Fast, low-pressure gas chromatography triple quadrupole tandem mass spectrometry for analysis of 150 pesticide residues in fruits and vegetables

Journal of Chromatography A, 1218 (2011) 7039–7050

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Fast, low-pressure gas chromatography triple quadrupole tandem mass spectrometry for analysis of 150 pesticide residues in fruits and vegetables Urairat Koesukwiwat a,b , Steven J. Lehotay a,∗ , Natchanun Leepipatpiboon b a b

US Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center, 600 East Mermaid Lane, Wyndmoor, PA 19038, USA Chromatography and Separation Research Unit, Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand

a r t i c l e

i n f o

Article history: Received 30 April 2011 Received in revised form 28 July 2011 Accepted 29 July 2011 Available online 6 August 2011 Keywords: QuEChERS Sample throughput Pesticide analysis Vacuum-outlet gas chromatography Triple quadrupole mass spectrometry

a b s t r a c t We developed and evaluated a new method of low-pressure gas chromatography–tandem mass spectrometry (LP-GC/MS–MS) using a triple quadrupole instrument for fast analysis of 150 relevant pesticides in four representative fruits and vegetables. This LP-GC (vacuum outlet) approach entails coupling a 10 m, 0.53 mm i.d., 1 ␮m film analytical column between the MS transfer line and a 3 m, 0.15 mm i.d. capillary at the inlet. The MS creates a vacuum in the 10 m analytical column, which reduces the viscosity of the He carrier gas and thereby shifts the optimal flow rate to greater velocity. By taking advantage of the H2 -like properties of He under vacuum, the short analytical column, a rapid oven temperature ramp rate, and the high selectivity and sensitivity of MS/MS, 150 pesticides were separated in <6.5 min. The 2.5 ms dwell time and 1 ms interscan delay of the MS/MS instrument were critical for achieving >8 data points across the 2–3 s wide peaks. To keep dwell and cycle times constant across all peaks, each segment consisted of 30 analytes (60 transitions). For assessment, we injected extracts of spiked broccoli, cantaloupe, lemon, and sweet potato from the updated QuEChERS sample preparation method. Average recoveries (n = 72) were 70–120% for 144 of the pesticides, and reproducibilities were <20% RSD for all but 4 analytes. Also, detection limits were <5 ng/g for all but a few pesticides, depending on the matrix. In addition to high quality performance, the method gave excellent reliability and high sample throughput, including easy peak integration to obtain rapid results. Published by Elsevier B.V.

1. Introduction Pesticide residues in foods is an important concern in food safety programs worldwide, and analytical methods are required in monitoring programs to ensure that the food is safe and the farmers follow environmentally responsible agricultural practices. Typically, limits of quantification (LOQ) <10 ng/g are needed for monitoring purposes to routinely and reliably identify and quantify hundreds of pesticide residues in hundreds of commodities. In recent years, improvements in sample preparation and instrumentation, especially in the cases of liquid and gas chromatography (LC and GC) with mass spectrometry (MS) or tandem-MS (MS/MS) detection, have led to tremendous gains in better meeting regulatory requirements for monitoring and enforcement purposes. In addition to achieving high quality results with wide analytical scope, analytical methods should be fast in terms of both sample turnaround time and sample throughput, inexpensive, easy-toperform, and rugged.

∗ Corresponding author. Tel.: +1 215 233 6433; fax: +1 215 233 6642. E-mail address: [email protected] (S.J. Lehotay). 0021-9673/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.chroma.2011.07.094

In terms of sample preparation, the “quick, easy, cheap, effective, rugged, and safe” (QuEChERS) approach possesses several beneficial features which have been previously described and reviewed in the literature [1–8]. The QuEChERS template for sample preparation is very flexible and different versions have been independently developed, validated, and applied in monitoring laboratories depending on the analytes, matrices, instruments, and personal preferences [1–17]. Recently, we sought to combine the best features of the two official versions of QuEChERS [2,3] in an attempt to devise the most applicable protocol overall that most efficiently attains acceptable results for as many pesticides and commodities as possible [11]. This method showed acceptable results for most of the 150 pesticides tested, which met typical quantification and qualitative identification criteria for regulatory monitoring while also increasing sample throughput with a high degree of ruggedness [11,12]. In terms of analysis of GC-amenable pesticides, typical GC–MS methods use a relatively long, narrow-bore analytical column (30 m, 0.25 mm i.d.), which generally requires >30 min run times per sample, especially when analyzing >100 GCamenable pesticides. Among alternative fast-GC/MS methods [18–21], low-pressure (LP)-GC/MS has advantages of increased ruggedness, sensitivity, and speed with the only sacrifice of reduced

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chromatographic separation efficiency [11,12,21–38]. Nearly all GC–MS methods entail vacuum outlet conditions in which the low pressure region extends up the analytical column to a certain distance, but in LP-GC/MS, that distance essentially covers the entire 10 m, 0.53 mm i.d. analytical column. To maintain a positive head pressure at the injector, the analytical column is coupled to a short, micro-bore (3 m, 0.15 mm i.d.) uncoated restriction capillary at the inlet. During chromatography under vacuum conditions, the He carrier gas has reduced viscosity, like that of H2 , which serves to increase the optimum linear velocity (Uopt ) and allows use of higher gas flow rate. This and the combination of a short, mega-bore column and rapid oven temperature ramp rate help provide faster GC-separations and higher sample throughput. Higher sample throughput in GC–MS has become especially important since the greater adoption of methods using ultrahigh performance (UHP)LC for analysis of LC-amenable pesticides. UHPLC–MS(/MS) methods are capable of identifying and quantifying hundreds of analytes in ≈10 min [39,40], and GC–MS must be able to keep pace in concurrent analyses to avoid being the rate limiting step in sample throughput. LP-GC/MS has been demonstrated in multiresidue monitoring [11,12,21–38] to possess improved features compared to conventional GC–MS, including greater sensitivity, shorter run time, increased sample loading (due to thicker film), reduced thermal degradation of problematic pesticides (due to higher flow), reduced peak tailing, and increased ruggedness (due to wider column and thicker film). LP-GC/MS relies on MS detection to provide increased selectivity to overcome the sacrifice in separation efficiency for increased speed. As more pesticides are included in the method, a greater burden is placed on the MS to distinguish co-eluting pesticides from each other, particularly at 10 ng/g concentrations in highly complicated extracts. The peak widths in LP-GC/MS are 2–3 s (similar to traditional GC), and the data collection rate must be fast enough to achieve at least 5 points across the peak for typical quantitative purposes [21]. Previous LP-GC/MS methods have employed selected ion monitoring (SIM) or full-scan operation with quadrupole, ion-trap, and time-of-flight (ToF) systems. For several years, the need for rapid data collection rates to yield enough points across the peaks had greatly limited the number of targeted analytes that could be included in the fast-GC method using SIM and MS/MS. Thus, the very fast data collection rate of ToF (up to 500 spectra/s with low resolution instruments) has been used to permit analysis of a potentially unlimited number of analytes in full mass spectral acquisition. We evaluated LP-GC/ToF both quantitatively [11] and qualitatively [12] for the analysis of 150 pesticides in fruits and vegetables. The method worked well in many respects, but ultimately, it did not provide sufficiently low LOQs of <10 ng/g for many pesticides, and the false positive and negative rates were higher than desired when using mass spectral deconvolution at the final conditions. MS/MS with a triple quadrupole instrument provides greater sensitivity and selectivity than full scan instruments, with the limitation that it only allows targeted analytes to be monitored. Until recently, the dwell and interchannel delay times of the commercial triple quadrupole instruments were too long to permit analysis of enough pesticides (due to co-elutions) to meet monitoring needs. Recently, however, instrumentation has been introduced that allows as little as 1 ms each of dwell and interscan delay times, which may sufficiently overcome the previous deficiencies in scope of the LP-GC/MS–MS concept. The objective of this study was to optimize and assess state-ofthe-art LP-GC/MS–MS for fast, sensitive, selective, quantitative, and rugged analysis of 150 targeted pesticides in 4 representative fruits and vegetables (cantaloupe, broccoli, sweet potato, and lemon) extracted using the updated QuEChERS method. The quantitative

