereals like wheat, rice, maize, millets are rich in
carbohydrate and minerals. They constitute the major portion of human diet. In
India wheat alone contributes to 50 % of total calorie intake. Production and
consumption of cereals are always greater than any other food commodity. Cereal
crops face problems of pest infestation and production loss by weeds. Wide
spectrums of Weedicides, Insecticides are used for crop protection from
competing plant and insects. Their use at both pre-harvest and post- harvest
duration has considerably increased the productivity of cereals since 1950. In
spite of using insecticide for crop protection the losses due to pest attack is
still high. According to an estimate soybean and wheat bears an annual loss of
26 to 29 %, whereas maize and rice bears 30--40 % of total production from pest
infestation [1].
Farmers increase the dose rate in order to prevent
losses which create the problem of pesticide residues in various substances
like soil, fruits, vegetables, and water. Regulating authorities (like. FAO US,
EPA) have prescribed the Maximum Residue Level (MRL) values for each pesticide
in water and various food items for example MRL for individual pesticide in
food is 10 µgKg-1 [2]. Presence of pesticides at above
MRL values pose threat to human health.
Pesticide residue analysis at trace level requires two
basic steps first- sample preparation and second- instrumental analysis. First
step of analysis removes matrix interference and concentrate the analyte up to
the (Limit of Quantification) LOQ level of instrument and second step allows
the qualitative and quantitative analysis of analyte by a suitable instrumental
technique. Sample preparation involves pesticide extraction and clean up
processes. Various pesticide residue analysis methods have already been
developed and validated using various extraction techniques like- Accelerated
Solvent Extraction (ASE)/Pressurized Liquid Extraction (PLE)[3],
Solid Phase Extraction (SPE)[4], Super Critical Fluid Extraction
(SFE) [5], Sonication [6] Microwave Extraction. Earlier
studies have reported better extraction of pesticides and their metabolites by
organic solvents like acetone [7--11], acetonitrile [12,13],
ethyl acetate [14, 15], methanol [16] and dichloromethane
[17]. Anastassiades et. al. and Lehotay et. al. established
acetonitrile as good extracting solvent in his QuEChERS (Quick, Easy, Cheap,
Effective, Rugged and Safe) method[18,19,]. Extraction of pesticide
in organic solvent is followed by clean-up step in which co-extracts are removed
by various techniques like Solid Phase Extraction cartridges (SPE), dispersive
solid phase extraction (d-SPE). A large number of SPE cartridges have been
utilized depending upon the nature of pesticides like C-18 cartridge [20,21,22],
Cation-Exchange SPE[16], Gel Permeation Chromatography (GPC)[23],
and Graphitized Carbon Black (GCB) [9,10], Amino propyl column [24].
Often these clean-up are performed by coupling one with other [25].
Dispersive Solid Phase Extraction (d-SPE) using anhydrous MgSO4, Primary
Secondary Amine (PSA), C-18 were used for clean up by Lehotay in his QuEChERS
method[18]. Pesticide analysis concluded by identification and
estimation of pesticide residue by various instrumental techniques like Gas
Chromatography (GC) and Liquid Chromatography (LC). These instruments coupled
with various detectors like Electron Capture Detector (ECD), Flame Ionization
Detector (FID), Flame Photometric Detector (FPD), Nitrogen Phosphorus Detector
(NPD), Ultra Violet (UV), Photo Diode Array (PDA) and Mass Spectrometry (MS)
provide varied degree of selectivity and sensitivity. Method development and
validation for pesticide residue analysis using above mentioned detectors had
been published. Patel et.al developed and validated rapid screening and
estimation of organophosphorus pesticide using GC-FPD [26]. GC-ECD
was used for the analysis of pyrethroid, organophosphate and organochlorine
residues in sediments and water [6,27]. Melo et.al. developed
analysis of one fungicide and five herbicide using Ultraviolet detector in High
performance liquid chromatography[4].
This paper presents an in-house method which was
developed and validated (single laboratory validation) for analysis of 25
multiclass pesticide residues and their metabolites on wheat. The various parameters
studied for method development and validation work were linearity, accuracy,
repeatability/ reproducibility, specificity/ selectivity, limit of detection
(LOD), limit of quantification (LOQ), and robustness/ruggedness [28, 29].
This paper presents the method development and validation work.
