Article pubs.acs.org/JAFC
Interlaboratory Comparison of a General Method To Screen Foods for Pesticides Using QuEChERs Extraction with High Performance Liquid Chromatography and High Resolution Mass Spectrometry Brian D. Eitzer,*,† Walter Hammack,‡ and Michael Filigenzi§ †
Department of Analytical Chemistry, The Connecticut Agricultural Experiment Station, 123 Huntington Street, New Haven, Connecticut 06511, United States ‡ Florida Department of Agriculture, 3125 Conner Blvd, Tallahassee, Florida 32399-1650, United States § California Animal Health and Food Safety Laboratory, University of California at Davis, West Health Sciences Drive, Davis, California 95616, United States S Supporting Information *
ABSTRACT: An interlaboratory comparison of a multipesticide residue analytical method is reported. The goal of the comparison was to evaluate the potential for liquid chromatography/high resolution mass spectrometry along with a specific automated screening procedure to allow the determination of the presence or absence of a set of targeted compounds without additional manual review. The method utilized an off the shelf QuEChERs based extraction followed by analysis with an orbitrap mass spectrometer with the data evaluated by ToxID. The method was tested at three laboratories, with three produce matrices (spinach, carrots, and oranges), and three levels of spiked pesticides with all analyses in triplicate. A series of 247 compounds were tested, and it was found that the three laboratories produced consistent data; however, manual review was still necessary. The data was shown to have no false negatives for 211 compounds in the three produce matrixes at 200 ppb. Of these 211 compounds, 189 had no false negatives at 50 ppb, and 129 had no false negatives at 10 ppb. The HRMS method was shown to be robust with similar data being achieved by all three laboratories and detectable concentrations only slightly above the range shown for triple quadrupole MS/MS. KEYWORDS: QuEChERs, pesticide residue, interlaboratory comparison, high resolution mass spectrometry
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INTRODUCTION
The extracts produced in these multiresidue techniques have been commonly used along with liquid chromatography tandem mass spectrometry. These combined techniques lead to reports of the determination of pesticide residues in produce, wine, mango, watermelon, tea, and many other foods.5−10 These quantitative methods require that the mass spectrometric procedures be targeted for a specific set of pesticides prior to analysis, so that the scan functions can be optimized for the selected reaction monitoring of each individual pesticide. One drawback of this optimization can be a limitation on the number of pesticides monitored at similar retention times due to the duty cycle of the instrument (the number of different scan functions that can be cycled through while still getting sufficient points across each chromatographic peak) Untargeted compounds will not be determined. More recently the use of high resolution mass spectrometry (HRMS) rather than tandem mass spectrometry has been explored. Various instrumental configurations have been used: an orbitrap for the analysis of 350 pesticides in honey;11 pesticide residues in fruits and vegetables with an orbitrap12,13 and a quadrupole orbitrap;14 organic contaminants in foods using a quadrupole time-of-flight MS;15 a report of pesticide residues in ginseng and spinach using several different HRMS
It has become evident that there are a multitude of potential contaminants in foods. The vast numbers of compounds include hundreds of different pesticides, mycotoxins, industrial compounds, and their byproducts. Insuring food safety therefore increasingly requires the use of methodologies that can monitor for hundreds of chemicals at the same time. These multiresidue methods combine a single extraction technique with analytical instrumentation capable of determining numerous compounds. Screening methods that can relatively quickly examine hundreds of compounds at relevant concentrations (i.e., below tolerance levels) have their place along with more rigorous quantitative methods. It has been noted that for screening to be effective for hundreds of compounds automated library based procedures will need to be implemented.1 Extraction protocols for multiresidue methods have been investigated by many researchers, with much of the recent efforts focused on modifications to the QuEChERS procedure as originally described by Anastassiades et al.2 Versions with various buffers have been compared by Lehotay et al.3 and a modified QuEChERS version was compared with aqueous acetonitrile extraction and pure acetonitrile extraction by Lacina.4 Both groups report that the various different versions of QuEChERS perform reasonably well for most pesticides with the differences between the various procedures most pronounced for a couple of pH dependent pesticides. © 2013 American Chemical Society
Received: August 16, 2013 Accepted: December 9, 2013 Published: December 9, 2013 80