results of this new LP-GC/MS–MS method would be compared with those from the LP-GC/ToF method evaluated previously [11,12]. Furthermore, the greater selectivity of MS/MS better allowed the addition of analyte protectants (ethylglycerol, gulonolactone, sorbitol, and shikimic acid) to the final extracts [1,41–43], which led us to set an additional goal to assess matrix effects (MEs) when using them in LP-GC/MS–MS. 2. Experimental 2.1. Chemicals and materials Pesticide reference standards of ≥95% or higher purity were obtained from the Environmental Protection Agency’s National Pesticide Repository (Fort Meade, MD, USA), Dr. Ehrenstorfer GmbH (Augsburg, Germany), and Chemservice (West Chester, PA, USA). Table 1 lists the pesticides in the study. Isotopically labeled internal standards (I.S.), atrazine-d5 (ethyl-d5 ) and fenthion-d6 (o,o-dimethyl-d6 ), were purchased from C/D/N Isotopes (PointeClaire, Quebec, Canada). Triphenylphosphate (TPP) was used as a quality control (QC) standard in this study. Analyte protectants consisted of 3-ethoxy-1,2-propanediol (ethylglycerol), l-gulonic acid-␥-lactone (gulonolactone), d-sorbitol, and shikimic acid (all were ≥95% purity) obtained from Sigma–Aldrich (St. Louis, MO, USA). Acetonitrile (MeCN) and ethyl acetate (EtOAc) were of HPLCgrade from J.T. Baker (Phillipsburg, NJ, USA), and toluene was from Sigma–Aldrich. Glacial acetic acid (HOAc) and formic acid were of analytical grade obtained from J.T. Baker. We used the updated QuEChERS method for sample preparation [11]. For the extraction/partitioning step, 6 g anhydrous (anh.) MgSO4 plus 1.5 g anh. NaOAc were pre-weighed in advance and kept in amber glass vials. For dispersive solidphase extraction (d-SPE) cleanup, 2 mL mini-centrifuge tubes containing 150 mg anh. MgSO4 + 50 mg primary secondary amine (PSA) + 50 mg C18 + 7.5 mg graphitized carbon black (GCB) sorbents were purchased from UCT (Bristol, PA, USA). Samples of cantaloupe, broccoli, sweet potato, and lemon were purchased from a local organic food store. The raw commodities were processed as they were received, peels and all. 2.2. Standard solution preparation Individual pesticide, I.S., and QC stock solutions were prepared at about 4000 ng/␮L in toluene or ethyl acetate depending on their solubility properties, and stored in amber glass vials at −18 ◦ C. A working standard mixture of 150 pesticides at 20 ng/␮L in MeCN with 0.05% formic acid was prepared from the stock solutions. This mixture served as the high spiking solution, and two other spiking solutions of 3.75 ng/␮L (middle) and 0.5 ng/␮L (low) were also prepared in MeCN with 0.05% formic acid. A mixture of both I.S. solutions at 10 ng/␮L and QC standard at 100 ng/␮L was also prepared in MeCN with 0.05% formic acid. A mixture of QC and analyte protectants solution containing TPP at 1 ng/␮L + ethylglycerol at 10 mg/mL + gulonolactone at 1 mg/mL + d-sorbitol at 1 mg/mL + shikimic acid at 0.5 mg/mL was prepared in 4/1 (v/v) MeCN/water with 0.5% formic acid (the water in this mixture helped to dissolve the polar analyte protectants). Intermediate calibration solutions consisted of mixtures of 150 pesticides at 0.05, 0.1, 0.25, 0.75, 2, 4, and 6 ng/␮L plus 1 ng/␮L of the two I.S. compounds in each solution. The working calibration standards were prepared by adding 500 ␮L of MeCN (reagent-only standards) or 500 ␮L of blank sample extracts (matrix-matched standards), 50 ␮L of each intermediate calibration solution, and 50 ␮L of the QC and analyte protectants mixture. These solutions yielded concentrations of 5, 10, 25, 75, 200, 400, and 600 ng/g

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Table 1 Average retention times (tR ), MRM conditions, average regression (R2 ) values in matrix (n = 4), average overall %recoveries and %RSD (reproducibility among 4 matrices) in the validation experiments, n = 72, and lowest calibrated levels (LCLs), for the pesticide analytes among the 4 matrices. An asterisk is used to denote the use of the molecular ion as the precursor in MS/MS. R2 < 0.990, recovery >120%, and RSD > 20% are noted in bold text. Recovery <70% is noted in underlined text. Avg R2

Avg tR

MRM transitions (m/z)

Analyte

(min)

Quantifier

CE (V)

Qualifier

CE (V)

n=4

Avg Overall % recovery (%RSD)

LCL

Alachlor Aldrin Atrazine Atrazine-d5 (I.S.) Azinphos-ethyl Azinphos-methyl

3.913 4.091 3.556 3.548 5.312 5.197

188 → 130 263 → 193 215* → 200 205 → 105 160 → 77 160 → 77

40 40 10 15 15 15

188 → 160 263 → 228 215* → 138 205 → 127 132 → 77 132 → 77

10 20 10 10 15 15

0.998 0.991 0.998 n/a 0.998 0.977

96 (7) 84 (11) 97 (8) n/a 99 (5) 109 (14)

5 5 5 5 5 5

BHC, alphaBHC, betaBHC, gamma (Lindane) BHC, deltaBifenthrin Bromophos Bromophos-ethyl Bromopropylate Bupirimate Buprofezin

3.487 3.592 3.622 3.718 5.038 4.178 4.364 5.047 4.567 4.554

219 → 183 219 → 183 219 → 183 219 → 183 181 → 165 331 → 316 359 → 303 341 → 183 273 → 108 172 → 57

5 5 5 5 30 15 15 15 15 10

183 → 147 183 → 147 183 → 147 183 → 147 181 → 166 331 → 93 303 → 211 341 → 157 273 → 193 172 → 171

15 15 15 15 15 30 30 40 5 5

0.993 0.997 0.989 0.997 0.999 0.999 0.998 0.998 0.999 0.999

97 (11) 97 (9) 97 (7) 98 (7) 94 (6) 94 (6) 91 (6) 99 (6) 99 (5) 96 (6)

5 5 5 5 5 5 5 5 5 5

Cadusafos Captafol Captan Carbaryl Carbofuran Carbophenothion Carfentrazone-ethyl Chinomethionat Chlordane, cisChlordane, transChlorfenvinphos Chlorothalonil Chlorpropham Chlorpyrifos Chlorpyrifos-methyl Coumaphos Cyanophos Cyfluthrin Cyhalothrin, lambdaCypermethrin Cyprodinil

3.430 4.951 4.290 3.084 3.541 4.811 4.811 4.366 4.437 4.371 4.275 3.738 3.347 4.093 3.883 5.450 3.625 5.562 5.233 5.654 4.212

159 → 97 79 → 77 149 → 79 144 → 115 164 → 149 342 → 157 312 → 151 234* → 206 373 → 266 373 → 266 267 → 159 266* → 133 213* → 127 199 → 171 286 → 93 362* → 81 243* → 109 163 → 91 181 → 152 181 → 152 224* → 208

20 10 10 25 5 5 20 10 30 30 20 40 15 20 25 40 10 15 25 20 25

159 → 79 149 → 79 79 → 77 144 → 116 164 → 103 157 → 75 312 → 122 234* → 148 373 → 301 373 → 301 267 → 81 266* → 168 213* → 171 314 → 258 286 → 271 362* → 109 243* → 79 163 → 127 197 → 141 163 → 91 224* → 197

35 10 10 15 25 40 30 25 10 10 40 25 5 20 15 15 30 5 15 15 25

0.998 0.957 0.953 0.982 0.998 0.999 0.999 0.991 0.997 0.998 0.998 0.968 0.995 0.999 0.998 0.998 0.998 0.996 0.998 0.998 0.996

94 (8) 105 (16) 122 (26) 91 (10) 99 (7) 96 (7) 99 (6) 54 (14) 93 (7) 92 (7) 99 (5) 95 (16) 98 (6) 95 (6) 97 (6) 92 (7) 101 (5) 97 (8) 98 (7) 97 (7) 69 (9)

5 5–200 5–200 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

DDD, o,p DDD, p,p - + DDT, o,p DDE, o,p DDE, p,p DDT, p,p Deltamethrin Demeton-s-methyl Demeton-s-methyl sulfone Diazinon Dichlorfenthion Dichlorobenzophenone, 4,4 Dicloran Dicrotophos Dieldrin Dimethoate Dioxathion Diphenylamine Disulfoton Disulfoton sulfone