MATERIALS AND METHOD:
Sampling
Wheat grains were collected from the local grain
market of Gurgaon, Haryana (India). They were thoroughly washed by water and
dried in sunlight in order to remove the visible impurities and environmental
contamination. Dried wheat grains were grinded to smaller but of coarser
particles size.
Reagent and Materials
Certified Reference Material (CRM) of all the 25
pesticides were supplied by M/S Sigma Aldrich and Accustandard Inc. USA. Each
pesticide is prepared separately as a stock solution, at concentration of 1000
mg/Kg (~10mg in 10ml of vol. flask) in HPLC grade n-Hexane solvent procured
from Fisher Scientific. Solvents were used as such without distillation because
no impurities were found in blank chromatogram from GC-ECD and GC-MS. Mixed
spiking solution of 100 LOQ was prepared for spiking cereal sample at 1 LOQ, 5
LOQ and 10 LOQ. Both Stock and working standard were stored at -20 0C
in deep freezer of Siemen. Weighing balance of Metler Toledo make (range 0.01
mg -200g) were used for all weighing needs. Electric grinder of Bajaj Juicer,
mixer and grinder were used for the grinding grains. Two centrifuge one of Remi
and other of Tarson table top was used for centrifugation. Anhydrous MgSO4,
Na2SO4, Acetonitrile were procured from Fisher
Scientific, PSA from bondesil, C-18 from Varian Inc. USA
Sample preparation
A slight modification in QuEChERS method resulted in
the method being followed in this paper. Sample processing includes grinding,
pulverizing and mixing homogenously of grain sample to get the coarser size of
material. Samples were weighed (10g ± 1mg) in 50 ml polypropylene centrifuge
tubes (Tarson). Three replicates of each spiking level viz. 1 LOQ, 5 LOQ and 10
LOQ were prepared. Spiking of cereal samples were done by mixed standard
solution of 100 LOQ. 10g of anhydrous MgSO4 and 10 ml of
Acetonitrile was added to spiked samples. Samples were mixed and shaken
vigorously on vortex shaker for 1 minute then centrifuge at 4500 rpm for 5
minutes. 1 ml of supernatant was taken in 1.5 ml polypropylene centrifuge tube
(Tarson) having 25 mg of anhydrous MgSO4, C-18, 150 mg of PSA, tubes
were capped and mixed properly by vortex shaker. Mixed content then subjected
to centrifugation at 3000 rpm for 1 minute. 200 µl of Supernatant were taken in
GC vials with insert for GC analysis.
Gas Chromatography Instrument
Gas chromatography with Electron capture detector
(GC-ECD, Shimadzu 2010 model) equipped with auto-sampler. For efficient
chromatographic separation and quantification of pesticides a DB-5 fused silica
capillary column (J and W Scientific Co., 5% Phenylated methyl siloxane, 30 m
length × 0.25 mm i.d. × 0.25 μm film thickness) was used. Temperature of
injector was set at 280oC in split ratio of 10:1. Total gas flow in
injector was14.0ml/min with linear velocity of 30.7 cm/sec. The detector
temperature was set at 300oC at a current of 1.0 nA and makeup gas
flow at 30.0 ml/min. Nitrogen as carrier gas was set at a flow rate of 1
ml/min. Better chromatographic separation was observed using an oven
programming of initial temperature 170oCfor 5 min, followed by a
ramp rate of 5oC/min. upto final temperature of 280oCwith
a hold time of 10 min.
Another Gas Chromatographic instrument equipped with
Mass Selective Detector (GC-MSD, GC-QP 2010 plus MSD model) andsame column
(DB-5, fused silica capillary column of J and W Scientific Co.) as used in GC-ECD
was used for confirmation of the target pesticides. Inert gas Helium was used
as carrier gas at a flow rate of 1 ml/min.Oven programming for GC-MSD was kept
same as of GC-ECD instrument except splitless mode of injection and solvent
delay time of 6.5 min. Temperature of interface and ion source were set at 300
and 230oC, respectively. MS Quadruple temperature was set at 150oC
at emission current of 300 μA. Ionization mode was Electron Impact (EI)
with electron energy of 70 eV. The analyses were done at both Selective Ion
Monitoring (SIM) and full Scan mode for enhanced sensitivity and selectivity.