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configurations.16 One focus has been on how much mass resolution is necessary. Recent efforts have postulated that complex matrices require resolution greater than 50000 (m/Δm),1 and in a comparison of HRMS to tandem MS, it was shown that above 50000 resolution, the selectivity of HRMS is greater than standard tandem MS.17 In a later report, Kaufmann et al. stated that while “sensitivity was slightly inferior ... HRMS has advantages that will most likely never be met by MS/MS”.18 Among the advantages is the nontargeted nature of full-scan data acquisition. Signals for virtually all ions within the selected mass range are acquired as opposed to only those targeted for acquisition as occurs with multiple reaction monitoring in MS/ MS. This makes it possible to analyze data retrospectively for analytes that were not considered at the time of acquisition. Potential drawbacks of the orbitrap based methods are some ion suppression issues that can occur in the c-cell of these instruments12,19 and the need for robust automated methods to handle the abundance of data produced.1,13 Many of these recent efforts utilizing both time-of-flight and orbitrap platforms have been reviewed with the conclusion that these systems are “attractive for residue analysis in food”.20 It is, however, important to show that these methods are robust and rugged with results that agree across different users. This robustness can be best shown by comparing results on the same samples from different laboratories. We report here on such an interlaboratory comparison using an orbitrap HRMS to screen for pesticide residues in three diverse food matrices: spinach, a leafy green; oranges, watery fruit; and carrots, a root crop. The goal of the project was not the development of a full quantitative method but rather an examination of the utility of HRMS along with an automated data processing program to relatively quickly screen produce for the presence of pesticide residues. Our intent was not to investigate the method but to evaluate current procedure. We therefore chose to only utilize the 100000 resolution achievable by the orbitrap. As mentioned previously there are many versions of the QuEChERs extraction protocol, all of which do reasonably well for most compounds. We therefore chose to mimic a recently published validation of a QuEChERS LC-tandem mass spectrometric procedure10 using the same matrices and extraction protocols. These choices would allow us to examine the utility of the HRMS screening procedure without going through optimization of the extraction or analysis protocols. To further examine the utility of the procedure, we chose to use a commercially available set of 247 pesticides, none of which were preselected to be amenable to the chosen QuEChERS procedure and LCMS. Although this set of pesticides was not the same as the procedure that we were mimicking there was a sufficient overlap that we could compare our HRMS results with their tandem MS results. All samples were prepared at a single laboratory, frozen, and shipped to the other two laboratories. At each laboratory, the samples were spiked with the set of standards, extracted using the modified version QuEChERs, and analyzed by LC/HRMS on an orbitrap mass spectrometer.
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Materials. Prefilled QuEChERs extraction tubes containing 4 g of MgSO4 and 1 g of NaCl (part no. ECMSS50CT, UCT, Bristol, PA) and prefilled QuEChERs extraction tubes containing 50 mg of PSA and 150 mg of MgSO4 (part no. CUMPS2CT, UCT). Standards. A mixed pesticide standard set was obtained from Accustandard (part no. S-21974-set, New Haven, CT). This set consists of 13 1 mL ampules each containing 16−22 pesticides at 100 μg/mL, for a total of 247 compounds. The stock standard was prepared by combining 400 μL from each of the ampules in a single 10 mL volumetric flask, and the final volume was brought to 10 mL with acetonitrile for a final concentration of 4 μg/mL. A diluted stock solution was prepared by bringing 2.5 mL of the stock solution to 10 mL with acetonitrile for a final concentration of 1 μg/mL. A 5 ppm solution of triphenylphosphate and d-6 malathion in acetonitrile was prepared as an internal standard. Sample Preparation. Three different matrices were prepared. One pound or more of carrots, oranges, and spinach were chopped in a Hobart food chopper without peeling. The mixture was then thoroughly blended in a Robot Coupe, model R2 equipped with a 3 quart stainless steel bowl. The sample homogenate was frozen in 18 ounce Nasco whirl-pak bags with each bag containing about 250−300 g each. Bags of each matrix were shipped overnight to the other two laboratories for all subsequent work. At each laboratory, 12 10 g samples of each homogenized commodity were placed into 50 mL centrifuge tubes. Three tubes were left as unspiked matrix samples, three were spiked with 100 μL of the diluted stock standard (10 ppb in produce), three with 