4.558 4.704 4.379 4.516 4.859 6.165 3.298 4.003 3.660 3.837 4.114 3.530 3.392 4.545 3.531 3.609 3.304 3.696 4.406

235 → 165 235 → 165 246 → 176 246 → 176 235 → 165 181 → 152 88 → 60 169 → 109 304* → 179 279 → 223 139 → 111 206* → 176 127 → 109 263 → 193 93 → 63 270 → 141 169* → 168 88 → 60 213 → 97

25 25 35 35 25 25 5 10 15 10 15 10 10 40 5 20 10 5 20

235 → 199 235 → 199 318 → 248 318 → 248 235 → 199 181 → 127 88 → 59 169 → 125 179 → 137 279 → 205 139 → 75 206* → 148 127 → 95 277 → 241 125 → 93 270 → 97 169* → 140 88 → 59 213 → 125

15 15 25 25 15 25 15 5 20 30 30 20 15 10 15 5 35 15 10

0.998 0.998 0.999 0.996 0.992 0.997 0.997 0.991 0.998 0.998 0.994 0.994 0.991 0.998 0.994 0.992 0.992 0.999 1.000

96 (5) 95 (6) 91 (6) 89 (6) 94 (7) 94 (7) 100 (7) 105 (9) 97 (7) 95 (6) 95 (5) 97 (5) 92 (8) 93 (6) 98 (6) 101 (8) 96 (6) 98 (8) 102 (7)

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5–10 5 5 5 5

Endosulfan sulfate Endosulfan, alphaEndosulfan, betaEndrin Endrin ketone EPN Esfenvalerate Ethalfluralin Ethion Ethoprophos Ethoxyquin

4.862 4.429 4.686 4.646 5.044 5.055 5.954 3.358 4.715 3.321 3.532

387 → 253 241 → 170 241 → 206 263 → 193 317 → 101 169 → 77 167 → 125 276 → 202 231 → 129 158 → 97 202 → 174

15 30 15 40 15 25 5 15 25 15 10

387 → 206 241 → 206 241 → 170 263 → 228 317 → 245 169 → 141 167 → 89 276 → 105 231 → 175 158 → 81 202 → 145

30 15 30 20 15 10 35 25 10 15 30

0.997 0.996 0.996 0.997 0.998 0.996 0.996 0.982 0.999 0.998 0.942

96 (13) 95 (15) 95 (6) 94 (8) 96 (7) 100 (5) 97 (7) 93 (15) 98 (5) 96 (7) 90 (26)

5–10 5–10 5 5 5 5 5 5 5 5 5

Famphur Fenamiphos

4.820 4.458

218 → 109 303* → 154

15 20

218 → 79 303* → 217

30 20

0.997 0.999

103 (7) 99 (5)

5 5

(ng/g)

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Table 1 (Continued) Avg R2

Avg tR

MRM transitions (m/z)

Avg Overall % recovery (%RSD)

LCL

Analyte

(min)

Quantifier

CE (V)

Qualifier

CE (V)

n=4

Fenarimol Fenchlorphos Fenitrothion Fenoxycarb Fenpropathrin Fensulfothion Fenthion Fenthion sulfone Fenthion-d6 (I.S.) Fenvalerate Fipronil Flucythrinate Fluvalinate, tau Folpet Fonofos

5.294 3.947 3.998 4.369 5.067 4.702 4.082 4.722 4.072 5.888 4.288 5.654 5.953 4.320 3.649

139 → 111 285 → 93 277* → 109 255 → 186 181 → 152 293 → 97 278* → 109 310* → 109 284* → 115 167 → 125 367 → 213 199 → 107 250 → 55 147 → 76 246* → 109

15 25 20 5 20 25 20 30 20 5 35 30 20 25 15

139 → 75 285 → 63 277* → 125 255 → 77 181 → 77 293 → 109 278* → 169 310* → 105 284* → 169 167 → 89 367 → 255 199 → 157 250 → 200 147 → 103 246* → 137

30 40 20 40 40 15 15 15 15 35 20 5 20 15 5

0.997 0.999 0.998 0.945 0.997 0.998 0.999 0.997 n/a 0.998 0.995 0.997 0.995 0.958 0.997

97 (6) 96 (6) 100 (6) 109 (45) 88 (12) 98 (5) 100 (5) 102 (7) n/a 98 (7) 99 (7) 102 (7) 98 (8) 125 (17) 96 (7)

5 5 5 5–200 5 5 5 5 5 5 5 5 5 5–25 5

Heptachlor Heptachlor epoxide Heptenophos Hexachlorobenzene

3.935 4.265 3.206 3.525

272 → 237 353 → 263 124 → 89 284* → 214

20 15 10 35

272 → 117 353 → 253 124 → 63 284* → 249

35 25 35 20

0.977 0.995 0.995 0.996

92 (13) 95 (8) 100 (8) 60 (9)

5 5 5 5

Iprodione Isofenphos

5.012 4.269

314 → 56 213 → 121

35 15

314 → 245 213 → 185

10 5

0.997 0.999

101 (7) 99 (6)

5 5

Kepone Kresoxim methyl

4.838 4.563

355 → 285 116 → 89

35 20

355 → 320 206 → 131

30 20

0.931 0.998

91 (26) 100 (6)

5–75 5

Leptophos

5.191

171 → 77

20

171 → 124

10

0.999

80 (6)

5

Malathion Metalaxyl Methacrifos Methidathion Methiocarb Methoxychlor Metolachlor Metribuzin Mevinphos Mirex Myclobutanil

4.033 3.933 3.053 4.359 3.996 5.068 4.077 3.847 2.921 5.248 4.560

173 → 99 206 → 132 208 → 180 145 → 85 168 → 153 227 → 169 238 → 162 198 → 82 127 → 109 272 → 237 179 → 125

15 20 5 5 10 30 15 15 10 15 15

173 → 117 206 → 105 208 → 79 145 → 58 168 → 109 227 → 141 238 → 133 198 → 55 192 → >127 272 → 143 179 → 90

10 20 35 15 20 30 30 35 10 40 35

0.997 0.999 0.991 0.998 0.997 0.992 0.998 0.997 0.990 0.998 0.995

102 (6) 95 (6) 98 (9) 100 (5) 102 (8) 101 (8) 97 (6) 98 (4) 98 (9) 77 (7) 100 (5)

5 5 5 5 5 5 5 5 5 5 5

Nonachlor, cisNonachlor, trans-

4.737 4.458

409 → 300 409 → 300

25 25

409 → 145 409 → 145

15 15

0.998 0.995

90 (7) 94 (10)

5 5

Oxadixyl Oxyfluorfen

4.758 4.538

163 → 132 252 → 146

5 40

163 → 117 252 → 170

25 40

0.999 0.997

99 (6) 99 (6)

5 5

Parathion Parathion-methyl Penconazole Pendimethalin Pentachloroanisole Pentachlorothioanisole Permethrin, cisPermethrin, transPhenylphenol, oPhorate Phosalone Phosmet Phosphamidon Phthalimide Piperonyl butoxide Pirimiphos-ethyl Pirimiphos-methyl Procymidone Profenophos Propachlor Propargite Propazine Propetamphos Propham Propiconazole Propoxur Propyzamide Pyrimethanil

4.097 3.883 4.253 4.237 3.539 4.044 5.380 5.406 3.102 3.452 5.186 5.055 3.857 2.988 4.927 4.175 3.995 4.319 4.496 3.285 4.916 3.571 3.609 2.968 4.869 3.271 3.634 3.661

291* → 109 263* → 109 159 → 89 252 → 162 280* → 237 296* → 263 183 → 168 183 → 168 170* → 115 231 → 129 182 → 111 160 → 77 127 → 109 147 → 103 176 → 131 318 → 166 290 > 125 285* → 96 337 → 267 176 → 77 135 → 107 214 → 172 138 → 110 179* → 93 259 → 69 110 → 63 173 → 145 198* → 118

10 15 35 10 20 15 15 15 35 25 20 25 10 5 10 15 25 10 15 30 10 5 5 20 10 30 15 35

291* → 81 263* → 79 248 → >157 252 → >191 280* → 265 296* → 246 183 → 153 183 → 153 170* → 141 231 → 175 182 → 75 160 → 51 264 → 127 147 → 76 176 → 103 318 → 182 290 → 151 285* → 67 337 → 188 176 → 92 135 → 77 229 → 58 138 → 64 179* → 137 259 → 173 110 → 64 173 → 109 198* → 183