Method Development and Validation
Seven basic parameters required to develop and
validate a method studied were: linearity, accuracy/recovery, repeatability/
reproducibility, specificity/selectivity, limit of detection, limit of
quantitation, and robustness/ ruggedness.
Linearity
Linearity of GC-ECD instrument was assessed at 6
points calibration curve of matrix matched standard calibration, prepared by
spiking pesticide mixture solution at different concentration levels in blank
sample extract. Calibration curve were plotted by plotting an area of
individual pesticide against six different concentration levels of 0.005,
0.010, 0.050, 0.100, 0.250, 0.500 mg/Kg with regression co-efficient (r2)
varying from 0.9993 to 0.9756. Table1.0 given below presents the r2
value of each pesticide.
Accuracy/ Recovery
Accuracy of method was measured in terms of recovery.
Recovery of sample preparation method was determined by spiking the 10g of
grounded and homogenously mixed sample at 1, 5 and 10 LOQ level from a 100 LOQ
working standard pesticide mixture solution. The spiked sample in Tarson’s 50
ml centrifuged tubes was left for tumbling rotation on Tarson rotator for 1
hour time period before extraction to ensure homogenous mixing of spiked
solution and sample matrix. Samples were processed as per the method and analysed
quantitatively by GC-ECD using calibration curve’s linear equation model.
Repeatability and reproducibility
To study repeatability of the method samples analysed
for recovery studied were repeated for three times are R1, R2 and R3. Mean (M),
Standard Deviation (SD), and Relative Standard Deviation (RSD) of each
pesticide were calculated. Table 1.0 shows the r2 and recovery mean,
SD and RSD of each pesticide.
Specificity/Selectivity
Each pesticide is matched with its respective
retention time obtained from mix standard run on GC-ECD. In the above mentioned
GC method chromatographic peaks of the pesticides were well resolved and
observed no overlapping as shown in figure 1. In GC-MS each peak was confirmed
at above or equal to 85% matching to NIST library. Additional confirmation by
GC-MS was also done in Selective Ion Monitoring (SIM) mode where three
qualifier ions were considered along with R.T. match for each pesticide
molecule. table 2 shows the list of pesticides their R.T. (in cereal matrix)
and their respective qualifier ions figure 2, shows the GC-ECD chromatogram of
mix standard run of 25 pesticides and their degradation products. Retention
time of each pesticide is mentioned in table 1.0. Matrix effect in GC-ECD was
found minimal (figure 2) so the spiked sample (at 2 LOQ) gave distinct peaks
for each pesticide.
Limit of Detection (LOD) and Limit of
Quantification (LOQ):
LOD and LOQ was measured by using EPA
method as it is simple, easy and practical to implement [29-30]. To
measure the LOD peak to peak noise (Np-p)blank of
blank matrix (cereal) at or around the R.T. of individual pesticides
chromatogram of standard mix were noted and averaged for three replicates.
Concentration of the individual pesticide was calculated (in µg/g) from the
matrix spiked chromatogram which could produce the signal equal to three times
of (Np-p)blank..LOQ was calculated by multiplying
the LOD value by factor 3 round of to two decimal place value. LOD and LOQ
values of individual pesticides and their metabolites are given in table 2.
Fig. 1 GC-ECD Chromatogram of 25 pesticides and metabolites.
Uncertainty calculation:
Combined uncertainty (U) was calculated at
the level of 5 LOQ as per the statistical procedure mentioned in the
EURACHEM/CITAC Guide CG 4[28]. Uncertainty was calculated from the 3
sources namely:
1) Relative standard uncertainty of
analytical standards (U1):
Uncertainty associated due to the purity
percentage of the certified reference material as the confidence level at which
purity was measured was not provided in the certificate of CRM. To calculate
standard uncertainty (SU1) normal distribution was assumed in the below
equation,
where,
Uc is the uncertainty value of
the CRM purity percentage mentioned in the certificate, Standard uncertainty
(SU1) was used to calculate the Relative standard uncertainty (U1) by the
formula:
2) Relative standard uncertainty in weighing (U2):
Assuming normal distribution for uncertainty caused by
weighing was calculated by formula:
Where,
Ub is the uncertainty of weighing balance
at 95 % confidence level which is 0.0001, Wi is the weight measured
(mg) of individual pesticide.
