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Multi-residue Method for Pesticides Residue Analysis in Grapes by LC-MS/MS Chapter 2 Published in J.Chromatography A, 1173 (2007) 98–109

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Multi-residue Method for Pesticides Residue Analysis in Grapes by LC-MS/MS

Chapter 2

Published in J.Chromatography A, 1173 (2007) 98–109

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Chapter -2

Multi-residue Method for

Pesticides Residue in Grapes by

LC-MS/MS

[ Published in Journal of Chromatography A, 1173 (2007) 98–109]

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ABSTRACT

A method was validated for the multi-residue analysis of 82 pesticides in

grapes at ≤25 ng/g level. Berry samples (10 g) mixed with sodium sulphate (10 g)

were extracted with ethyl acetate (10 mL); cleaned by dispersive solid phase

extraction and the results were obtained by liquid chromatography–tandem mass

spectrometry. Reduction in sample size and proportion of ethyl acetate for extraction

did not affect accuracy or precision of analysis when compared to the reported

methods and was also statistically similar to the QuEChERS technique. The method

was rugged (HorRat<0.5) with <20% measurement uncertainties. Limit of

quantification was <10 ng/g with recoveries 70–120% for most pesticides. The

method offers cheaper and safer alternative to typical multi-residue analysis methods

for grape.

2.1 INTRODUCTION

Grape is an important fruit crop in India, the commercial cultivation of which

receives frequent application of a large number of pesticides throughout the cropping

season to control a variety of pests and diseases. Pesticide residue is a major concern

for the stakeholders of the grape industry, since the quality regulations and food

safety standards are becoming more stringent in most countries. The management of

pesticide residues in grapes is challenging because, besides direct application,

pesticide residues may also appear in grape berries from indirect sources like soil,

contaminated agro-inputs (e.g. manures, fertilizers, growth regulators, irrigation

water, etc.), drift from adjoining fields of other agricultural crops, etc. This is the

reason why around 100 chemicals are regularly monitored in all the grape samples of

Indian origin for export, although the number of recommended chemicals for use in

grapes consists only of 45[1]. Considering the importance of food safety, the

Government of India has made the certificate of residue analysis a mandatory pre-

requisite for issuance of a phytosanitary certificate for export[2]. The pesticides in the

monitoring list belong to diversified chemical classes and the testing laboratories in

India used to analyze them in multiple groups by GC and HPLC-based methods,

requiring a long time to complete the analysis. But the present situation demands a

rapid turn-around time – where the analysis needs to be completed within 24–48 h

after a sample is submitted to a laboratory. Thus, it is necessary to have an analytical

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method by which we can simultaneously determine the target pesticides in any

sample by a single effort with equivalent or superior overall efficiency.

The QuEChERS (quick, easy, cheap, effective, rugged and safe) method is

well known for its applicability in simultaneous analysis of a large number of

pesticides in a variety of food matrices[3–5]. The method has already received

worldwide acceptance because of the simplicity and high throughput enabling a

laboratory to process significantly larger number of samples in a given time as

compared to the earlier methods[6–8]. Recently, the QuEChERS method has received

the distinction of an AOAC official method for multiple pesticides in fruits and

vegetables[9]. The application of HPLC hyphenated to tandem mass spectrometry

(LC–MS/MS) in multi-class pesticide residue analysis has made the multi-residue

analysis more rugged and convenient by offering the possibility of simultaneous

determination of a large number of pesticides with varied physico-chemical

properties without the need for chromatographic baseline separation. It has

minimized the requirement for extensive sample cleanup, which was otherwise

essential in earlier methods of analysis.

In the development of the QuEChERS method, Anastassiades et al.[3] found

that ethyl acetate also gave high recoveries of pesticides in many fruits and

vegetables and few co- extractives, but they ultimately chose acetonitrile due to its

compatibility with HPLC and better performance for pH-dependent pesticides.

Mastovska and Lehotay [4] evaluated and compared the suitability of six organic

solvents for pesticide residue analysis and stability of multi-class pesticides and

identified acetonitrile as the most suitable extraction solvent for a variety of matrices.

Ethyl acetate is equally acceptable as extraction solvent for different products[10–15],

since it does not pose limitations in terms of lipid co-extractives. It is especially

suitable for the extraction of high-sugar commodities like grape since sugar has

limited solubility in ethyl acetate. Furthermore, substitution of acetonitrile by ethyl

acetate can substantially reduce the input cost of analysis.

Objective

In this work, we report the complete single laboratory validation of a multi-

residue analysis method based on ethyl acetate extraction followed by simultaneous

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determination of 82 pesticides in grapes by LC–MS/MS with good selectivity, high

sensitivity and a wide application scope.

2.2 MATERIALS AND METHODS

2.2.1 Chemicals and Apparatus

Certified reference standards of all the test pesticides were of >98% purity

and purchased from the Ehrenstorfer GmbH, Augsburg, Germany. All the solvents,

namely ethyl acetate, acetonitrile, methanol and water were HPLC grade. Primary

secondary amine (PSA, 40µm, Bondesil) sorbent was purchased from Varian Inc.,

USA. The other reagents namely anhydrous sodium sulphate and magnesium

sulphate (dried) were of analytical reagent grade and purchased from the Merck India

Ltd. (Mumbai, India). These were activated by heating at 650 ◦C for 4 h before use

and kept in desiccators.

An Agilent 1200 series HPLC system (Agilent Technologies, USA)

hyphenated to an API 4000 Q-Trap mass spectrometer (Applied Biosystems, MDS

sciex, Canada) was used, which was equipped with an electrospray ionization

interface set at positive polarity. A high-speed homogenizer (DIAX-900, Heidolph,

Germany), low-volume concentrator (TurboVap LV, Caliper Life Sciences,

Russelsheim, Germany), non-refrigerated centrifuge (Eltek, Mumbai, India) and a

microcentrifuge (Microfuge Pico, Kendro D-37520, Osterode, Germany) were used

for residue analysis.

2.2.2 Selection of Pesticides

The selected pesticides included all the insecticides, fungicides and

herbicides, which are currently recommended in Indian viticulture as well as those,

which require monitoring in samples for export to the European Union countries[1]. A

total of 115 pesticides were thus initially considered for this study, out of which 82

pesticides were finally found to be suitable for LC–MS/MS analysis. The names of

these pesticides are presented in Table 2.1 along with their corresponding chemical

classes.

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2.2.3 Preparation of Standard Solutions

The stock solutions of the individual pesticide standards were prepared by

accurately weighing 10 mg (±0.01 mg) of each analyte in volumetric flasks (certified

‘A’ class) and dissolving in 10 mL methanol. These were stored in dark vials in a

refrigerator at 4◦C. An intermediate stock standard mixture of 10 mg/L was prepared

by mixing the appropriate quantities of the individual stock solutions followed by

requisite volume makeup. A working standard mixture of 1 mg/L was prepared by

diluting the intermediate stock standard solution, from which the calibration

standards within the range 0.1–50.0 ng/mL were prepared by serial dilution with

methanol–water (1:1, v/v).

2.2.4 Method Validation

The analytical method was validated as per the single laboratory validation

approach of Thompson et al. [16]. The quantification was based on nine-point

calibration graph obtained by plotting the area ratio of the daughter ion of the

individual target compounds to that of the internal standard (triphenyl phosphate at

25 ng/mL methanol) against concentrations of the calibration standards. The limit of

detection (LOD) of the test compounds was determined by considering a signal to

noise ratio of 3 with reference to the background noise obtained from blank sample,

whereas, the limits of quantifications (LOQs) were determined by considering a

signal to noise ratio of 10.

a. Sample Size- The sample size for extraction was decided by experimentation

under ambient condition over three different days. On each day, fresh berries (2 kg

sample) were treated with the test pesticide mixture (25 ng/g) and macerated

thoroughly in a blender. From this macerated mass, we drew 200 and 400 g samples

in separate sets and further homogenized in a high speed homogenizer at 15,200 rpm

for 2 min. The extent of homogeneity was evaluated by analyzing 10 random

portions (10 and 25 g samples drawn from 200 and 400 g homogenized masses,

respectively) of the homogenized mass for the concentration of the test compounds

by LC–MS/MS. The two sets of data (for 10 and 25 g) over 3 different days were

compiled and subjected to statistical evaluation by Student’s t-test. The relative

standard deviations for each data set were also compared.