30 30 25 5 10 35 15 15 20 10 40 40 10 30 25 10 20 40 30 15 30 15 15 5 15 15 30 15

0.996 0.999 0.997 0.998 0.995 0.996 0.994 0.997 0.982 0.986 0.999 0.990 0.998 0.988 0.998 0.999 0.998 0.996 0.999 0.995 0.996 0.999 0.999 0.990 0.997 0.999 0.997 0.995

100 (6) 100 (6) 95 (5) 92 (7) 82 (9) 63 (7) 91 (7) 94 (8) 97 (7) 97 (14) 99 (5) 103 (9) 95 (9) 95 (8) 98 (6) 97 (7) 98 (7) 98 (6) 97 (5) 97 (10) 98 (7) 99 (4) 100 (5) 98 (8) 97 (6) 99 (7) 99 (5) 80 (7)

5 5 5 5 5 5 5 5 5 5 5 5 5–25 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

Quintozene (PCNB)

3.644

295* → 237

20

237 → 143

30

0.996

88 (10)

5

Resmethrin

4.933

123 → 81

5

171 → 128

15

0.998

93 (7)

5

(ng/g)

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Table 1 (Continued) Avg R2

Avg tR

MRM transitions (m/z)

Avg Overall % recovery (%RSD)

LCL

Analyte

(min)

Quantifier

CE (V)

Qualifier

CE (V)

n=4

Simazine Sulprofos

3.536 4.768

201* → 186 322* → 97

5 25

186 → 96 322* → 156

10 10

0.996 0.999

94 (8) 97 (5)

5 5

Tebuconazole Tecnazene Terbufos Terbutylazine Tetrachlorvinphos Tetraconazole Tetradifon Tolclofos-methyl Triadimefon Triazophos Trifluralin Triphenylphosphate (QC)

4.923 3.283 3.625 3.617 4.401 4.129 5.153 3.905 4.107 4.776 3.388 4.932

251 → 125 261* → 203 231 → 129 214 → 71 331 → 109 336 → 218 356 → 159 265 → 250 208 → 181 161 → 134 306 → 264 326* → 169

20 20 25 20 25 15 15 15 5 5 5 35

251 → 127 203 → 83 231 → 175 214 → 104 331 → 79 336 → 156 356 → 229 265 → 93 208 → 111 161 → 91 264 → 160 326* → 77

25 25 10 20 35 30 15 25 20 25 15 40

0.994 0.995 0.998 1.000 0.994 0.996 0.997 0.999 0.998 0.999 0.983 n/a

97 (8) 93 (12) 96 (9) 97 (4) 102 (9) 100 (6) 96 (6) 98 (5) 99 (5) 99 (5) 94 (13) n/a

5 5 5 5 5 5 5 5 5 5 5 5

Vinclozolin

3.876

212 → 145

30

212 → 109

40

0.996

97 (7)

5

(ng/g)

n/a = not applicable.

equivalent standards, plus 100 ng/g of each I.S. (atrazine-d5 and fenthion-d6 ) and the QC compound (TPP); the analyte protectants (ethylglycerol, gulonolactone, sorbitol, and shikimic acid) were 1, 0.1, 0.1, and 0.05 mg/mL, respectively, in all injected solutions. We kept all pesticide solutions and the final extracts in MeCN with 0.05% formic acid to help stabilize the pesticides that are prone to degrade under basic conditions [44]. 2.3. Sample preparation method We used the same QuEChERS protocol that was previously validated using LP-GC/ToF for analysis [11]. All samples (≈750 g) were cut into approximately 2–4 cm3 portions, placed in a freezer until frozen, and comminuted separately in a 2 L Robotcoupe (Jackson, MS, USA) RSI 2Y1 chopper with dry ice. The thoroughly homogenized samples were transferred to containers for each sample type and stored in a −18 ◦ C freezer until needed for sample preparation and analysis. The sample preparation procedure consisted of the following steps: (1) weigh 15 ± 0.1 g homogenized sample into a 50 mL polypropylene centrifuge tube; (2) add 300 ␮L of spiking solution and 150 ␮L of I.S. solution, and vortex the tube for 1 min; (3) add 15 mL of MeCN with 1% HOAc using a solvent dispenser and shake the tube vigorously for 30 s by hand; (4) add 6 g of anh. MgSO4 and 1.5 g of anh. NaOAc; (5) shake the tube immediately and vigorously for 1 min by hand to prevent the agglomeration of MgSO4 salts; (6) centrifuge the tube at 3250 rcf for 2 min; (7) transfer 1 mL of MeCN extract (upper layer) to the d-SPE tube containing 150 mg anh. MgSO4 + 50 mg PSA + 50 mg C18 + 7.5 mg GCB; (8) mix the extract thoroughly for 30 s and centrifuge the d-SPE tube at 3250 rcf for 2 min; (9) transfer 0.5 mL of the extract into an autosampler vial; (10) add 50 ␮L of the QC and analyte protectants mixture and 50 ␮L MeCN (to make sample volumes equal those of the calibration standards), and mix thoroughly for LP-GC/MS–MS analysis. 2.4. LP-GC/MS–MS conditions Analysis was performed on an Agilent 7000A triple–quadrupole mass spectrometer interfaced to an Agilent 7890A GC (Palo Alto, CA, USA), which was equipped with a 220 V fast oven heating upgrade (instead of the 110 V standard oven in the USA) and multimode inlet (MMI) that we operated as a programmable temperature vaporizer (PTV). The injection volume was 5 ␮L into a sintered glass liner (Agilent; P/N 5190–1426) placed inside of the standard single tapered liner 4 mm i.d. Injection was conducted by a MPS2 autosampler

controlled by Maestro software (Gerstel Corp., Linthicum, MD, USA) using a 10 ␮L syringe. The optimized injection conditions involved solvent venting at a split flow of 50 mL/min with an initial injector temperature of 80 ◦ C for 0.31 min, and then the vent was closed and the injector was ramped to 320 ◦ C at 420 ◦ C/min where it was held for the entire GC run (9.5 min). The split flow of 50 mL/min was restarted at 4 min. To prevent the syringe plunger from sticking due to dried-out residues of the analyte protectants, the syringe was washed 8 times each with acetone/MeCN/water (1/1/3, v/v/v) followed by MeCN/water (1/1, v/v). The GC separation was conducted on a 10 m × 0.53 mm i.d. × 1 ␮m film thickness Rti-5ms analytical column coupled to a 3 m × 0.15 mm i.d. HydroGuard non-coated restriction capillary (both from Restek, Bellefonte, PA, USA). The virtual column length entered into the GC configuration was 3.13 m, 0.15 mm i.d. (vacuum outlet) according to a previous calculation using Leco (St. Joseph, MI, USA) ChromaTOF 3.22 software [11,12]. Ultra-high purity helium (Airgas, Radnor, PA, USA) was used as the carrier gas at 2 mL/min constant flow rate. A GC column union (Agilent; P/N 0101-0594) was used to couple the capillary columns wherein the micobore restrictor fit inside the megabore analytical column to form a zero dead volume connection. The same type of union was used to connect the analytical column to a ≈30 cm × 0.53 mm i.d. uncoated capillary (Restek) at the transfer line. In this way, the analytical column would not be exposed to the hot transfer line even when the column oven was cool. This spares the column coating from degradation if O2 or H2 O enter the system (provided that the column is kept less than ≈100 ◦ C) [45]. The final extracts contain ≈4% water and ≈0.05% formic acid in MeCN, most of which is vented during injection, but we wished to avoid potential damage to the column coating if the ≈0.2 ␮L injected water per 5 ␮L extract reaches the hot transfer line. The oven temperature was programmed at an initial temperature of 70 ◦ C (held for 1.5 min), then ramped at 80 ◦ C/min to 180 ◦ C, then to 250 ◦ C at 40 ◦ C/min, followed by a 70 ◦ C/min ramp to 290 ◦ C, at which temperature it was held for 4.3 min. An oven insert pad (Agilent part G2646-60500) was used to reduce the oven size which enabled slightly faster heating and cooling. More importantly, a greater consistency of analyte retention times was achieved using the oven pad and heating upgrade on the GC. The GC re-equilibration time was less than 3 min. The MS transfer line and ion source temperatures were set at 250 ◦ C and 320 ◦ C, respectively. Electron ionization energy of −70 eV was used with a filament-multiplier delay of 3 min. The default instrument settings of filament current 35 ␮A, collision