3) Relative standard uncertainty associated with
precision (U3):
Uncertainty associated to the precision or
repeatability of results are due to the random errors in the sample processing
and instrumental analysis. To measure U3 three replicate recoveries were
calculated for its Mean (m), Standard deviation (SD)
Where,
n = replicates (here n=3)
Combined uncertainty (U) at confidence level of 95 %
was calculated by multiplying mean(m) with square root of squared sum of
individual relative uncertainties, equation shown below
Expanded uncertainty (2U), which is twice the value of
combined uncertainty (U) was used in reporting of results. Uncertainties values
of each pesticide was shown in table 3.0
RESULTS AND DISCUSSION:
Selected 25 pesticides belong to groups-
organ chlorine (14), organophosphate (7), synthetic pyrethroid (4) are common
in agriculture use for cereal crop protection from pest infestation within the
Indian subcontinent. Group also includes some of the degradation products of
pesticides which are toxic and usually found in the food matrix as metabolite
of parent pesticide molecule. Metabolites of those pesticides which had been
banned for agriculture use are also commonly found in food matrices because of
their persistent nature. Optimization of GC parameters like; oven temperature
programming, carrier gas flow rate and split control were done to get the
better chromatographic separation in run time of 35 minutes. GC-ECD instrument
gave better peaks separation, peak shape at lower concentration (i.e. 2 LOQ) in
matrix (figure 2). ECD response to blank matrix (wheat) was minimal and did not
pose problems false positive peak in spiked samples (figure 3).
Fig. 2 GC-ECD chromatogram of Cereal spiked at 2 LOQ.
Fig. 3 GC-ECD chromatogram of Wheat matrix
(Control)
Limit of Detection (LOD) and Limit of
Quantification (LOQ) was calculated as the lowest concentration a pesticide in
a selected matrix which gave signal to noise (S/N) ratio of approximately
equals to 3 and 10 respectively. Both LOD and LOQ values of the pesticides in
the cereal matrix were found below the MRL value of individual pesticides set
by Codex-alimenarius-WHO for wheat. table 2.0 presents the LOD and LOQ values
of individual pesticide calculated for wheat matrix. Lowest values of LOD and
LOQ were observed for Organochlorines and synthetic pyrethroid group pesticide.
However, in organophosphates the LOD and LOQ values of some pesticides like
-Quinolphos, Malathion, Phorate were found in slight higher in comparison to
Organochlorines and Organophosphate group. Six point calibration range set for
pesticides extend well over a range from lowest (0.005 µg/g) to higher
concentration of analyte (≥ 20 % of LOQ). Regression coefficient (r2)
of the pesticides varies from 0.9993 to 0.9756, presenting the greater order of
linearity in the instrument response to pesticides. Figure 3 shows the
calibration curve of one organochlorine pesticide, Endosulfan sulphate having
the (r2) = 0.9907 and linear equation with slope (m) = 3e+06 and
intercept (c) = 66639.
Matrix matched recovery studies were
performed to include the interferences (if any) from the cereal matrix. Mean
recovery from spiked samples (n=3)was found in Codex acceptable range
(67-128%) for 1, 5 and 10 LOQ, except Endosulfan-II of 10 and 5 LOQ having
mean recovery below 70%. However mean recovery of same pesticide for 1 LOQ was
found to be 90 %. The reason of low mean recovery in few pesticides could
possibly due to human errors during fortification or sample processing.
Relative Standard Deviation (RSD) for three replication (n =3) was calculated
(after discarding outliers) to be less than 20%, 17 %, 16% for 10, 5 and 1 LOQ
spiked studies respectively. Uncertainties in the result (U) caused due to
individual measurement uncertainty at each steps. Three major source of
uncertainties were chosen viz. uncertainty in purity of CRM, weighing, and
repeatability. Expanded uncertainty (2U) could be divided into three categories
based upon their percentage deviation from the actual real value viz. i) 1-10%,
ii) 11-15%, iii) 16-20%. Percentage deviation in U (2U%) of 64 % of selected
pesticides falls in first, 16% in second and rest 20% in third categories. 2U%
of Endosulfan-II showed was 19.6% due to poorest recovery, also other molecules
like alpha, gamma, delta isomers of HCH, showed higher 2U% because of higher 2U
values. This suggest that multi-residue method could be used best use for the
estimation of 21 (out of 25) pesticide residue in wheat matrix.