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b. Selection of Extraction Solvent- Two organic solvents namely ethyl acetate and

acetonitrile were evaluated for their extraction efficiency. Ethyl acetate was used at

sample–solvent ratio of 1:1 (w/v) and 1:2 (w/v) and the results were compared. In

case of the acetonitrile extraction, the QuEChERS technique [3] of sample preparation

was directly adopted and the results were used as standard checks to compare the

efficiency of the current method of ethyl acetate extraction.

c. Sample Preparation Technique –Standardization- The entire sample (2 kg berry

only) was homogenized in two steps as described in section a. The samples (10 g)

were extracted with ethyl acetate (10 mL) plus anhydrous sodium sulphate (10 g) by

homogenization followed by centrifugation at 2000 rpm for 5 min. For 25 g sample,

the quantity of ethyl acetate and sodium sulphate were 25 mL and 25 g, respectively.

An aliquot of 5 mL was drawn from the supernatant and cleaned by dispersive solid

phase extraction (DSPE) with PSA (25 mg). The cleaned extract (4 mL) was placed

in a 10 mL test tube and to it 200 µL of 10% diethylene glycol (in methanol) was

added (as keeper as well as analyte protectanat) and mixed thoroughly by vortexing.

This mixture was subsequently evaporated to near dryness under a gentle stream of

nitrogen in a low volume concentrator at 35 oC. The residues were dissolved in a

mixture of 1 mL methanol and 1 mL of 0.1% acetic acid in water by sonication (1

min) followed by vortexing (30 s). This solution was centrifuged at 10,000 rpm for 5

min. This supernatant was filtered through 0.2 µm polyvinylidene fluoride (PVDF)

membrane filters and then analyzed by LC–MS/MS. Fresh grapes, which did not

receive any treatment of the test pesticides were used as blank. In this method, acetic

acid was added to prevent pH-dependent degradation of certain compounds (e.g.

organophosphates) during sample preparation.

d. Extraction of Spiked Samples- The extraction efficiency of the above batches

were compared with separate sets where 10 and 25 g samples were extracted with 20

mL (+10 g sodium sulphate) and 50 mL (+25 g sodium sulphate) ethyl acetate,

respectively keeping other steps same as described above. A separate batch was

analyzed by the QuEChERS technique for comparison, where 10 g sample was

extracted with 10 mL acetonitrile, followed by liquid–liquid partitioning with a

mixture of 4 g anhydrous magnesium sulphate and 1 g sodium chloride. The sample

clean up was performed by DSPE with 150 mg anhydrous magnesium sulphate and

25 mg PSA and the final analysis was done by LC–MS/MS[3]. We did not use the

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modified QuEChERS as per the AOAC official method 2007.01[9] to maintain

absolute comparability in terms of the sample size and other conditions intact. All the

bove experiments were accomplished in ten replicates on 3 different days involving

two different analysts.

e. Precision- The precision in the conditions of repeatability (two different analysts

prepared six samples each on a single day) and intermediate precision (two different

analysts prepared six samples each on 6 different days) was determined separately

for a standard concentration of 25 ng g-1 of all the analytes. Horwitz ratio (HorRat)

pertaining to intra-laboratory precision, which indicates the acceptability of a method

with respect to precision[17,18], was calculated for all the pesticides by the following

way:

HorRat = RSD/PRSD ……………………………………………… (1)

where, RSD, relative standard deviation;

PRSD, predicted relative standard deviation = 2C−0.15;

where C is the concentration expressed as mass fraction (25 ng/g = 25×10−9).

f. Recovery Experiments- The recovery experiments were carried out on fresh

untreated grapes by fortifying the samples (10 g) in six replicates with pesticide

mixture separately at five concentration levels, i.e. 2.5, 5, 10, 25 and 50 ng/g (Table

2.2). The samples were extracted with 10 mL ethyl acetate. The recoveries obtained

(at 25 ng/g level) were compared with the results pertaining to the extractions by 20

mL ethyl acetate and 10 mL acetonitrile (QuEChERS), each done at six replicates

separately.

g. Evaluation of Matrix Effect- The matrix effect was assessed by employing

matrix-matched standards prepared in a similar fashion as described in section 2.2.3

with matrix extract of the untreated grape berries. The matrix extracts were at first

analyzed by LC–MS/MS to confirm the absence of the test pesticides in them before

spiking.

2.2.5 LC–MS/MS Analyses

The residue analyses were performed by liquid chromatography–tandem

mass spectrometry (LC–MS/MS). The HPLC separation was performed by injecting

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5 µL via autosampler on a Purosphere RP-18 (55 mm×2 mm×3 µm) column (Merck,

Germany) maintained at 35 oC at the flow rate of 0.3 mL min-1. The mobile phase

was composed of (A) methanol/water (20:80, v/v) with 5 mM ammonium formate

and (B) methanol/water (90:10, v/v) with 5 mM ammonium formate; gradient 0–1.0

min/90% A, 1–2 min/90%–5%A, 2–11 min/5%A, 11–12 min/5–90%A, 12–18

min/90%A. The MS parameters included ion spray voltage of 5500 V; nebulizer gas

30 psi; curtain gas 25 psi; heater gas 60 psi and the ion source temperature 500 oC.

The other parameters of LC–MS/MS analyses are presented in Table 2.1. Estimation

of the residues was performed by multiple reaction monitoring (MRM) with two

mass transitions for each test pesticide with dwell time of 20 ms; one for

quantification and the other for confirmation. The ion ratio for these two mass

transitions was used for unambiguous identification of each pesticide as per the

European Commission (EC) guidelines [19].

2.2.6 Assessment of Global Uncertainty

Global uncertainty in estimation was determined for all the pesticides at the

level of 25 ng g-1 as per the statistical procedure of the EURACHEM/CITAC Guide

CG 4[20]. Five individual sources of uncertainty were taken into account as described

below:

(i) Uncertainty associated with the calibration graph (U1) The uncertainty of the calibration curve was calculated at 25 ng/g according

to the following equation:

where s is the standard deviation of the residuals of the calibration curve, b1 is the

slope of the calibration curve, p is the number of measurements of the unknown, n is

the number of points used to form the calibration curve, c0 is the calculated

concentration of the analyte from the calibration curve, ´c is the arithmetic mean of

the concentrations of the standards used to make the calibration curve and sxx is

calculated as follows:

sxx = ∑ (cj – ć)2

U1 = (s/b1)[(1/p)+(1/n)+{(c0- ć)2/sxx}]1/2

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where j= 1, 2, …, n. cj is the concentration of each calibration standard used to build

up the calibration curve.

(ii) Uncertainty associated with precision

This contribution was further recognized as a composite of two sources, i.e.

precision due to different analysts (U2) and precision due to change in day (U3) of

analysis. These two sources of uncertainties were recognized keeping in mind that

they are independent of each other. The objective was to evaluate the effect of two

different kinds of analytical conditions on the performance of the analytical method.

Keeping that in mind the experiment was designed accordingly. The analyses were

carried out independently in two separate batches under same analytical conditions.

The first batch consisted of six analysts preparing the samples in three replicates in a

single day and the second batch consisted of a single analyst preparing the sample on

6 different days. The precision was calculated according to the following equations:

U2 = s1/n1/2,

where s1 is the standard deviation of the results obtained from a single analyst on

different days, and n is the number of assays.

U3 = s2/n1/2

where s2 is the standard deviation of the results obtained from different analysts on a

particular day, and n is the number of assays.

(iii) Uncertainty associated with accuracy/bias The contribution to bias was again recognized as a composite of two sources:

bias due to analyst change (U4) and the bias due to change in day of analysis (U5),

following the same argument for recognition of the two sources. Analysis was

carried out in the similar fashion described above. The bias was calculated according

to the following equations:

U4 = s1 (η)/n1/2, where s1(η) is the standard deviation of the percentage recoveries

obtained from a single analyst on different days, and n is the number of assays.

U5 = s2(η)/n1/2, where s2(η) is the standard deviation of the percentage recoveries

obtained from different analysts on a particular day, and n is the number of assays.

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The global uncertainty (U) was calculated as U = (U12+ U2

2+ U32+ U4

2+ U52)1/2 and

reported as expanded uncertainty which is twice the value of the global uncertainty.

The uncertainty values for each pesticide are reported as relative uncertainties in

Table 2.3.