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gas flow of N2 1.5 mL/min and He 2.35 mL/min, and quadrupole temperatures 150 ◦ C were used in all MS/MS experiments. The detector voltage was automatically set by the instrument after automated MS/MS tuning, which was typically 1250 V. A full autotune of the mass spectrometer using the default parameters of the instrument was performed before each sequence. Agilent MassHunter was used for instrument control and data acquisition/processing. For the final multiple reaction monitoring (MRM) acquisition method, 2 ion transitions at the experimentally optimized collision energy (CE) were monitored for each analyte, and dwell time of 2.5 ms was set for all transitions (interdwell delay of 1 ms). The “wide” MS resolution setting of 1.2 amu full width at half maximum was entered into the MRM method for all transitions. To detect all 153 analytes (150 pesticides + 2 I.S. + QC), the MRM method was divided into 26 time segments which consisted of 60 transitions in each segment during the GC run. Table 1 summarizes the optimized MS/MS conditions for the individual analytes and their typical retention times (tR ). Supplemental information also provides an electronic file showing the CE optimization experimental results for each pesticide. 2.5. Method validation The method validation in terms of recovery, repeatability, and reproducibility was carried out for each sample matrix in 6 replicates at 3 spiking levels: 10, 75, and 400 ng/g. The sequence of the validation experiments was cantaloupe, broccoli, sweet potato, and lemon. Sample sequences of each sample type consisted of the reagent blank, 7 reagent-only standards, 7 matrix-matched standards, 18 spiked sample extracts, and 2 sample blank extracts. The calibration standards were distributed among the sample extracts, and the reagent blank was injected after the highest concentration standard (600 ng/g) to check for carryover. Atrazine-d5 was used as the I.S. in all cases (which was slightly more consistent than the backup I.S., fenthion-d6 ). Matrix-matched standards at the given spiking levels were used to calculate the analyte recoveries. Solvent-only calibration standards were used to estimate the matrix effect (ME) for each analyte, which were calculated as the %difference in the sum of peak areas for matrix-matched (MM) standards vs. reagent-only (RO) standards between 5 and 400 ng/g: ME = (sum of MM standards − sum of RO stds)/(sum of RO stds). Injection liners were replaced after ≈70 injections (2 injection sequences). 3. Results and discussion 3.1. Optimization of LP-GC/MS–MS temperatures and flow Our starting point was to transfer the previous LP-GC/ToF method [11,12] developed for the same pesticide list to a modern triple–quadrupole MS/MS. We anticipated that sensitivity and selectivity of the approach would be much improved using MS/MS for the targeted analytes, but of course, we would lose the potential to search full spectral information for other compounds not targeted. Rather than using spectral matching factors or a heuristic approach as we evaluated in ToF [12], analyte identification in MS/MS was based on the ion ratios between 2 transitions. The qualitative validation study was extensive and too lengthy to be presented in this method optimization and quantitative validation paper; thus qualitative aspects will be presented in a separate report. The initial method transfer step was to choose appropriate GC parameters to achieve <10 min run time for the 150 pesticide analytes. We used the same LP-GC column configuration

Fig. 1. Influence of the column flow rate (1–10 mL/min) on the peak height and tR of 1 ng injected deltamethrin (last analyte to elute) obtained from the LP-GC/MS–MS approach using the standard (110 V) oven heating element (220 V upgrade further reduced tR by 0.9 min).

that was demonstrated to provide acceptable speed, performance, and ruggedness both in ToF [11,12] and SIM [23]. One difference was that the stronger turbomolecular pumping in the MS/MS instrument allowed better use of constant flow rather than the previous use of constant head pressure at 20 psi (138 kPa) [11,12,23]. Based on the Leco calculation software used previously [11,12], we entered into the GC configuration the calculated equivalent column dimension of 3.13 m × 0.15 mm i.d. with vacuum outlet for use in electronic pressure/flow control. In this way, the column head pressure was continuously adjusted to keep the flow rate constant in conjunction with oven temperature, leading to slightly faster analysis, sharper peaks for later-eluting analytes, and better tR reproducibility. Fig. 1 shows peak intensities and tR of deltamethrin (the last analyte to elute) vs. constant column flow rate of 1, 2, 2.5, 3, 4, 5, 7.5, and 10 mL/min. We found that 2 mL/min provided the greatest peak height for deltamethrin, which was also the case for most of the other 150 analytes in the mixture. Lower column flow rate resulted in shorter and wider peaks for late-eluting analytes, whereas higher flow rate provided slightly faster analyses and sharper peaks, but at the cost of reduced MS responses of the analytes, as shown in Fig. 1 for deltamethrin. We chose to use 2 mL/min constant flow rate for the sake of typical maximal response, which gave a tR of 7.1 min for deltamethrin with the 110 V standard oven heating unit in the U.S. When we upgraded the GC with a 220 V rapid oven heating unit we were able to achieve tR of 6.2 min for deltamethrin with the same oven temperature program as given in Section 2. Until we made the upgrade, the tR consistency was poor, which created problems when setting MRM segments and making analyte identifications based in part on tR . The oven insert pad also helped to reduce oven heating time (max rate) from 70 ◦ C to 290 ◦ C by ≈0.8 min and cooldown time by ≈0.15 min. With a 3 min post-deltamethrin bake out, the total analysis time of the LP-GC method became 9.5 min. We also studied the influence of temperatures of the transfer line (230–290 ◦ C), ion source (200–320 ◦ C), and quadrupoles (120–180 ◦ C) on signal responses. No significant difference in analyte responses were observed vs. temperature except for the ion source, in which case the trends in responses varied depending on the analyte. Fig. 2 gives examples for diazinon and bromopropylate calibration curves vs. ion source temperature. Increasing environmental temperature to which the molecule is exposed causes a more pronounced fragmentation pattern toward lower molecular weight ions [46]. This effect is indirectly shown in Fig. 2; the molecular ion of m/z 304 was chosen for diazinon, which gave decreasing MS/MS response vs. increasing temperature, but a fragment ion

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Concentration (ng/mL) Fig. 2. Calibration of 1 ␮L injection volume (in MeCN) for diazinon and bromopropylate in LP-GC/MS–MS at different ion source temperature settings. The precursor ion for diazinon is the molecular ion (designated by an asterisk), which decreases in intensity as temperature increases, whereas the opposite trend occurs when the precursor ion originates from a fragment ion, as shown for bromopropylate.

of m/z 341 was used for bromopropylate (MW = 428 g/mol), which gave the opposite trend as diazinon. As discussed in Section 3.3, we chose molecular ions as the precursor when it was deemed to be advantageous, but it was more common to select a fragment as the precursor ion. Therefore, the responses of most analytes followed the trend of bromopropylate in Fig. 2. For this reason, the ion source temperature of 320 ◦ C was selected as the overall compromise in the final method, and for the other settings, the instrument default values of the transfer line at 250 ◦ C and quadrupoles at 150 ◦ C were used. The heating element and temperature gauge are somewhat removed in distance from the ion source in the Agilent 7000A, so that is not to say that the actual temperature in the ion source was 320 ◦ C, but that was the setting on the instrument. 3.2. Injection optimization and detection limits For simplicity, we initially used splitless mode of 1 ␮L for method optimization instead of PTV [47]. Ideally, splitless (or even better, split) injection of the minimum equivalent sample amount would be used provided that we could still meet LOQ <10 ng/g with 1 g/mL equivalent final QuEChERS extracts. However, we needed to inject more volume (or more concentrated extracts) to meet detectability needs, particularly for deltamethrin and similar late-eluting analytes. Solvent evaporation of extracts defeats the purpose of “quick” in QuEChERS, thus large volume injection using a PTV becomes the better option.