Table 3 presents the individual uncertainty
(U) along with 2U of each pesticide and their components U1, U2, U3.
Uncertainty component due to repeatability (U3) contributes more than 50% of
the total combined uncertainty (U).
Table 1 List of 25 standard pesticide mixture showing
retention time (R.T), regression coefficient (r2), recovery mean
(M), standard deviation (SD), relative standard deviation (RSD) at 1, 5 and 10
LOQ.
|
S.
No
|
Pesticide
|
R.T.
|
r2
|
1 LOQ
|
5 LOQ
|
10 LOQ
|
|
M
|
SD
|
RSD
|
M
|
SD
|
RSD
|
M
|
SD
|
RSD
|
|
1
|
Phorate
|
8.52
|
0.9835
|
84.68
|
3.36
|
3.97
|
80.79
|
4.69
|
5.81
|
97.59
|
1.69
|
1.73
|
|
2
|
Alpha-HCH
|
8.78
|
0.9920
|
67.62
|
2.26
|
3.34
|
82.03
|
12.04
|
14.68
|
85.57
|
14.49
|
16.94
|
|
3
|
Dimethoate
|
9.18
|
0.9958
|
97.77
|
3.34
|
3.42
|
113.01
|
0.89
|
0.78
|
115.60
|
3.80
|
3.29
|
|
4
|
Beta-HCH
|
9.71
|
0.9875
|
84.25
|
6.67
|
7.92
|
81.30
|
6.76
|
8.32
|
98.22
|
5.88
|
5.99
|
|
5
|
Gamma-HCH
|
10.03
|
0.9909
|
68.04
|
4.72
|
6.94
|
82.52
|
11.51
|
13.94
|
87.68
|
11.95
|
13.63
|
|
6
|
Delta-HCH
|
11.12
|
0.9944
|
59.17
|
4.26
|
7.19
|
87.80
|
13.41
|
15.27
|
76.68
|
1.30
|
1.69
|
|
7
|
Alachlor
|
12.49
|
0.9790
|
88.36
|
2.49
|
2.82
|
92.41
|
6.18
|
6.68
|
103.47
|
7.15
|
6.91
|
|
8
|
Malathion
|
13.84
|
0.9846
|
88.81
|
14.52
|
16.35
|
99.60
|
9.53
|
9.57
|
101.71
|
11.90
|
11.70
|
|
9
|
Chlorpyriphos
|
14.08
|
0.9756
|
102.78
|
2.56
|
2.49
|
99.02
|
4.69
|
4.73
|
105.71
|
13.69
|
12.95
|
|
10
|
Pendimethalin
|
15.39
|
0.9836
|
92.04
|
2.43
|
2.64
|
107.06
|
10.57
|
9.88
|
122.06
|
12.15
|
9.95
|
|
11
|
Quinalphos
|
16.08
|
0.9899
|
88.15
|
6.15
|
6.97
|
88.92
|
5.33
|
6.00
|
113.13
|
10.51
|
9.29
|
|
12
|
O,P-DDE
|
16.8
|
0.9883
|
102.81
|
5.77
|
5.61
|
90.51
|
5.19
|
5.74
|
100.07
|
2.01
|
2.01
|
|
13
|
Butachlor
|
16.98
|
0.9810
|
95.46
|
6.47
|
6.78
|
85.12
|
3.21
|
3.77
|
96.52
|
11.82
|
12.24
|
|
14
|
Endosulfan-I
|
17.21
|
0.9877
|
75.28
|
6.67
|
8.86
|
92.47
|
3.34
|
3.61
|
63.29
|
12.16
|
19.21
|
|
15
|
Profenophos
|
17.93
|
0.9865
|
88.82
|
3.38
|
3.81
|
101.77
|
5.78
|
5.68
|
99.94
|
8.07
|
8.08
|
|
16
|
P,P-DDE
|
18.08
|
0.9903
|
128.04
|
10.66
|
8.33
|
92.40
|
4.01
|
4.34
|
96.88
|
2.68
|
2.76
|
|
17
|
O,P-DDD
|
18.33
|
0.9846
|
96.71
|
5.98
|
6.19
|
86.91
|
5.46
|
6.29
|
99.53
|
3.12
|
3.14
|
|
18
|
Endosulfan-II
|
19.51
|
0.9874
|
94.87
|
1.17
|
1.24
|
47.79
|
8.12
|
16.98
|
53.22
|
8.66
|
16.27
|
|
19
|
O,P-DDT
|
19.78
|
0.9868
|
86.65
|
13.77
|
15.89
|
85.93
|
8.34
|
9.70
|
110.11
|
15.21
|
13.81
|
|
20
|
Endosulfan
Sulfate