2.3 RESULTS AND DISCUSSION

2.3.1 Selection of Pesticides

Out of the 115 pesticides selected for this work, 10 were unfit for the LC–

MS/MS analysis, which includes 6 dithiocarbamates, 3 copper and 1 sulphur-based

pesticide because of the limitations regarding their solubility, volatility, ionization

problem, stability, etc. Efforts were undertaken to optimize the tuning parameters of

all the other pesticides in the LC–MS/MS in flow injection mode and then their

analytical performances were tested in different scan modes. For the 16

organochlorines, 6 synthetic pyrethroids (with isomers) and 1 dinitrophenol (viz.

dinocap) pesticides, the sensitivity in LC–MS/MS was not adequate to comply with

the international MRL regulations [21–24] and hence these were selected for GC–MS

analyses. For the other 82 pesticides belonging to different chemical classes (Table

2.1), LC–MS/MS multiple reaction monitoring gave excellent performance in terms

of peak shape, linearity, sensitivity, etc. The detailed MS/MS parameters are

presented in Table 2.1.

2.3.2 Method Validation

All the 82 pesticides could be analyzed in single chromatographic run of 18

min (Figure 2.1). The dwell time of 20 ms was found to be optimum for all the

compounds. Lowering the dwell time below 20 ms could accommodate more mass

transitions, but the sensitivity was sacrificed. Sensitivity remained same for most of

the pesticides for the dwell time range of 20–50 ms (Figure 2.2) and since all the

pesticides could be detectable at 5–10 ng/mL or even at lower level with 20 ms dwell

time, we decided to fix it for our method of multi-residue analysis. Linearity of the

calibration curve was established for all the pesticides. The correlation coefficient

(R2) of the calibration curve, both for pure solvent-based as well as matrix-matched

were >0.99 for all the compounds (Table 2.1). The LOD and LOQ for the test

pesticides are presented in Table 1. The matrix effect was prominent for a large

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number of pesticides. As for example, an overall suppression of the detector response

by above 30% was observed for organophosphorus pesticides like methamidophos,

acephate, phosalone, etc., whereas for triazole compounds, signal suppression was up

to 20%. Considering the variable matrix influences for different compounds in

mixture, the matrix-matched calibrations were used for all quantification purposes to

avoid any over or under-estimation of residues. The extent of homogenization was

quite satisfactory with relative standard deviation less than 2% for every compound

analyzed in ten replicates over 3 days. We also compared the effect of two-step

homogenization (described in section 2.2.3.c) with one step homogenization (10 g

samples were directly drawn after blending 2 kg fresh grapes and then analyzed in 10

replicates) on the precision of the results. The overall precisions improved in terms

of the relative standard deviations, which reduced from >7% with one-step

homogenization to <3% with two-step process. Mol et al.[10] recommended 40 mL

ethyl acetate to extract 25 g (fruits or vegetable) samples. We attempted to further

reduce the sample size from 25 to 10 g to minimize the use of organic solvent for

extraction. The recovery results for 25 and 10 g sample sizes were similar as

concluded by the Student’s t-test. The relative standard deviations were also

statistically similar for both 10 and 25 g samples and accounted for less than 3% in

both cases. Since the reduction in sample size did not affect the recoveries or

increase the error in estimation, we concluded these two sample sizes as equivalent

to each other in the case of grapes.

Our next endeavour was to find whether the sample–solvent ratio of 1:2 could

be reduced to achieve a more concentrated extract. Earlier, Mol et al. successfully

reduced the sample–solvent ratio from 1:2[11,13] to 1:1.6[10]. We further reduced the

ratio to 1:1 with a smaller sample size of only 10 g. For comparison, we extracted 10

g sample (spiked at 25ng g-1) with 10 and 20 mL ethyl acetate in separate batches,

cleaned the extracts by DSPE with PSA and compared the recoveries in 10 replicates

each on 3 different days. The recoveries in all these experiments were statistically

equivalent on the basis of the Student’s t-test (Figure 2.3). Thus, we decided to fix

the sample size as 10 g and used 10 mL ethyl acetate for extraction. This effort also

could offer higher signal-to-noise (S/N) ratio when compared to the corresponding

extract obtained with 20 mL ethyl acetate. Use of PSA for cleanup reduced the noise

level in matrix blank when compared to the samples processed without DSPE

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cleanup. This shows the efficiency of PSA in removing interfering substances like

fatty acids, etc. from a sample extract before injection into LC–MS/MS. It can also

improve the life of HPLC column (along with guard column), reduce the frequency

of cleaning the curtain plate, orifice, etc. and increase the life of the mass detector.

2.3.3 Ethyl acetate vs. Acetonitrile and Relative Matrix Influence

The results of the Student’s t-test performed on the comparative recoveries

obtained by using these two solvents showed that the recoveries of all the pesticides

were statistically on par at 95% level of confidence (Figure 2.3). In case of ethyl

acetate, the recoveries of highly polar pesticides like methamidophos and acephate

were less than 50% when quantified to pure solvent based calibration curve; but the

same recoveries reached to above 75% with good precision when quantified with

matrix-matched standards. In QuEChERS method with acetonitrile extraction,

however, the recoveries of methamidophos and acephate were above 85%. Thus,

selective extraction of matrix components in different solvents contributes to

different degree of matrix influence on different compounds. A relatively less

recovery of methamidophos and acephate might occur as a result of their limited

solubility in ethyl acetate as compared to acetonitrile. The recoveries of all the

pesticides increased with increase in fortification levels (Table 2.2). For two

pesticides namely metribuzin and temephos, the recoveries were below 60%.

Metribuzin has amino group in its structure, which might hinder its extraction in

relatively non-polar ethyl acetate. On the other hand, temephos is highly non-polar in

nature and that might restrict its extraction in ethyl acetate. Although the recoveries

of the above compounds were less, they were consistent as observed from their

standard deviations at any of the fortification levels (Table 2.2). In all these

compounds, however, the QuEChERS gave higher (>80%) recoveries. In case of

QuEChERS, when acetonitrile mixes with water in presence of salts, a

thermodynamic shift causes acetonitrile to become immiscible with water and as a

consequence, the non-polar analytes are driven towards the acetonitrile phase

resulting in higher recoveries. Precision in terms of HorRat (single laboratory) at 25

ng/g level were less than 0.5 for all the pesticides (Table 2.2), indicating satisfactory

repeatability and ruggedness of the methodology [17,18]. In the case of ethyl acetate

extraction, although there was a requirement of solvent exchange to methanol–water

for injection into the LC–MS system, the evaporation process was quite easy and

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fast. Typically, 50 samples could be evaporated simultaneously in a low-volume

concentrator within less than 20 min. Addition of diethylene glycol improved the

recovery by preventing the losses of analytes during evaporation. Solvent exchange

to methanol–water also prevented band broadening as observed in case of

QuEChERS estimation of highly polar analytes like methamidophos, which is also

reported in literature [10]. Ethyl acetate is also economically cheaper and

toxicologically safer than acetonitrile and thus found more appropriate for extraction

of matrix-like grape, which contains high sugar and less fat.

2.3.4 Selection of LC–MS/MS Conditions

Continuous infusion of each compound was carried out in positive and

negative ionization modes by an ESI source. Full scan mass spectra were recorded in

order to select the most abundant mass fragments. The relative intensity for the most

abundant m/z was used to evaluate the performance of each ionization mode. The

signal intensities obtained in the positive mode were high. Full scan daughter mass

spectra were obtained with continuous infusion of each analyte in product ion scan

mode. The most abundant product ion for each compound chosen for quantification

and the fragment with next relative abundance for confirmation is presented in Table

2.1. For each analyte, the values of the voltages applied to the ion source (ESI),

collision cell and quadrupoles were optimized in the MRM mode by continuous

infusion in order to achieve the highest sensitivity as possible. The optimization of

the nebulizing gas, auxiliary gas and curtain gas pressure further improved the

sensitivity. In matrix blanks, the peaks of the same mass transition were observed for

cymoxanil, iprovalicarb, paraoxon methyl and quinalphos. The intensity of the

interfering peaks from the matrix was almost 5–6 times higher in the case of

acetonitrile extraction. However, the confirmatory second mass transition was absent

in all these cases except cymoxanil, where both the mass transitions were matching

with a matrix component, although at a different retention time (RT).