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Among many PTV liners, a wall-coated sintered-glass liner was found to be very useful for multiresidue analysis of pesticides because the liquid extract is absorbed by the pores of the sintered glass [11,12,47]. Adsorption of pesticides and carry-over are potential concerns with this type of liner, but the solvent evaporation is very fast due to high surface area. This leads to a shorter vent time at lower initial temperature and reduced thermal degradation of labile pesticides. We used analyte protectants in this study to address analyte adsorption losses [41–43] and checked for carryover in every sequence by injection of a reagent blank immediately after the highest calibration standard. Agilent did not manufacture a wall-coated sintered-glass liner designed for their MMI product, thus we chose to use a small liner (2 mm i.d.) of this type that fit inside the standard (4 mm i.d.) single taper liner. The syringe needle entered into the smaller internal liner to deposit the liquid sample, and no injection problems occurred with this approach. We conducted several experiments initially involving injection of 10 ␮L standards in MeCN to optimize parameters for solvent venting, analyte transfer, and initial oven temperature. Ultimately, the final optimized conditions reported in Section 2 were very similar to those found to be optimal previously using a different PTV device [11]. An experiment was conducted using the final conditions to determine the minimum injection volume suitable at the optimal conditions to meet needs for LOQ < 10 ng/g. Injection volumes of 2.5, 5.0, 7.5, and 10 ␮L were tested using a 25 ␮L syringe in the autosampler. As the injection volume increased, the peak heights (and signal/noise ratios) increased in direct proportion for nearly all analytes with consistent peak widths and shapes. The consistent 1:1 amount:signal relationship was strong evidence that transfer efficiency and reproducibility of injection was high at the chosen conditions. The results indicated that 2.5 ␮L injection was just adequate to meet LOQ needs for all analytes, whereas 5 ␮L was more than sufficient. We did not seek to push LOQs to the lowest possible level, but the greater volume (5 ␮L) gave more room for day-to-day fluctuations in instrument performance. This is half of the 10 ␮L injection volume we used in LP-GC/ToF, and we were able to easily improve upon the 10–25 ng/g LOQs obtained in that approach. Newer models of GC–MS/MS instruments may allow use of 1 ␮L splitless injection rather than PTV to achieve the same levels of detection. We did not estimate the LOQs for each analyte in this study, which is a difficult and commonly inaccurate process using MS/MS due to non-existent electronic noise and/or highly variable chemical noise depending upon the matrix [48]. Instead, we followed the fit-for-purpose approach to determine the lowest calibrated level (LCL) of the method through injection of matrix-matched standards. Table 1 gives the LCLs for the pesticides in the final method for all 4 tested matrices, and nearly all analytes were able to be quantified and identified at the required 5–10 ng/g in all matrices. Only the typical pesticides that are known to chemically and/or thermally degrade (captan, captafol, folpet, and kepone) continued to give the same problems as in previous studies [11,12,44]. Fenoxycarb and phosphamidon gave higher LCLs in broccoli and lemon, respectively, due to greater matrix interferences (more chemical noise). 3.3. Choice of MRM ion transitions Rather than simply rely on the MRM conditions reported by others [49,50], we chose to conduct an independent test to determine the optimal ion transitions and collision energy (CE) voltages in our method. In our experiments, we monitored the most abundant ions vs. CE for each pesticide and plotted the precursor and fragment ion intensities graphically, which can be found in supplementary information included with this article.

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The first step involved the choice of precursor ions, and the fullscan mass spectra (m/z 50–550) and NIST’05 mass spectral database were used to help in the choices. Ideally, both selectivity and sensitivity would be maximized with the selection of the optimal ions, but both facets were difficult to measure with so many possible combinations of precursor ions and potential interferences from chemical noise. Another complicating factor was that the dwell times used may not have been ideal (we tried both 20 and 300 ms). The inherent problem is that the concentrations across the analyte peaks were changing as CE was also changing. Thus signal intensities vs. CE were not at a consistent analyte concentration, which reduced confidence in being able to make the ideal selection of ion transitions. A previous study has shown that background interferences in complicated matrices (e.g. spices) decrease by a factor of 20–30 for every increase of 100 amu [51]. Therefore, it usually provides the greatest benefit to choose the highest mass ion as the precursor unless it has relative abundance vs. the base peak less than ≈10%. Table 1 lists the pesticides and their ion transitions chosen for analysis. An asterisk appears next to those analytes in which the molecular ion was used as the precursor ion, which accounts for 20% of the 150 pesticides. We chose the molecular ion somewhat more frequently than others did [49,50], and although this may reduce sensitivity (response vs. concentration), we believe this provides a greater degree of selectivity and lower detection limits in complicated matrices. In our experiments, CE settings of 5, 10, 15, 20, 25, 30, 35, and 40 V were optimized by running individual analyte standard injections of 1 ng. For qualitative purposes, it was required to monitor 2 ion transitions for each pesticide (300 transitions total for 150 pesticides). The most intense transition would be chosen as the quantifier ion and the second strongest transition served as the qualifier or diagnostic ion. The choice of product ions was usually made on the basis of the two most abundant fragments at the CE value where nearly complete dissociation of the same precursor ion occurred. In some cases, however, the second-most intense ion transition was believed to be too weak or not sufficiently selective; so we resorted to using a different precursor ion which generated a more intense and/or unique product ion for qualitative purposes. These instances can be observed in Table 1 when a different precursor ion was chosen for identification purposes than for quantification. This approach is also unique compared to previous studies [49,50], and identifications can be made at lower levels when a more intense diagnostic ion transition is used. Once the final choices were made, the MRM conditions were tested in mixed sample extracts spiked or not with the 150 pesticides to evaluate the sensitivity and selectivity of the method. Ultimately, LCLs of 5 ng/g were easily achieved in the matrices at the final conditions as reported in Table 1, and a variety of different ion transitions and CE settings could have been used to attain the same outcome. 3.4. Setting of dwell times and MRM segments In LP-GC/MS(–MS), chromatographic separation efficiency is sacrificed for speed which causes many peak co-elutions when analyzing 150 pesticides with tR from 2.9 to 6.2 min. Only 79 compounds could be baseline-resolved in this 3.3 min time frame for 2.5 s wide peaks if it were not for the exceptional selectivity provided by MS(/MS) detection. The ability to quantify and identify all 153 targeted compounds using MS/MS with 2 transitions each requires fast data acquisition. Despite the sacrifice in chromatographic resolution, a benefit of LP-GC in this regard is that peak widths are typically the same as in traditional GC (2–3 s), and unlike applications using micro-bore columns [21], exceptionally fast data acquisition is not needed to define chromatographic peaks.

However, it remains a complicated process to set multiple MRM time segments to cover all targeted analytes along the chromatogram where there are few empty spaces for segment changes. Although Agilent has a software utility known as Dynamic MRM Assistant to help devise a series of MRM segments in a multi-analyte method, we found that it was not useful for our application. In fact, our attempts to use it led to a situation in which the cycle times changed during the middle of chromatographic peaks. The data acquisition differences from one segment to another caused notches in the peaks at the segment borders, which led to peak integration errors among other problems. It was virtually impossible to find enough places along parts of the chromatogram where MRM segments could be stopped and started without occurring in the middle of at least one of the analyte peaks. The solution was to maintain constant dwell and cycle times across each analyte peak even when segments ended and started in the middle of a peak. The only way to hold cycle and dwell times constant (with fixed electronic interdwell delay times of 1 ms) was to include the same number of ion transitions in each segment. We then sought to devise a reliable, simple, and consistent procedure to meet our required objectives for quantitative and qualitative purposes at 5 ng/g concentrations (25 pg injected amounts) of the 150 target analytes using 2 transitions each. As in dynamic MRM, our concept theoretically entailed a mathematical approach based on the peak widths, desired number of points across the peaks, the dwell and interdwell delay times, number of closely eluting analytes and their tR , and the number of ion transitions. Fortunately, this formula was greatly simplified by requiring fixed cycle times in all segments. As described in the literature [21], ≥5–6 points across a peak show acceptable precision in integrated peak measurements, and ≥8 points across a peak provide a well-defined chromatographic peak shape. Because the cycle time consists of the number of transitions multiplied by the dwell plus interdwell delay times, the choice to achieve ≥8 points/peak dictated that for 2 s wide peaks the data acquisition rate would have to be ≥4 Hz (or cycle time ≤250 ms). Using 250 ms as the cycle time and 1 ms interdwell delay for the instrument leads to the relationship that dwell time (ms) equals (250 − N)/N, where N is the number of ion transitions. The minimum dwell time on the instrument is 1 ms, thus the maximum N to achieve 8 points across a 2 s peak is 125 (this corresponds to 62 analytes per segment for 2 transitions/analyte). Our application entailed 150 pesticides and 300 ion transitions, thus in retrospect, we may have been able to use 3 segments of 100 transitions each with 1.5 ms dwell times to conduct the analyses. We conducted an experiment to estimate the analyte sensitivities and other factors for the 150 analytes vs. dwell times of 1, 2.5, 5, and 10 ms when N = 60 (30 pesticides per segment). Fig. 3 shows the peaks for 250 pg injected heptachlor (tR = 3.9 min). Calibration curves ranging from 5 to 750 ng/mL for the pesticides showed no significant differences in the detection sensitivities (slopes) vs. dwell times from 1 to 10 ms. The quantification ion for the 5 ng/mL standard was detected in all cases, but we held some concerns about achieving the desired consistency of the ion ratios using 1 ms dwell times. Longer dwell times provide more consistent signals, thus we wished to maximize dwell times, not minimize them. Although 5 ms dwell time was acceptable to achieve 8 points across 2.7 s wide heptachlor peaks as shown in Fig. 3, the software-reported peak widths narrowed to as little as 1.5 s at the lowest concentrations, which yielded only 4 points/peak. Therefore, we chose to use constant dwell time of 2.5 ms for all analytes (4.8 Hz data collection rate) as a more reasonable setting overall. Use of additional segments with fewer transitions was another option to allow longer dwell times, but it was already challenging to choose start and stop times for segments of 30 pesticides each. We sorted the pesticides by their tR and started with the first group