|
20.97
|
0.9907
|
82.01
|
7.85
|
9.57
|
115.42
|
6.62
|
5.73
|
95.84
|
19.19
|
20.02
|
|
21
|
P,P-DDT
|
21.15
|
0.9911
|
81.70
|
5.09
|
6.23
|
95.34
|
8.19
|
8.59
|
124.80
|
9.79
|
7.84
|
|
22
|
Lamda
Cyhalothrin
|
25.03
|
0.9930
|
79.77
|
1.00
|
1.26
|
90.22
|
1.83
|
2.03
|
83.19
|
11.91
|
14.32
|
|
23
|
Cypermethrin
|
28.54
|
0.9946
|
92.02
|
6.39
|
6.94
|
97.34
|
14.63
|
15.03
|
91.90
|
12.43
|
13.53
|
|
24
|
Fenvalerate
|
30.95
|
0.9993
|
82.46
|
4.36
|
5.28
|
102.45
|
9.39
|
9.16
|
94.01
|
10.26
|
10.92
|
|
25
|
Deltamethrin
|
33.50
|
0.9993
|
98.39
|
11.34
|
11.52
|
109.72
|
2.32
|
2.11
|
67.90
|
5.10
|
7.51
|
M: Mean; SD: Standard Deviation; RSD:
Relative Standard Deviation
Table 2 Limit of Detection (LOD) and Limit of Quantification
(LOQ) of Pesticide measured using three ions for each pesticide molecule.
|
S.No.
|
Pesticide
|
R.T.
|
Qualifier Ions (m/z)
|
Limit of Detection
(LOD), µg/g
|
Limit of Quantification
(LOQ), µg/g
|
|
Q1
|
Q2
|
Q3
|
|
1
|
Phorate
|
8.52
|
75
|
121
|
260
|
0.04
|
0.12
|
|
2
|
Alpha
HCH
|
8.78
|
181
|
219
|
109
|
0.02
|
0.06
|
|
3
|
Dimethoate
|
9.18
|
87
|
93
|
229
|
0.02
|
0.06
|
|
4
|
Beta
HCH
|
9.71
|
109
|
181
|
183
|
0.02
|
0.06
|
|
5
|
Gamma
HCH
|
10.03
|
181
|
111
|
109
|
0.01
|
0.03
|
|
6
|
Delta
HCH
|
11.12
|
181
|
109
|
145
|
0.009
|
0.03
|
|
7
|
Alachlor
|
12.49
|
269
|
160
|
188
|
0.03
|
0.09
|
|
8
|
Malathion
|
13.84
|
173
|
125
|
127
|
0.03
|
0.09
|
|
9
|
Chlorpyriphos
|
14.08
|
97
|
197
|
314
|
0.008
|
0.02
|
|
10
|
Pendimethalin
|
15.39
|
281
|
252
|
191
|
0.02
|
0.06
|
|
11
|
Quinalphos
|
16.08
|
146
|
157
|
156
|
0.06
|
0.20
|
|
12
|
O,P-DDE
|
16.8
|
246
|
176
|
316
|
0.007
|
0.02
|
|
13
|
Butachlor
|
16.98
|
160
|
176
|
188
|
0.01
|
0.03
|
|
14
|
Endosulfan-I
|
17.21
|
241
|
195
|
265
|
0.002
|
0.01
|
|
15
|
Profenophos
|
17.93
|
374
|
337
|
208
|
0.004
|
0.01
|
|
16
|
P,P-DDE
|
18.08
|
246
|
176
|
316
|
0.002
|
0.01
|
|
17
|
O,P-DDD
|
18.33
|
235
|
165
|
237
|
0.003
|
0.01
|
|
18
|
Endosulfan-II
|
19.51
|
241
|
195
|
160
|
0.002
|
0.01
|
|
19
|
O,P-DDT
|
19.78
|
235
|
165
|
199
|
0.002
|
0.01
|
|
20
|
EndosulfanSulfate
|
20.97
|
272
|
229
|
170
|
0.003
|
0.01
|
|
21
|
P,P-DDT
|
21.15
|
235
|
165
|
176
|
0.004
|
0.01
|
|
22
|
LamdaCyhalothrin
|
25.03
|
181
|
197
|
208
|
0.004
|
0.01
|
|
23
|
Cypermethrin
|
28.54
|
163
|
181
|
208
|
0.006
|
0.02
|
|
24
|
Fenvalerate
|
30.95
|
125
|
167
|
419
|
0.004
|
0.01
|
|
25
|
Deltamethrin
|
33.5
|
172
|
181
|
253
|
0.005
|
0.01
|
Table 3 Uncertainties values of each pesticide and
their metabolites.