2.3.5 Final Method of Sample Preparation

On the basis of the above findings, we finalized the sample preparation

method extracting homogenized grape samples (10 g) with 10 mL ethyl acetate (+10

g anhydrous sodium sulphate), cleanup by DSPE with PSA (25 mg) and final

analysis by LC–MS/MS after solvent-exchange to methanol–water (1:1). Selection of

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ethyl acetate offered precise advantages over acetonitrile in minimizing the matrix

components in the final extract and reducing the cost of analysis.

2.3.6 Measurement Uncertainty of Analyses

The total uncertainty was evaluated assuming that all the contributions were

independent of each other. A coverage factor of 2 was decided to evaluate the

expanded uncertainty at a confidence level of 95%. The expanded uncertainty of the

pesticides ranged up to a maximum of 20%, with 68 pesticides having uncertainties

≤10%. Uncertainties of 11 compounds ranged between 11 and 15%. Only three

compounds had uncertainties in the range 15–20%. The pesticides could be classified

into three groups on the basis of their range of expanded uncertainties (Table 2.3) as

follows:

Group I – uncertainty range up to 10%

Group II – uncertainty range up to 10–15%

Group III – uncertainty range up to 15–20%

The Group I pesticides are rugged and best suited to the method of sample

preparation and analysis. This group consisted of the pesticides that had low

uncertainties associated to precisions (in most cases below 3%) both on a day-to-day

basis as well as analyst-to-analyst basis. In addition, these compounds also had low

uncertainties associated with bias (mostly below 3%). Thus, overall expanded

uncertainties for these compounds were low. It can therefore be assumed that the

method selected for sample preparation and analysis is efficient and suitable for

determination of pesticides belonging to this group.

The Group II pesticides showed relatively higher level of expanded

uncertainties as compared to Group I. Uncertainties associated to precision (ranging

around 3% and above) and bias (ranging around 3% and above) were generally

higher than Group I. In Group I, higher uncertainty in precision was compensated by

the lower value of uncertainty in bias and vice versa. This was however not the case

for these pesticides. Equal contribution of uncertainties in both precision and bias led

to higher expanded uncertainties.

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The Group III pesticides consisted of temefos, paraoxon methyl, and

thiometon. When uncertainties in precision were considered, these compounds

showed uncertainties below 3% making them qualify for Group I pesticides.

However, the uncertainty in bias (in each case being above 5%) contributed hugely

towards the total and in turn expanded uncertainty, which is in conformity with

relatively high standard deviations for paraoxon methyl and thiometon and a poor

recovery of around 57% for temefos (Table 2.2). Thus, the method exhibited

relatively poor performance for these compounds because of relatively larger

uncertainties in bias, which might have occurred due to instability or incomplete

extraction. Taking into consideration the other benefits the method provides, special

attention is required in improving the recoveries of these compounds in future

endeavour.

2.3.7 Economics of Analyses

The total cost of sample preparation (inputs only; excluding the instrument

running cost, man-power and other overhead costs) was INR 25 per sample, which is

equivalent to approximately 0.6 US Dollar. It provided an overall savings to the tune

of around 30% per sample when compared to the QuEChERS technique. Our

estimate reflects that a single laboratory chemist on an average can alone process

around 25 samples in 8 working hours with the activities starting from weighing the

berries until the stage of ready-to-inject condition for LC–MS/MS analysis. The

relative output increases to above 30 per person per day when a group of chemists

work hand-in-hand, processing the samples in a chain mode. Hence, the current

method increases the overall turnover of a testing laboratory with superior benefit:

cost ratio and thus has promise to be accepted by the testing laboratories in their

regular residue analyses programs on grapes. The current method is also relatively

safer to the analysts due to less exposure to organic solvent.

2.4 CONCLUSION

The method gives distinct advantages over the typical techniques of multi-

residue analysis in grapes by minimizing the sample quantity to only 10 g followed

by extraction with only 10 mL of ethyl acetate despite ensuring satisfactory precision

and accuracy at residue level as low as 2.5 ng/g. LC–MS/MS MRM could analyze

the entire 82 test pesticides within a short chromatographic run of 18 min.

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Satisfactory recoveries (70–120%) were obtained below the level of 10 ng/g. The

method reduces the cost of analysis and also offers low level of measurement

uncertainty (<20%), indicating suitability to the requirements of the international

standards as per the ISO/IEC 17025 for laboratory accreditation [25].

2.5 REFERENCES

[1] APEDA (2006a), Regulation of export of fresh grapes from India through

monitoring of pesticide residues, Amendments in grape RMP –

2007,Amendment-5 (Revised Annexure – 7&11), http://www.apeda.com,

dated 7th February 2007, accessed on 30th September 2007.

[2] APEDA Regulation of export of fresh grapes from India through monitoring

of pesticide residues, Annexure 1: Circular No.91-4/95 PQD dated 29th

February 2000, http://www.apeda.com/apeda/GRAPES 05/ RMPGrapes 2007

17 oct06 3.pdf. (2006b).

[3] Anastassiades M, Lehotay S J, Stajnbaher Dˇ, Schenck F J, J. AOAC Int. 86,

412-431, (2003).

[4] Mastovska K, Lehotay S J, J. Chromatogr. A 1040, 259-272, (2004).

[5] Lehotay S J, de Kok A, Hiemstra M, Bodegraven P, J. AOAC Int. 88, 595-

614, (2005).

[6] Luke M, Froberg J E, Masumoto H T, J. Assoc. Off. Anal. Chem. 58, 1020-

1026, (1975).

[7] Stan H, Linkerhagner M, J. Chromatogr. A 750, 369-390, (1996).

[8] AOAC Official Method 985, Official Methods of Analysis (2000) 17th ed.

AOAC INTERNATIONAL, Gaithersburg, MD, p. 22. (2000)

[9] Lehotay S J, J. AOAC Int. 90, 485-520, (2007).

[10] Mol H G J, Rooseboom A, van Dam R, Roding M, Arondeus K, Sunarto S,

Anal. Bioanal. Chem. 389, 1715-1754, (2007)

[11] Banerjee K, Upadhyay A K , Adsule P G, Patil S H, Oulkar D P, Jadhav D R,

Food Addit. Contam. 23, 994-999, (2006).

[12] Fern´andez Moreno J L, Arrebola Li´ebanas F J, Garrido Frenich A, Martinez

Vidal J L, J. Chromatogr. A 1111, 97-105, (2006).

[13] Mol H G J, van Dam R C J, Steijger O M, J. Chromatogr. A 1015, 119-127,

(2003).

[14] Andersson A, Palshleden H, Fresenius J. Anal. Chem. 339, 365-367, (1991).

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59

[15] Diez C, Traag W A, Zomer P, Marinero P, Atienza J, J. Chromatogr. A 1131,

11-23, (2006).

[16] Thompson M, Ellison S L, Wood R, Harmonized guidelines for single

laboratory validation of methods of analysis. IUPAC Technical Report, Pure

Appl. Chem. 74, 835-855, (2002).

[17] Horwitz W, Albert R, J. AOAC Int. 89, 1095-1109 (2006).

[18] Horwitz W, Kamps L R, Boyer K W, J. Assoc. Off. Anal. Chem. 63, 1344-

1354 (1980).

[19] European Commission Decision 2002/657/EC of 12th August 2002

implementing Council Directive 96/23/EC concerning the performance of

analytical methods and the interpretation of results, Off. J. Eur. Communities

L 221, p. 8 17th August 2002.

[20] EURACHEM/CITAC Guide CG 4, EURACHEM/CITAC Guide,

Quantifying Uncertainty in Analytical Measurement, second ed.,

http://www.measurementuncertainty.org/ (2000).

[21] European Union. Informal coordination of MRLs established in Directives

76/895/EEC, 86/362/EEC, 86/363/EEC, and 90/642/EEC,

http://europa.eu.int/comm/food/plant/protection/pesticides/index.en.htm,

accessed on 30th April 2007.

[22] Germany, Maximum Residue Levels according to German legislation,

http://www.kennzeichnungsrecht.de/english/mrlsearch.htm, accessed on 30th

April 2007.

[23] The Netherlands, Maximum Residue Limits of Pesticides in the Netherlands,

http://www.rikilt.wageningen-ur.nl/vws/index.html, accessed on 30th April

2007.

[24] United Kingdom, www.psd.gov.uk, accessed on 30th April 2007.

[25] ISO/IEC 17025, General Requirements for the Competence of Testing and

Calibration Laboratories, (2005).