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Fig. 3. Influence of dwell time on the chromatographic peaks of 0.25 ng injected heptachlor when 60 ion transitions were used per segment (1 ms interdwell delay time) in the LP-GC/MS–MS analysis. Peak heights and areas were very similar in each case. Dwell times of 2.5 ms were used in the final method.

of 30. We added and subtracted 0.2 min from each tR to ensure that the corresponding pesticide transitions would be included in all segments covering at least the 0.4 min (24 s) chromatographic time window designated for each analyte. The data collection time was sliced into the minimum interval possible with the software (0.05 min), and when the time interval reached the end of a pesticide data collection window, the ion transitions for that pesticide (or multiple pesticides) would be dropped from the segment and an equal number of pesticides would be added to the next segment to maintain a total of 30 pesticides (60 transitions) per segment. The time segment program is provided in Supplemental information to show this process and final method. As few as one pesticide and as many as 8 pesticides were dropped/added between successive time segments, which led to a total of 26 segments. The segments would have to be modified as additional analytes are added, which is not an easy process manually, but the math is not complicated as described above, and we would expect that future software versions for MS/MS instruments would provide an option to automatically set the MRM segments in this fashion. 3.5. Effect of analyte protectants (APs) on analyte responses In GC analysis, the matrix-induced chromatographic response effect is generally observed for analytes with relatively polar functional groups (e.g. –OH; P O; −NH2 ) in complex matrices [52,53]. In the absence of matrix, the susceptible analytes are prone to interact with active sites in the inlet, column, and MS(/MS) source, causing losses and a reduced signal. Matrix components fill the active sites in sample extracts, thereby reducing analyte losses and increasing responses. The AP concept entails the addition of polar chemicals to saturate the active sites and minimize analyte losses which maximizes analyte response [1,41–43]. APs are sugar derivatives that break down in the inlet and column, which allow them to continue filling active sites as the analytes across a broad volatility range make their way through the column. Ethylglycerol, gulonolactone, and sorbitol were previously demonstrated to work well for most pesticides [41–43], and shikimic acid was reported to more effectively protect base-sensitive pesticides, such as captan, folpet, tolyfluanid, and diclofluanid [54]. Fig. 1S (Supplemental information) shows the effectiveness of APs in terms of the peak shape and response of two particularly susceptible pesticides (dimethoate and folpet) at 50 ng/g spiked in mixed sample extracts (equal parts of broccoli, lemon, cantaloupe, and sweet potato combined) with and without APs added. As the figure demonstrates, signal intensity and peak shape improved considerably using the APs. Currently, it is common practice in pesticide residue analysis to employ matrix-matching for calibration to compensate for the matrix-induced chromatographic response enhancement effect [53,55]. The amounts of APs added are intended to saturate the active sites in the system, which ideally maximize analyte

responses and completely compensate for MEs. In this way, the APs can be added to final extracts with sample matrix and calibration standards in reagents-only for accurate quantification without the need for matrix-matching. Fig. 4 gives the calibration slopes (sensitivities) normalized to the solvent-based standards containing the APs for several pesticides generated from injection of calibration standards at 0, 2.5, 5.0, 10, 20, 40, 80, 160, 320, and 640 ng/g using the 4 different calibration approaches (reagent-only and matrixmatched both with and without APs added). The matrix used was a mixture of equal part extracts of broccoli, lemon, sweet potato, and cantaloupe. Fig. 4 demonstrates that the use of APs was helpful to improve sensitivity of the analytes in all evaluated cases, particularly for highly susceptible pesticides such as mevinphos and coumaphos (and dimethoate and folpet as shown in Fig. 1S). The APs and their breakdown products yield relatively small ions that do not directly interfere in MS, even in full-scan mode, but the high concentrations of co-eluting compounds may cause problems by filling ion-traps to cause indirect effects on quantification or adversely affect MS deconvolution programs in full-scan operation. The targeted approaches of SIM and MS/MS with quadrupole instruments avoid these potential problems altogether, and we felt confident that the APs would yield all their benefits without drawbacks in this LP-GC/MS–MS method. However, a similar phenomenon in the LP-GC/MS–MS approach was observed as reported previously in that the APs alone do not fully compensate for MEs of those pesticides at the end of the chromatogram [43]. The chosen pesticides in Fig. 4 are listed in elution order from left to right, and coumaphos and deltamethrin showed

Fig. 4. Comparison of the LP-GC/MS–MS sensitivities (slopes of 9-point calibration curves ranging from 2.5 to 640 ng/g) for selected pesticides when adding analyte protectants (APs) or not in calibration standards prepared in MeCN or in mixed extracts from 4 matrices (cantaloupe, broccoli, sweet potato, and lemon) normalized to the results using APs in MeCN. Analytes are listed in order of increasing tR from left to right. The differences in slopes affects quantification depending on how closely the calibration standards match the samples in terms of matrix effects.

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less than maximal sensitivity when using the APs alone. This occurs because sorbitol and its breakdown products do not fully co-elute with those less volatile pesticides to mask active sites in the system during that part of the chromatogram [43]. Thus, we could still not use the AP concept alone for accurate quantification purposes and resorted to the use of matrix-matched calibration standards plus additions of APs for quantification. 3.6. Method validation At this stage in the study, we had completed method optimization and were ready to perform the quantitative method validation experiments. However, one final detail remained: carry-over was observed for the most sensitive pesticides, such as the BHCs, diphenylamine, 4,4 -dichlorobenzophenone, and DDD/DDE/DDTs, especially after the injection of high concentration of standards. This problem was probably exacerbated by the use of the wallcoated sintered-glass liner, which was also found to be a factor previously [12]. The extent of carry-over was measured to be 0.3–0.5% for the observed analytes, which would cause a high bias by as much as 30% if a 10 ng/g sample was injected after the 600 ng/g standard. Subsequent use of different liners still has not fully resolved the problem. The easiest way to avoid the carry-over in the validation study was to inject nothing (0 ␮L of air) immediately after the injections of the high-standard concentrations (400 and 600 ng/g) and high-spike extracts. For real sample analyses, concentrations <10 ng/g would not be reported, and the analyst would have to remain mindful of the extent of carry-over in samples injected immediately after samples with high concentrations. The MS/MS approach is so sensitive that carry-over and ultra-trace lab contamination pose a problem in many applications [48]. Among isomeric compounds that gave the same ion transitions, ␤-BHC and lindane (␥-BHC) were partially co-eluted, which affected peak integration, but they could be distinguished individually by their tR difference of 1.8 s (␤-BHC 3.592 min and lindane 3.622 min), except in broccoli due to a matrix interferant. For o,p DDT and p,p -DDD, the analytes co-eluted, and we integrated and quantified these compounds as one peak. For pyrethroids that formed multiple chromatographic peaks (cyfluthrin, cypermethrin, and flucythrinate), all peaks were integrated together. 3.6.1. Linearity and matrix effects (MEs) Linearity of the method was evaluated both in reagent-only and matrix-matched standards for each matrix/sequence during method validation. The lowest calibration standard of 5 ng/g was chosen to evaluate the performance of the method at concentrations lower than the default MRL of 10 ng/g for unregistered pesticides in the sample commodities. All integrated peak areas were normalized to atrazine-d5 (fenthion-d6 served as a backup) and as listed in Table 1, the 7-point calibration plots in each matrix were linear with average (n = 4) regression coefficients (R2 ) ≥0.990 for all pesticides with the exception of a few known problematic ones (the lower R2 values are given in bold text in Table 1). Ideally, the calibration curves using reagent-only and matrixmatched standards would exactly coincide with each other, but MEs tend to occur for many pesticides, as described in Section 3.5. During validation experiments for each commodity, we measured the MEs as differences in the sums of the peak areas between the LCL (typically 5 ng/g) to 400 ng/g from the two types of calibration standards; the final results are shown in Supplemental information. We chose to use the sum of peak areas in this range rather than the slopes of the calibration curves to provide greater accuracy. The slopes can be inaccurate if the 600 ng/g standard is beyond the linear dynamic range or if R2 is not >0.99. Fig. 5 summarizes the degree of MEs for the analytes in the different matrices when the IS was used or not. The IS not only helps to compensate for volumetric