|
S.no.
|
Pesticide
|
Purity
|
Wt. std.
|
Uncertainty
|
SU1
|
U1
|
U2
|
U3
|
|
1
|
Phorate
|
96
|
1.24
|
0.005
|
0.0029
|
0.0030
|
4.032E-05
|
0.0335
|
|
2
|
Alpha
HCH
|
99.8
|
1.51
|
0.005
|
0.0029
|
0.0029
|
3.333E-05
|
0.0847
|
|
3
|
Dimethoate
|
99.6
|
1.37
|
0.005
|
0.0029
|
0.0029
|
3.649E-05
|
0.0045
|
|
4
|
Beta
HCH
|
99.2
|
1.78
|
0.005
|
0.0029
|
0.0029
|
2.809E-05
|
0.0480
|
|
5
|
Gama
HCH
|
99.5
|
3.68
|
0.005
|
0.0029
|
0.0029
|
1.359E-05
|
0.0805
|
|
6
|
Delta
HCH
|
99.5
|
1.15
|
0.005
|
0.0029
|
0.0029
|
4.348E-05
|
0.0882
|
|
7
|
Alachlor
|
99.4
|
1.21
|
0.005
|
0.0029
|
0.0029
|
4.132E-05
|
0.0386
|
|
8
|
Malathion
|
98.5
|
1.28
|
0.005
|
0.0029
|
0.0029
|
3.906E-05
|
0.0553
|
|
9
|
Chlorpyriphos
|
99.6
|
1.12
|
0.005
|
0.0029
|
0.0029
|
4.464E-05
|
0.0273
|
|
10
|
Pendimethlin
|
100
|
1.1
|
0.005
|
0.0029
|
0.0029
|
4.545E-05
|
0.0570
|
|
11
|
Quinalphos
|
99.4
|
3.64
|
0.005
|
0.0029
|
0.0029
|
1.374E-05
|
0.0346
|
|
12
|
O,P
DDE
|
99.5
|
2
|
0.005
|
0.0029
|
0.0029
|
2.500E-05
|
0.0331
|
|
13
|
Butachlor
|
97.7
|
1.28
|
0.005
|
0.0029
|
0.0030
|
3.906E-05
|
0.0218
|
|
14
|
Endosulphan-I
|
99.6
|
1.24
|
0.005
|
0.0029
|
0.0029
|
4.032E-05
|
0.0209
|
|
15
|
Profenophos
|
96.9
|
1.77
|
0.005
|
0.0029
|
0.0030
|
2.825E-05
|
0.0328
|
|
16
|
P,P-DDE
|
99.5
|
2.95
|
0.005
|
0.0029
|
0.0029
|
1.695E-05
|
0.0251
|
|
17
|
O,P-DDD
|
99.5
|
1.57
|
0.005
|
0.0029
|
0.0029
|
3.185E-05
|
0.0363
|
|
18
|
Endosulphan-II
|
99.5
|
1.5
|
0.005
|
0.0029
|
0.0029
|
3.333E-05
|
0.0981
|
|
19
|
O,P-DDT
|
99.3
|
2.34
|
0.005
|
0.0029
|
0.0029
|
2.137E-05
|
0.0560
|
|
20
|
Endosulphan
sulphate
|
98.8
|
1.28
|
0.005
|
0.0029
|
0.0029
|
3.906E-05
|
0.0331
|
|
21
|
P,P-DDT
|
99.7
|
1.22
|
0.005
|
0.0029
|
0.0029
|
4.098E-05
|
0.0496
|
|
22
|
Lamda
Cyhalothrin
|
99
|
1.77
|
0.005
|
0.0029
|
0.0029
|
2.825E-05
|
0.0117
|
|
23
|
Cypermethrin
|
97.2
|
1.94
|
0.005
|
0.0029
|
0.0030
|
2.577E-05
|
0.0868
|
|
24
|
Fenvalarate
|
99
|
1.08
|
0.005
|
0.0029
|
0.0029
|
4.630E-05
|
0.0529
|
|
25
|
Deltamethrin
|
98.9
|
1.48
|
0.005
|
0.0029
|
0.0029
|
3.378E-05
|
0.0122
|
Table:-3 Contd...