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Figure 2.1: LC–MS/MS Chromatogram of 82 Pesticides in Grape Matrix at 50

ng/mL.

0.0E+00

2.0E+05

4.0E+05

6.0E+05

8.0E+05

0 20 40 60 80 100 120 140 160

Dw ell time (ms)

Peak

are

a (c

ps) Acephate

Methamidophos

Omethoate

Figure 2.2: Dependence of Peak Area (cps) on Dwell Time (analytes: acephate, methamidophos and omethoate)

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Figure 2.3: Comparative Recoveries of Selected Test Pesticides by Different Extraction Methods at 25 ng g-1 (n=10×3 days).

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Table 2.1: Overview of the LC-MS/MS Analyses of the Test Pesticides

Sr. No.

Name of pesticide (Class*)

RT (min) Q Q1 DP

(V) CE (V)

CXP (V) Q2 CE (V) CXP (V) LOD

(ng/g) LOQ (ng/g) R2

1. Acephate (I) 0.90 184 143 48 14 5 125 29 4 2.0 5.0 0.9979

2. Acetamiprid (IV) 4.95 223 126 60 27 6.6 56 35 3.5 1.0 2.5 0.9993

3. Atrazine (VII) 6.00 216 174 65 28 8 104, 96 30 2 1.0 2.5 0.9992

4. Azinphos methyl (I) 6.23 318 160 54 13 7 132 24 5 1.0 2.5 0.9940

5. Azoxystrobin (V) 6.18 404 372 53 22 4 344 32 2 0.1 0.25 0.9982

6. Benalaxyl (VIII) 6.83 326 208 65 24 11 148 23 7 0.1 0.25 0.9996

7. Bitertanol (II) 6.98 338 269 45 19 4 70 19 2 1.0 2.5 0.9985

8. Buprofezin (XVII) 7.62 306 201 32 20 9 116 24 7 0.3 1.0 0.9998

9. Butachlor (XIX) 7.64 312 238 31 18 12 162, 91 35, 40 8, 5, 3.5 1.5 5.0 0.9992

10. Carbendazim (XI) 5.24 192 160 33 30 7 132 43 5.5 0.3 1.0 0.9993

11. Carbaryl (III) 5.88 202 145 53 13 6 127 40 6 0.3 1.0 0.9988

12. Carbofuran (III) 5.75 222 165 55 20 8 123 28 6 0.3 1.0 0.9963

13. Carbofuran-3-OH (III) 4.80 238 163 32 17 8 163,107 22, 46 8, 4.4 0.5 1.5 0.9970

14. Clothianidin (IV) 3.68 250 169 50 20 5 132 29 6 1.0 2.5 0.9992

15. Cymoxanil (X) 5.24 199 111 48 31 4.3 128 22 6 1.0 2.5 0.9971

16. Demeton-S-methyl (I) 5.75 231 89 34 18 4.8 155, 61 25,47 8.3,5 1.0 2.5 0.9968

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Sr. No.

Name of pesticide (Class*)

RT (min) Q Q1 DP

(V) CE (V)

CXP (V) Q2 CE (V) CXP (V) LOD

(ng/g) LOQ (ng/g) R2

17. Demeton-S-methyl sulfone (I)

1.80 263 169 62 22 8 121 22 5 1.0 2.5 0.9984

18. Diazinon (I) 6.93 305 169 15 31 8 153, 97 34, 50 4 0.3 1.0 0.9997

19. Dichlofluanid (XX) 6.60 333 224 33 24 11 123 39 6 1.5 5.0 0.9963

20. Dichlorvos (I) 5.74 221 109 65 27 2 127 28 5.5 1.0 2.5 0.9898

21. Difenoconazole (II) 7.11 406 337 74 25 4 251 34 13 0.3 1.0 0.9993

22. Dimethoate (I) 4.50 230 199 50 18 1 125 29 4 0.3 1.0 0.9981

23. Dimethomorph (XV) 6.30 388 301 55 30 3 165 49 8 0.3 1.0 0.9993

24. Disodium methylarsonate (DMSA) (XXI)

7.10 201 137 40 14 6 92 27 5 1.0 2.5 0.9974

25. Diniconazole (II) 5.50 326 159 74 51 7.2 70 53 2 0.3 1.0 0.9982

26. Emamectin benzoate (VI) 7.67 886.5 158 187 48 7 82.3 95 2 1.0 5.0 0.9992

27. Ethion (I) 7.82 385 199 25 16 1 171 25 1 0.3 1.0 0.9991

28. Etrimfos (I) 6.95 293 125 127 40 6 265, 79 24, 57 13, 2 0.7 2.0 0.9993

29. Fenamidone (XIII) 6.20 312 236 53 21 5 92 35 3 0.3 1.0 0.9991

30. Fenarimol (II) 6.55 331 268 90 35 10 81 55 4 1.0 2.5 0.9968

31. Fenbucarb (III) 6.25 208 95 12 25 4.6 152 14 8 0.3 1.0 0.9981

32. Fenpyroximate (XIV) 8.60 422 366 63 27 2 135, 138 50 6 0.3 1.0 0.9992

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Sr. No.

Name of pesticide (Class*)

RT (min) Q Q1 DP

(V) CE (V)

CXP (V) Q2 CE (V) CXP (V) LOD

(ng/g) LOQ (ng/g) R2

33. Fenthion (I) 6.98 279 247 10 16 2.3 169, 105 27, 35 8.2, 4 1.0 2.5 0.9972

34. Flusilazole (II) 6.95 316 165 13 37 8 247 28 2 0.7 2.0 0.9993

35. Forchlorfenuron (IX) 6.10 248 129 56 25 5.6 155 25 5.6 0.3 1.0 0.9976

36. Hexaconazole (II) 6.95 314 70 52 38 2 159 38 6 0.3 1.0 0.9979

37. Imazalil (XIII) 6.80 297 159 52 34 7.8 201 40 2 1.0 2.5 0.9991

38. Imidacloprid (IV) 3.57 256 209 55 21 11 175 29 8 1.0 2.5 0.9988

39. Indoxacarb (III) 7.04 528 203 81 21 7 249, 56 25, 55 3.2 1.0 2.5 0.9982

40. Iprovalicarb (III) 6.40 321 203 51 13 10 186, 119 18, 25 10, 5 0.3 1.0 0.9992

41. Isoprothiolane (XII) 6.42 291 231 38 19 11.0 189, 145 34, 49 9, 6 0.3 1.0 0.9992

42. Isoproturon (IX) 6.02 207 72 57 35 2 165 20 9 1.0 2.5 0.9997

43. Iprobenfos (I) 6.70 289 91 46 37 6 205 15 10 0.3 1.0 0.9968

44. Kresoxim methyl (V) 6.78 314 267 58 10 9 206, 116 10, 21 9, 5 1.0 2.5 0.9917

45. Malathion (I) 6.38 331 127 62 19 6.0 285, 99 13, 42 4 0.3 1.0 0.9994

46. Malaoxon (I) 5.75 315 127 15 17 10.0 99 42 4 0.3 1.0 0.9959

47. Mandipropamid (XVI) 6.30 412 328 68 18 6 356, 125 15,48 7, 5 1.0 2.5 0.9985

48. Metalaxyl (VIII) 6.05 280 192 58 26 8.0 220, 160 20, 27 8, 9 0.3 1.0 0.9987

49. Methamidophos (I) 0.75 142 94 14 18 5.0 125 26 3.5 2.0 5.0 0.9956

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Sr. No.