Fig. 5. Summary of matrix effects (MEs) for the 150 pesticides in the different commodities when using APs in both reagent-only and matrix-matched standards. Use of 100 ng/g atrazine-d5 as the internal standard (IS) compensated for many of the differences due to MEs.

biases in the method, but also serves to reduce the extent of MEs. The cantaloupe and sweet potato gave cleaner final extracts and provided small differences compared to the cases of broccoli and lemon. This is not a surprising finding for cantaloupe, but sweet potato was expected to give greater problems due to high levels of fatty acids and sugars [56], which are known to give difficulties in traditional GC–MS analysis. 3.6.2. Recoveries and reproducibilities Table 1 and Supplemental information summarize the recoveries and reproducibilities of the pesticide analytes in all matrices. Fig. 6 also shows the distribution of these results in the different commodities. The vast majority (95–97%) of the analytes gave recoveries between 70 and 120% with <20% RSD which is an

Fig. 6. Distribution of overall recoveries and RSD of the 150 pesticide analytes in cantaloupe, broccoli, sweet potato, and lemon using the new updated QuEChERS extraction and LP-GC/MS–MS analysis.

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accepted range for quantitative pesticide residue methods [55]. Low recoveries (<70%) occurred for only ≈3% of the analytes. Some of these (chinomethionat, cyprodinil, hexachlorobenzene, and pentachlorothioanisole) are structurally planar, which are known to be partially removed by the 7.5 g GCB in the d-SPE cleanup step [5]. The consistency of the results (<10% RSD) for these pesticides independent of the different matrices over multiple days presents the option that results could be corrected for the known recovery factors in analyses [15]. We conducted d-SPE quickly in a consistent manner in the validation study, and it is conceivable that longer and/or inconsistent exposure of the pesticides to the GCB may cause greater variations in the results. As part of the validation, we also determined the IS compounds recoveries and reproducibilities spiked at 100 ng/g in 140 injections covering 4 different matrices over 4 days. The recoveries were 95–103% and RSDs were 6–11% for cantaloupe and sweet potato extracts, whereas the recoveries ranged from 87 to 95%, and higher RSDs of 16–27% were observed in broccoli and lemon extracts. A similar trend was observed for the QC compound, TPP, which was added to the final extracts before analyses to isolate the analytical step from the sample preparation procedure. TPP gave average recovery of 100%, as expected, with ≤13% RSD for cantaloupe and sweet potato (which both used new liners) and 89–105% recoveries with 20–29% RSD for broccoli and lemon extracts (old liners). Perhaps the liners contributed to worse performance for broccoli and lemon matrices, but they were also more complex matrices. Recoveries >120% were observed for azinphos-methyl (129%), captafol (129%), captan (174%), and folpet (194%) in broccoli and lemon extracts, which contain a large amount of chlorophyll, fatty acids, citrus oil, pectin, etc. [56], whereas their recoveries were ≥90% with ≤10% RSD in cantaloupe and sweet potato. In terms of repeatabilities (intra-day precision), RSDs >20% were only found for a few pesticides (captan, chlorothalonil, ethoxyquin, fenoxycarb, folpet, and kepone). These results appear in Supplemental information and are summarized in Fig. 6. As also shown in Supplemental information, reproducibilities (inter-day precision) of the method were exceptional among the matrices. The overall quality of the results demonstrated the suitability of the method for quantification purposes using matrix-matching with APs. A surprising outcome in the use of the LP-GC/MS–MS method was substantially improved results for the problematic pesticides that had given high variabilities and poor recoveries with ToF [11]. These pesticides consist of captan, captafol, folpet, and chlorothalonil, which are known to degrade during sample preparation using MeCN at neutral/basic conditions, and/or during GC analysis. Results were still not ideal for these fungicides in all matrices (e.g. captafol disappeared in the sweet potato analyses), but much greater confidence was achieved in their determinations. Among other pesticides, ethoxyquin yielded poor results in broccoli due to matrix interferences. Other pesticides typically of greater difficulty in GC are those relatively polar ones commonly analysed by LC methods, such as carbaryl, dimethoate, demeton-s-methylsulfone, fenthion sulfone, atrazine, methiocarb, phosmet, oxadixyl, azinphos-methyl, and metribuzin. The fast LP-GC/MS–MS approach using cooler PTV injection, higher flow rate, and shorter residence times at hotter temperature, combined with the use of APs and bufferedQuEChERS extraction, yielded excellent results on par with the quality of (UHP)LC–MS/MS [39,40]. Pyrethroid pesticides are common indicators to determine overall quality of the method in multiclass, multiresidue analysis because they tend to provide lower sensitivities due to their late elution times (combination of poor transfer efficiencies, peak broadening, and elution during high column bleed). In this study, all pyrethroid compounds in sample extracts could be detected

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at LCLs ≤5 ng/g and gave acceptable average recoveries ranging from 86 to 108% with ≤10% RSD in all cases, except in lemon extracts which gave RSDs up to 17%. The overall high quality results for pyrethroids further demonstrated the superior performance of MS/MS, and LP-GC with constant flow rather than constant pressure also undoubtedly contributed to the improved results. 4. Conclusions A new LP-GC/MS–MS method was developed and validated for the multiresidue analysis of pesticides in 4 representative fruits and vegetables using the updated QuEChERS method. APs were also included in this study to increase analyte signals, particularly for relatively polar GC-amenable pesticides. This method provided high sample throughput of 20 extractions of pre-homogenized samples plus 35 injections per 9 h all done by a single chemist in the laboratory. Average recoveries (n = 72) were 70–120% for 144 of the 150 pesticides, and reproducibilities were <20% RSD for all but 4 analytes. This LP-GC/MS–MS method greatly outperformed the previous LP-GC/ToF method for the same pesticides in similar matrices in many respects: (1) greater selectivity led to much easier peak integration and identification; (2) at least 50% lower LOQs were obtained with 50% less injection volumes; (3) much less time was needed for data analysis processing; and (4) a substantially greater number of pesticides showed excellent recoveries and precision in the analyses. Triple quadrupole MS/MS was a powerful tool to avoid interferences from co-elutions in complicated matrices, and APs were useful to further increase sensitivities for quantification purposes, especially in the case of relatively polar GC-amenable pesticides. Unfortunately, MEs remain a concern in general in both GC and LC analyses [48]. Disclaimer Mention of brand or firm name does not constitute an endorsement by the U.S. Department of Agriculture above others of a similar nature not mentioned. The USDA is an equal opportunity employer. Acknowledgments The authors thank Philip Wylie and Tom Barrett of Agilent Technologies for helpful discussions about the instrument and software. The authors thank Lucía Geis-Asteggiante for helping in sample preparations and checking details in the manuscript. This work was supported by the U.S.-Israel Binational Agricultural Research and Development Fund grant US-4273–09. This research was also partially supported by Thailand Research Fund (TRF), The Royal Golden Jubilee Ph.D. Program (PHD0036/2550), Chulalongkorn University Graduate Scholarship to Commemorate the 72nd Anniversary of his Majesty King Bhumibol Adulyadej, and Higher Education Research Promotion and National Research University Project of Thailand; Office of the Higher Education Commission (FW648I). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2011.07.094. References ˇ [1] M. Anastassiades, S.J. Lehotay, D. Stajnbaher, F.J. Schenck, J. AOAC Int. 86 (2003) 412. [2] S.J. Lehotay, J. AOAC Int. 90 (2007) 485. [3] European Committee for Standardization (CEN) Standard Method EN 15662; Food of Plant Origin – Determination of Pesticide Residues using GC–MS and/or LC–MS/MS following Acetonitrile Extraction/Partitioning and Clean-Up by Dispersive SPE – QuEChERS Method 2008 (www.cen.eu and www.quechers.com).

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