500 μL of the diluted stock standard (50 ppb in produce), three with 500 μL of the stock standard (200 ppb in produce). All were spiked with 100 μL of the internal standard solution. A reagent blank consisting of 10 mL water was also prepared. Acetonitrile was added to each of the tubes so that the total volume of acetonitrile added (spikes plus acetonitrile) was 10 mL. A ceramic stone was added to each tube, and the sample was vigorously shaken for 1 min. A UCT salt packet containing 4 g of MgSO4 and 1 g of NaCl was then added, and again the samples were shaken for 1 min. The sample was centrifuged for 5 min at ≥3000 rpm. A 1 mL aliquot was transferred to a tube containing 50 mg of PSA and 150 mg of MgSO4. The sample was shaken for 1 min. The sample was centrifuged for 5 min at ≥3000 rpm. A 0.5 mL aliquot was diluted with 0.5 mL of the aqueous mobile phase (water with 0.1% formic acid), and the sample was filtered into a microvial for LC-HRMS analysis. Instrumental Analysis. Samples were analyzed using an Agilent (Santa Clara, CA) 1200 series rapid resolution liquid chromatograph interfaced to a Thermo Fisher Scientific (West Palm Beach, FL) Exactive orbitrap mass spectrometer. The LC was operated using a Thermo Fisher Scientific Hypersil Gold aQ C-18, 100 mm × 2.1 mm, 1.9 μm column held at 40 °C. The mobile phase flow rate was 0.25 mL/min, going from 99% water with 0.1% formic acid to 95% acetonitrile with 0.1% formic acid from 1 to 10 min followed by a 5 min hold at 95%, with a 5 min re-equilibration step for a total run time of 21.5 min. Three microliters of extract was injected. HRMS data was acquired in the positive electrospray mode using a metal needle at 3.5 kV, a source capillary temperature of 300 °C and mass resolution of 100000 (as specified in the tune page), the balanced mass accuracy setting, and an injection time of 20 ms. Data was acquired over the mass range of m/z = 75−1500. Prior to analysis, the mass spectrometer was tuned by infusion of azoxystrobin into the system. The tube lens, skimmer, and other parameters were adjusted so that the relative abundance of m/z 372 compared with m/z 404 was less than 10%. This tuning step was performed in order to ensure that any fragmentation of analytes through the tube lens and skimmer was minimized. A single tune file from each laboratory was used for the entire set of analyses though a mass calibration was performed prior to the analysis of each sample matrix. It should be noted that the final extract concentration was 0.5 g sample/mL extract. At the 10 ppb concentration using a 3 μL injection, this would result in a total of 15 pg on column. While these low levels may help reduce ion suppression, they also can be a challenge to detect.
MATERIALS AND METHODS
Reagents. The three laboratories in this study may have had different suppliers for some of the reagents so these are listed by specification. Water was supplied as 18.2 MΩ water from a Millipore Milli-Q source or purchased as HPLC grade. Acetonitrile was HPLC grade. When used for the mobile phases, the water and acetonitrile were either fortified at 0.1% with ACS reagent grade formic acid or purchased as prefortified HPLC grade reagents. 81
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Figure 1. Mass profile for an infusion of thiobendazole and carbaryl on the orbitrap mass spectrometer at a resolving power of 100000. The theoretical m/z of the (M + H)+ ions are 202.04334 and 202.08626, respectively. Data Analysis. The raw pesticide data from the instrument was analyzed using the ToxID software package (Thermo Scientific). This program required an input file listing the molecular formula for each pesticide of interest, along with its expected retention time. During the setup of the method, the stock solution of 247 standards was injected on the instrument to determine retention times. Five compounds had distinctively split peaks and were entered into the input file twice (with separate retention times), while six compounds were not detected in the positive electrospray mode, thus in total there were 246 entries in the input file used for these tests. During operation of the software, the raw exact mass data for each entry was compared with the theoretically expected data (as computed from the entries molecular formula) for each of the individual pesticides in the input file. User parameters were set for detection at an elution time of ±0.25 min with a mass accuracy of ±5 ppm. The response threshold was set by each laboratory and had a slight variability depending on instrument but was in the range of 1000- 5000 counts. The ToxID data output for samples, spiked samples, and method blanks were reviewed to determine if analytes were found correctly and identified in amounts significantly greater (3x) than that observed in the method blanks. Safety. Pesticides are considered hazardous materials. In accordance, all laboratory procedures were conducted with good chemical handling practices, and waste was disposed of as per the laboratory’s hazardous waste plan.