Name of pesticide (Class*)

RT (min) Q Q1 DP

(V) CE (V)

CXP (V) Q2 CE (V) CXP (V) LOD

(ng/g) LOQ (ng/g) R2

50. Methidathion (I) 6.13 303 145 39 13 10.0 85 32 4 0.3 1.0 0.9988

51. Methomyl (III) 1.70 163 106 34 17 2.0 88 17 2 1.0 2.5 0.9982

52. Metribuzin (XXII) 5.68 215 187 64 25 9 84 32 6 1.0 2.5 0.9888

53. Mevinphos (I) 4.74 225 193 43 15 9.0 127 22 6 2.0 6.0 0.9986

54. Monocrotophos (I) 2.22 224 127 52 16 3.0 98 20 3 0.3 1.0 0.9983

55. Myclobutanil (II) 6.40 289 70 67 50 2.0 125 29 5 0.3 1.0 0.9985

56. Omethoate (I) 0.95 214 125 45 35 9.0 109, 183 42, 20 4, 10 2.0 5.0 0.9979

57. Oxydemeton methyl (I) 1.54 247 169 48 20 8.8 229, 109 17, 19 12, 4.5 0.3 1.0 0.9969

58. Paraxon methyl (I) 5.55 248 202 40 27 11.0 231, 127 25, 32 12, 6 1.0 2.5 0.9910

59. Penconazole (II) 6.75 284 159 56 36 8.0 70 45 2 1.0 2.5 0.9984

60. Phenthoate (I) 6.75 321 163 18 20 8.0 275, 247 11, 17 4.4, 13 0.3 1.0 0.9990

61. Phosalone (I) 7.01 368 182 68 30 9.0 138, 111 48, 60 6, 4 1.0 2.5 0.9993

62. Phosmet (I) 5.57 318 160 109 10 9.0 133 50 5 0.2 0.6 0.9940

63. Phosphamidon (I) 6.30 300 174 68 21 6.0 127 30 5 0.3 1.0 0.9903

64. Profenophos (I) 7.46 373 303 75 28 6.0 344, 207 19, 38 4,10 0.3 1.0 0.9990

65. Propargite (XVIII) 8.17 368 231 12 17 10.0 175 24 8.5 1.0 2.5 0.9993

66. Propiconazole (II) 6.87 342 159 30 33 8.3 69 40 2 0.6 2.0 0.9966

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Sr. No.

Name of pesticide (Class*)

RT (min) Q Q1 DP

(V) CE (V)

CXP (V) Q2 CE (V) CXP (V) LOD

(ng/g) LOQ (ng/g) R2

67. Pyraclostrobin (V) 6.92 388 194 20 18 10 163, 296 40, 18 7.8, 3 1.0 2.5 0.9997

68. Quinalphos (I) 6.85 299 147 58 37 6.2 163, 243 37, 26 6.2, 11 0.6 2.0 0.9996

69. Simazine (VII) 5.73 202 132 60 27 5.8 124, 96 27, 34 6,3 2.0 5.0 0.9966

70. Spinosyn A (VI) 8.20 732 142 90 38 7.6 99 101 4 2.0 5.0 0.9960

71. Spinosyn D (VI) 8.85 746 142 93 35 7.0 99 100 5 2.0 5.0 0.9988

72. Tebuconazole (II) 6.48 308 70 61 55 4 125 59 8 1.0 2.5 0.9981

73. Temefos (I) 6.77 467 419 92 30 6.0 341, 125 40, 49 3.5, 6 0.3 1.0 0.9993

74. Tetraconazole (II) 7.70 372 70 66 68 5.0 169 40 6.5 2.0 5.0 0.9970

75. Thiamethoxam (IV) 5.26 292 211 52 18 10.0 132 31 6 1.0 2.5 0.9980

76. Thiacloprid (IV) 1.95 253 126 65 29 6 186 24 6 0.3 1.0 0.9998

77. Thiodicarb (II) 5.89 355 88 10 26 4.3 193, 163 14, 13 9, 8 0.3 1.0 0.9987

78. Thiometon (I) 6.00 247 89 12 10 4.5 61 50 3.6 3.3 10.0 0.9896

79. Triazophos (I) 6.40 314 162 29 25 7.0 119 49 4.8 0.7 2.0 0.9998

80. Triadimefon (II) 6.45 294 197 58 21 8.0 115, 69 18, 33 5.8, 2 0.3 1.0 0.9990

81. Triadimenol (II) 6.47 296 70 35 25 5.0 227 14 10 0.2 0.5 0.9991

82. Trifloxystrobin (V) 7.17 409 186 10 25 9.7 206, 116 19 10.4, 4 0.2 0.5 0.9999

83. Triphenyl phosphate (Internal Standard) (I)

327 215 80 38 10 152, 77, 51 55, 65, 125

7, 7, 5 0.9963

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RT = Retention time (minute), Q = Protonated parent ion, Q1 = Quantifier ion, Q2 = Second transition, DP = Declustering potential, CE =

Collision energy, CXP = Collision cell exit potential

*Pesticide class designations:

I = Organophosphorus, II = Triazole, III = Carbamate, IV = Neonicotinoid, V = Strobilurin, VI = Macrocyclic lactone, VII = Triazine,

VIII = Acylamino acid, IX = Urea, X = Aliphatic nitrogen, XI = Benzimidazole, XII = Dithiolane, XIII = Imidazole, XIV = Pyrazole,

XV = Morpholine, XVI = Amide, XVII = Chitin synthesis inhibitor, XVIII = Sulfite ester, XIX = Chloroacetanilide, XX = Sulfamide,

XXI = Arsenical, XXII = Triazinone

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Table 2.2: Mean Recovery (±SD) of the Test Pesticides at Different Levels of Fortifications and HR (Horwitz Ratio) at 25ng/g.

Sr.

No. Name of pesticides

Level of fortification (ng/ g) HR

2.5 5.0 10.0) 25.0 50.0

1. Acephate -- 75

(±11) 75

(±9) 80

(±2) 82

(±3) 0.15

2. Acetamiprid 74

(±7) 86

(±7) 86

(±6) 95

(±5) 95

(±2) 0.20

3. Atrazine 81

(±10) 83

(±8) 81

(±6) 87

(±3) 89

(±0.7) 0.06

4. Azinphos methyl 83

(±2) 85

(±3) 92

(±3) 90

(±4) 111 (±4)

0.18

5. Azoxystrobin 80

(±9) 79

(±2 ) 80

(±0.7 ) 81

(±0.4) 100

(±0.3) 0.25

6. Benalaxyl 80

(±8) 82

(±8) 85

(±6) 83

(±4) 93

(±2) 0.19

7. Bitertanol 70

(±10) 83

(±6) 85

(±9) 83

(±8) 90

(±2) 0.18

8. Buprofezin 78

(±11) 74

(±4) 77

(±2) 80

(±5) 93

(±0.6) 0.19

9. Butachlor 67

(±16) 65

(±10) 61

(±4) 66

(±3) 67

(±4) 0.15

10. Carbendazim 78

(±4) 80

(±9) 76

(±6) 80

(±2) 95

(±2) 0.10

11 Carbaryl 81

(±10) 91 (9)

91 (±4)

101 (±2)

96 (±2)

0.19

12 Carbofuron 88

(±7) 87

(±6) 90

(±2) 94

(±3) 102 (±2)

0.11

13 Carbofuron-3-OH 80

(±16) 82

(±12) 87

(±7) 88

(±5) 102 (±4)

0.20

14 Clothianidin 90

(±12) 85

(±11) 92

(±8) 89

(±7) 107 (±7)

0.13

15 Cymoxanil 82

(±8) 81

(±5) 86

(±4) 94

(±5) 93

(±4) 0.29

16 Demeton-S-methyl 83

(±14) 82

(±15) 80

(±13) 80

(±9) 85

(±5) 0.17

17. Demeton-S-methyl sulfone

104 (±8)

113 (±14)

120 (±13)

110 (±13)

97 (±9)

0.27

18. Diazinon 82

(±10) 82

(±12) 87

(±7) 84

(±1) 98

(±0.9) 0.14

19. Dichlofluanid 97

(±8) 99

(±7) 99

(±5) 98

(±7) 98

(±7) 0.32

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Sr.