|
S.no.
|
Pesticide
|
SD Recovery
|
Mean Recovery
|
U
|
2U
|
|
1
|
Phorate
|
0.028
|
0.485
|
0.0163
|
0.0326
|
|
2
|
Alpha
HCH
|
0.042
|
0.287
|
0.0243
|
0.0487
|
|
3
|
Dimethoate
|
0.002
|
0.283
|
0.0015
|
0.0030
|
|
4
|
Beta
HCH
|
0.017
|
0.244
|
0.0117
|
0.0235
|
|
5
|
Gama
HCH
|
0.020
|
0.124
|
0.0100
|
0.0199
|
|
6
|
Delta
HCH
|
0.025
|
0.132
|
0.0116
|
0.0232
|
|
7
|
Alachlor
|
0.048
|
0.370
|
0.0143
|
0.0286
|
|
8
|
Malathion
|
0.005
|
0.498
|
0.0276
|
0.0551
|
|
9
|
Chlorpyriphos
|
0.026
|
0.099
|
0.0027
|
0.0054
|
|
10
|
Pendimethlin
|
0.053
|
0.268
|
0.0153
|
0.0306
|
|
11
|
Quinolphos
|
0.005
|
0.889
|
0.0309
|
0.0618
|
|
12
|
O,P
DDE
|
0.005
|
0.091
|
0.0030
|
0.0060
|
|
13
|
Butachlor
|
0.001
|
0.128
|
0.0028
|
0.0056
|
|
14
|
Endosulphan-I
|
0.003
|
0.018
|
0.0004
|
0.0008
|
|
15
|
Profenophos
|
0.003
|
0.051
|
0.0017
|
0.0033
|
|
16
|
P,P-DDE
|
0.001
|
0.018
|
0.0005
|
0.0009
|
|
17
|
O,P-DDD
|
0.003
|
0.043
|
0.0016
|
0.0032
|
|
18
|
Endosulphan-II
|
0.003
|
0.017
|
0.0016
|
0.0033
|
|
19
|
O,P-DDT
|
0.002
|
0.017
|
0.0010
|
0.0019
|
|
20
|
Endosulphan
sulphate
|
0.003
|
0.058
|
0.0019
|
0.0038
|
|
21
|
P,P-DDT
|
0.004
|
0.048
|
0.0024
|
0.0047
|
|
22
|
Lamda
Cyhalothrin
|
0.001
|
0.045
|
0.0005
|
0.0011
|
|
23
|
Cypermethrin
|
0.015
|
0.097
|
0.0084
|
0.0169
|
|
24
|
Fenvalarate
|
0.005
|
0.051
|
0.0027
|
0.0054
|
|
25
|
Deltamethrin
|
0.002
|
0.110
|
0.0014
|
0.0028
|
CONCLUSION:
It is concluded that developed sample processing
method for pesticide residue in wheat matrix is fast, simple and cost effective
in comparison to the original QuEChERS method. Instrument parameters for the
analysis were also the suitably optimized for the pesticide residue analysis in
wheat matrix. The method have been validated by the required data of linearity,
accuracy, precision, sensitivity and specificity. The data obtained were in
acceptable range of validation limits for in house method development and
validation purpose. The proposed method left scope for further investigation
and comparison of the quantification of pesticides between GC-MS (SIM mode) and
GC-ECD. This method/work can be extended by the inter-lab validation data,
enhancing the scope by increasing the number of pesticide and their degradation
product in each class, testing the method effectiveness on other cereals like
oat, barley, maize etc.
ACKNOWLEDGEMENT:
The authors are highly grateful to the Director,
Institute of Pesticide Formulation Technology for allowing them to use the
Research Facilities for work.
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