No. Name of pesticides

Level of fortification (ng/ g) HR

2.5 5.0 10.0) 25.0 50.0

20. Dichlorvos 70

(±20) 71

(±8) 75

(±7) 72

(±4) 81

(±4) 0.32

21. Difenoconazole 71

(±11) 75

(±8) 76

(±3) 73

(±3) 81

(±1) 0.13

22. Dimethoate 88

(±15) 103 (±3)

101 (±3)

95 (±3)

99 (±0.7)

0.15

23. Dimethomorph 84

(±5) 92

(±6) 91

(±9) 90

(±0.8) 92

(±0.4 ) 0.13

24. Diniconazole 71

(±9) 76

(±11) 75

(±12) 80

(±9) 88

(±7) 0.23

25. DMSA 89

(±9) 88

(±9) 86

(±7.0) 100 (±4)

97 (±2)

0.23

26. Emamectin benzoate -- 54

(±9) 58

(±8) 73

(±5) 75

(±3) 0.14

27. Ethion 86

(±9) 89

(±6) 90

(±2) 91

(±3) 98

(±0.7) 0.22

28. Etrimfos 79

(±15) 79

(±4) 89

(±3) 88

(±0.9) 97

(±0.5) 0.11

29. Fenamidone 117 (±9)

108 (±6)

99 (±5)

99 (±2)

98 (±0.9)

0.17

30. Fenarimol 85

(±15) 96

(±8) 90

(±3) 89

(±4) 90

(±4) 0.28

31. Fenbucarb 96

(±5) 108 (±7)

95 (±4)

106 (±5)

83 (±4)

0.17

32. Fenpyroximate 88

(±16) 105 (±9)

106 (±8)

101 (±5)

95 (±5)

0.14

33. Fenthion 76.4 (±11)

79 (±12)

72 (±10)

73 (±5)

77 (±7)

0.25

34. Flusilazole 83

(±10) 87

(±9) 82

(±5) 83

(±5) 94

(±1) 0.20

35. Forchlorfenuron 80

(±8) 81

(±4) 83

(±2) 80

(±4) 91

(±1) 0.15

36. Hexaconazole 76

(±15) 76

(±9) 82

(±8) 75

(±5) 80

(±3) 0.20

37. Imazalil 78

(±9) 76

(±4) 82

(±5) 79

(±6) 81

(±4) 0.45

38. Imidacloprid 77

(±6) 79

(±2) 82

(±4) 84

(±2) 102 (±5)

0.14

39. Indoxacarb 82

(±11) 93

(±7) 96

(±7) 94

(±3) 96

(±3) 0.16

40. Iprovalicarb 90 92 88 98 99 0.20

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Sr.

No. Name of pesticides

Level of fortification (ng/ g) HR

2.5 5.0 10.0) 25.0 50.0 (±12) (±4) (±3) (±3) (±2)

41. Isoprothiolane 92

(±10) 88

(±10) 92

(±9) 89

(±7) 98

(±1) 0.21

42. Isoproturon 85

(±10) 83

(±4) 88

(±6) 89

(±3) 91

(±2) 0.23

43. Iprobenfos 92

(±2)

92 (±5)

93 (±6)

93 (±2)

95 (±3)

0.22

44. Kresoxim methyl 88

(±5) 93

(±4) 92

(±2) 94

(±7) 90

(±3) 0.38

45. Malathion 97

(±12) 105 (±7)

101 (±4)

102 (±5)

96 (±4)

0.13

46. Malaoxon 113 (±9)

111 (±5)

93 (±6)

93 (±1)

101 (±1)

0.10

47. Mandipropamid 88

(±10) 92

(±9) 92

(±8) 97

(±9) 92

(±4) 0.27

48. Metalaxyl 96

(±11) 105 (±6)

94 (±7)

92 (±0.7)

107 (±0.8)

0.21

49. Methamidophos -- 76

(±9) 77

(±9) 75

(±2) 83

(±0.3) 0.28

50. Methidathion 102 (±9)

103 (±8)

103 (±4)

94 (±1)

95 (±0.8)

0.18

51. Methomyl 100

(±11) 107 (±4)

97 (±4)

99 (±0.3)

102 (±0.3)

0.08

52. Metribuzin 36

(±9) 54

(±4) 58

(±4) 55

(±4) 56

(±2) 0.17

53. Mevinphos 76

(±5) 90

(±9) 82

(±1) 97

(±0.5) 104

(±0.8) 0.16

54. Monocrotophos 88

(±10) 93

(±5) 100 (±5)

97 (±1)

94 (±2)

0.36

55. Myclobutanil 79

(±10) 76

(±8) 81

(±3) 79

(±0.7) 86

(±0.6) 0.07

56. Omethoate -- 80

(±6) 73

(±3) 72

(±0.8) 85

(±3) 0.15

57. Oxydemeton methyl 85

(±5) 84

(±5) 88

(±6) 88

(±0.7) 94

(±0.5) 0.16

58. Paraoxon methyl 117

(±19) 119

(±11) 110 (±8)

88 (±7)

88 (±0.5)

0.41

59. Penconazole 71

(±15) 76

(±11) 75

(±9) 75

(±5) 82

(±0.9) 0.27

60. Phenthoate 83

(±4) 85

(±5) 80

(±2) 91

(±1) 91

(±0.5) 0.26

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Sr.

No. Name of pesticides

Level of fortification (ng/ g) HR

2.5 5.0 10.0) 25.0 50.0

61. Phosalone 68

(±9) 65

(±6) 74

(±3) 78

(±0.3) 81

(±0.3) 0.14

62. Phosphamidon 72

(±15) 82

(±5) 80

(±7) 81

(±2) 85

(±0.5) 0.18

63. Phosmet 103

(±17) 103

(±13) 100

(±15) 98

(±13) 99

(±0.9) 0.16

64. Profenophos 102 (±9)

86 (±2)

79 (±4)

81 (±0.7)

82 (±0.8)

0.11

65. Propargite 61

(±5) 62

(±5) 60

(±3) 62

(±2) 80

(±0.6) 0.43

66. Propiconazole 82

(±10) 86

(±7) 87

(±7) 89

(±0.8) 84

(±0.2) 0.15

67. Pyraclostrobin 91

(±6) 78

(±5) 90

(±6) 88

(±0.9) 84

(±1.0) 0.24

68. Quinalphos 78

(±16) 100 (±5)

85 (±7)

83 (±0.4)

84 (±0.2)

0.11

69. Simazine -- 89

(±8) 89

(±10) 78

(±5) 82

(±5) 0.24

70. Spinosyn A -- 71

(±11) 74

(±13) 75

(±9) 74

(±7) 0.15

71. Spinosyn D -- 70

(±15) 70

(±10) 73

(±10) 75

(±7) 0.28

72. Tebuconazole 84

(±8) 81

(±8) 89

(±4) 84

(±2) 85

(±1) 0.54

73. Temefos 48

(±6) 54

(±3) 52

(±4) 57

(±3) 53

(±2) 0.30

74. Tetraconazole -- 73

(±2) 85

(±4) 93

(±3) 93

(±0.8) 0.19

75. Thiacloprid 80

(±7) 93

(±4) 91

(±4) 84

(±2) 92

(±0.7) 0.27

76. Thiamethoxam 89

(±6) 82

(±4) 88

(±6) 88

(±2) 94

(±0.8) 0.23

77. Thiodicarb 98

(±17) 106 (±4)

106 (±5)

104 (±0.7)

101 (±0.3)

0.29

78. Thiometon -- -- 80

(±9) 85

(±11) 87

(±10) 0.35

79. Triadimefon 91

(±11) 81

(±2) 84

(±3.0) 86

(±2) 93

(±2) 0.11

80. Triadimenol 64

(±4) 74

(±4) 74

(±3) 106

(±4.0) 96

(±1.0) 0.19

81. Triazophos 75 78 81 80 101 0.12

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Sr.

No. Name of pesticides

Level of fortification (ng/ g) HR

2.5 5.0 10.0) 25.0 50.0 (±8) (±7) (±3) (±2) (±3)

82. Trifloxystrobin 101 (±9)

86 (±8)

88 (±7)

108 (±2)

98 (±0.5)

0.17

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Table 2.3: Results of Individual and Global Uncertainties for Each Pesticide

Uncertainty components (expressed as relative measures, calculated at 25 ng g-1)

Sr.

No. Name of Pesticide

(class)

Cal.

curve U1

Precision Bias (U) (2U)

U2 U3 U4 U5

1. Acephate (I) 0.05 0.014 0.018 0.015 0.013 0.05 0.10

2. Acetamiprid (I) 0.03 0.007 0.011 0.020 0.021 0.05 0.10

3. Atrazine (I) 0.02 0.014 0.027 0.006 0.013 0.04 0.08

4. Azinphos methyl (I) 0.03 0.021 0.020 0.019 0.023 0.05 0.10

5. Azoxystrobin (I) 0.02 0.013 0.019 0.025 0.012 0.04 0.08

6. Benalaxyl (I) 0.02 0.011 0.016 0.019 0.011 0.04 0.08

7. Bitertanol (I) 0.02 0.015 0.012 0.018 0.011 0.04 0.08

8. Buprofezin (I) 0.01 0.013 0.019 0.020 0.013 0.03 0.06

9. Butachlor (I) 0.03 0.011 0.013 0.010 0.012 0.04 0.08

10. Carbaryl (I) 0.01 0.022 0.015 0.014 0.011 0.03 0.06

11. Carbendazim (I) 0.01 0.026 0.018 0.014 0.011 0.04 0.08

13. Carbofuran (I) 0.02 0.012 0.012 0.021 0.009 0.03 0.06

12. Carbofuran-3-OH (II) 0.03 0.024 0.032 0.011 0.049 0.07 0.14

14. Clothianidin (I) 0.03 0.010 0.022 0.013 0.022 0.05 0.10

15. Cymoxanil (I) 0.01 0.013 0.020 0.029 0.023 0.05 0.10

17. Demeton-S-methyl (I) 0.00 0.021 0.033 0.020 0.021 0.05 0.10

16. Demeton-S-methyl sulfone (I)

0.03 0.018 0.028 0.017 0.018 0.05 0.10

18. Diazinon (I) 0.02 0.010 0.015 0.014 0.013 0.03 0.06

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Uncertainty components (expressed as relative measures, calculated at 25 ng g-1)

Sr.

No. Name of Pesticide

(class)

Cal.

curve U1

Precision Bias (U) (2U)

U2 U3 U4 U5

19. Dichlofluanid (I) 0.03 0.012 0.021 0.022 0.015 0.05 0.10

20. Dichlorvos (II) 0.03 0.034 0.040 0.015 0.019 0.07 0.14

21. Difenoconazole (I) 0.01 0.011 0.006 0.013 0.009 0.02 0.04

22. Dimethoate (I) 0.03 0.015 0.022 0.015 0.012 0.04 0.08

23. Dimethomorph (I) 0.01 0.014 0.014 0.013 0.013 0.03 0.06

24. Diniconazole (I) 0.02 0.014 0.022 0.023 0.008 0.04 0.08

25. DMSA (I) 0.02 0.018 0.023 0.023 0.018 0.05 0.10

26. Emamectin benzoate (II) 0.02 0.048 0.005 0.014 0.023 0.06 0.12

27. Ethion (I) 0.00 0.013 0.008 0.022 0.018 0.03 0.06

28. Etrimfos (I) 0.02 0.010 0.005 0.011 0.014 0.03 0.06

29. Fenamidone (I) 0.02 0.011 0.015 0.015 0.017 0.04 0.08

30. Fenarimol (II) 0.03 0.029 0.032 0.028 0.021 0.06 0.12

31. Fenbucarb (I) 0.01 0.015 0.021 0.017 0.015 0.04 0.08

32. Fenpyroximate (I) 0.01 0.013 0.009 0.025 0.027 0.04 0.08

33. Fenthion (I) 0.02 0.013 0.013 0.026 0.018 0.04 0.08

34. Flusilazole (I) 0.01 0.013 0.022 0.021 0.014 0.04 0.08

35. Forchlorfenuron (I) 0.03 0.013 0.024 0.021 0.018 0.05 0.10

36. Hexaconazole (I) 0.02 0.010 0.018 0.027 0.013 0.04 0.08

37. Imazalil (II) 0.01 0.007 0.006 0.056 0.032 0.07 0.13

38. Imidacloprid (I) 0.02 0.015 0.020 0.014 0.020 0.04 0.08

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Uncertainty components (expressed as relative measures, calculated at 25 ng g-1)

Sr.

No. Name of Pesticide

(class)

Cal.

curve U1

Precision Bias (U) (2U)

U2 U3 U4 U5

39. Indoxacarb (I) 0.02 0.012 0.013 0.018 0.019 0.04 0.08

43. Iprobenfos (I) 0.02 0.025 0.032 0.014 0.015 0.05 0.10

40. Iprovalicarb (I) 0.02 0.009 0.014 0.014 0.016 0.03 0.06

41. Isoprothiolane (I) 0.01 0.011 0.019 0.021 0.014 0.03 0.06

42. Isoproturon (I) 0.02 0.014 0.026 0.015 0.016 0.04 0.08

44. Kresoxim methyl (II) 0.04 0.025 0.035 0.028 0.025 0.07 0.14

46. Malaoxon (I) 0.03 0.016 0.016 0.010 0.016 0.04 0.08

45. Malathion (I) 0.01 0.013 0.014 0.014 0.014 0.03 0.06

47. Mandipropamid (I) 0.01 0.017 0.017 0.022 0.012 0.03 0.06

48. Metalaxyl (I) 0.01 0.015 0.027 0.011 0.012 0.04 0.08

49. Methamidophos (I) 0.02 0.029 0.018 0.028 0.018 0.05 0.10

50. Methidathion (I) 0.02 0.014 0.028 0.019 0.007 0.04 0.08

51. Methomyl (I) 0.02 0.009 0.013 0.008 0.011 0.03 0.06

52. Metribuzin (II) 0.02 0.030 0.052 0.021 0.020 0.07 0.14

53. Mevinphos (I) 0.02 0.015 0.018 0.016 0.013 0.04 0.08

54. Monocrotophos (I) 0.01 0.018 0.020 0.037 0.010 0.05 0.10

55. Myclobutanil (I) 0.03 0.011 0.016 0.007 0.010 0.04 0.08

56. Omethoate (I) 0.02 0.014 0.023 0.015 0.014 0.04 0.08

57. Oxydemeton methyl (I) 0.01 0.012 0.016 0.016 0.018 0.03 0.06

58. Paraoxon methyl (III) 0.05 0.027 0.030 0.042 0.034 0.09 0.18

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Uncertainty components (expressed as relative measures, calculated at 25 ng g-1)

Sr.

No. Name of Pesticide

(class)

Cal.

curve U1

Precision Bias (U) (2U)

U2 U3 U4 U5

59. Penconazole (I) 0.03 0.011 0.016 0.028 0.009 0.04 0.08

60. Phenthoate (I) 0.01 0.015 0.021 0.027 0.016 0.04 0.08

61. Phosalone (I) 0.02 0.012 0.009 0.014 0.009 0.03 0.06

62. Phosmet (I) 0.03 0.021 0.021 0.022 0.023 0.05 0.10

63. Phosphamidon (I) 0.02 0.011 0.018 0.016 0.014 0.04 0.08

64. Profenophos (I) 0.02 0.011 0.017 0.011 0.011 0.03 0.06

65. Propargite (II) 0.01 0.013 0.007 0.052 0.029 0.06 0.12

66. Propiconazole (I) 0.02 0.012 0.017 0.016 0.011 0.04 0.08

67. Pyraclostrobin (I) 0.01 0.011 0.012 0.004 0.005 0.02 0.04

68. Quinalphos (I) 0.02 0.008 0.009 0.011 0.007 0.02 0.04

69. Simazine (I) 0.02 0.021 0.028 0.017 0.018 0.05 0.10

70. Spinosyn A (I) 0.01 0.017 0.007 0.016 0.035 0.04 0.08

71. Spinosyn D (I) 0.02 0.028 0.006 0.029 0.025 0.05 0.10

72. Tebuconazole (II) 0.03 0.006 0.009 0.036 0.026 0.06 0.12

73. Temephos (III) 0.01 0.012 0.008 0.055 0.054 0.08 0.16

74. Tetraconazole(I) 0.01 0.012 0.012 0.030 0.015 0.04 0.08

75. Thiacloprid(II) 0.02 0.014 0.023 0.030 0.032 0.06 0.12

76. Thiamethoxam (II) 0.04 0.020 0.023 0.027 0.010 0.06 0.12

77. Thiodicarb (I) 0.02 0.017 0.023 0.029 0.018 0.05 0.10

78. Thiometon (III) 0.02 0.029 0.044 0.035 0.073 0.10 0.20

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Uncertainty components (expressed as relative measures, calculated at 25 ng g-1)

Sr.

No. Name of Pesticide

(class)

Cal.

curve U1

Precision Bias (U) (2U)

U2 U3 U4 U5

79. Triadimefon (I) 0.02 0.013 0.010 0.020 0.012 0.03 0.06

80. Triadimenol (I) 0.01 0.011 0.014 0.012 0.013 0.03 0.06

81. Triazophos (I) 0.01 0.013 0.017 0.012 0.010 0.03 0.06

82. Trifloxystrobin (I) 0.02 0.012 0.016 0.018 0.010 0.03 0.06

(U= global uncertainty, 2U= Expanded uncertainty