and thiabendazole (C10H7N3S) have the same nominal mass of 201, the theoretical exact m/z’s of their (M + H)+ ion are 202.08626 and 202.04334, respectively. While these ions would be indistinguishable on unit resolution mass spectrometers, they are easily differentiated under high resolution conditions with a required resolution (m/Δm) of only 4700. This is clearly demonstrated in Figure 1, which shows the infusion (no chromatography) of these two compounds into the orbitrap system used in the current work. The protonated molecular ions of the two compounds are quite distinct. The work herein described was performed at a mass resolution of 100000. Isomeric compounds, having identical formulas, cannot be distinguished by differences in the exact mass of the molecular ion. However, these compounds can often still be distinguished by differences in relative retention time through the chromatographic system. Alternatively they can be differentiated by collisional fragments generated in the HCD cell of the instrument. These experiments could be conducted on the described instrumentation, but that is the subject of future work. Interlaboratory Comparison. Data was reviewed for each individual pesticide at each level in each matrix. The number of detections (out of three samples) for each combination (matrix−pesticide−level) was recorded. All data from the three laboratories is included in the Supporting Information. The number of detections for the same combination was then compared across the three laboratories and is shown in Table 1. As can be seen in the table, the three different laboratories were very consistent in their ability to detect the pesticides. At the lowest level over 70% of the time either all three laboratories found a pesticide the same number of times in a particular matrix or did not see it at all in that matrix. By the highest concentration level the consistency in the data was almost at 90%. Clearly the entire method showed good consistency across the three laboratories.
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RESULTS AND DISCUSSION Specificity. Individual pesticide residues were identified in samples through two fundamental characteristics of the pesticide. The first was that the retention time for the pesticide must have been within 0.25 min of the time observed for an authentic standard of that pesticide. The second and more important characteristic was that the exact m/z of the pesticide must have been within 5 ppm of the calculated theoretical value. As an example of the specificity that is provided by an exact mass instrument consider the case of the two pesticides, carbaryl and thiabendazole. Although carbaryl (C12H11NO2) 82
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Reagent Blanks. With each set of samples, a reagent blank (using clean water and all other reagents including the internal standards) was analyzed. These samples were processed using the same ToxID protocol as used for all samples. A total of 43 of the compounds on the entry list were reported as a positive
hit in at least one reagent blank sample by the ToxID software. Upon further inspection almost all of these “detections” appeared to be the result of software integrating noise. This points out the need for user accessible data integration parameters (other than intensity threshold) in the ToxID software. In addition, this also indicates that there needs to be a manual review of data. Unspiked Matrix. The analyzed samples were purchased at the supermarket with no a prioi knowledge of pesticide use. Therefore, the unspiked matrix samples were examined for the presence of any of the pesticides in the spiked mixture. Each laboratory extracted a set of three samples of each matrix without any spikes other than the internal standards. Three incurred residues were identified in the oranges (imazalil, pyriproxifen, and thiadendazole); two residues in the spinach (imidacloprid and spinosad), and none in the carrots. For these residues, in all cases there were good distinct peaks that appeared to be real in all nine samples analyzed of the unspiked matrix. There were a number of additional compounds tentatively identified by ToxID in the unspiked matrix that on closer inspection did not look like real peaks. These pesticides would be considered as false positves and are summarized in Table 2. There are two ways to consider the false positive rate: either (number of pesticides seen at least
Table 1. Comparison of the Percentage of Times That the Different Laboratories Agree on the Detection (Or Nondetection) At the Specified Concentration Level concn level
% 0 of 3 agree
% 2 of 3 agree
% 3 of 3 agree
10 50 200
5.65 2.0 1.6
22.6 14.6 9.5
71.8 83.3 88.8
Table 2. The Number of Pesticides with the Specified Number of False Positive Hits in Each Matrixa matrix
1 hit
2 hits
3 hits
4 hits
5 hits
6 hits
7 hits
8 hits
9 hits
oranges carrots spinach
13 5 11
6 2 6
11 10 13
3 3 3
1 0 1
4 0 2
0 0 0
1 0 0
1 1 1
a
Each unspiked matrix was analyzed three times by each laboratory so the maximum number of hits is nine.
Figure 2. Selected ion chromatogram (5 ppm window) around (M + H)+ ion for aldicarb sulfoxide spiked into oranges at 10 ppb (top) and 50 ppb (bottom). ToxID identifies a peak in both samples; however, the compound was not considered to be identified in the upper sample upon review. 83
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once)/(the total number of pesticides, 247) or (the total number of hits)/(total possible hits, 2223). Thus, the false positive rates for the three matrixes are as follow: oranges 16% and 5.2%, carrots 9% and 2.7%, spinach 15% and 4.5%. Although these rates are low, they again point to the need for additional data review beyond the library program. False Negatives. A false negative occurs if a residue that should be reported was missed. To give a true assessment of the false negative rates, the data needed to be adjusted for positive detections based on the integration of noise. To do this, any pesticide that had a ToxID reported detection in either an unspiked matrix or reagent blank was carefully examined in the lowest spiked samples. In a few cases, the ToxID program integrated a noise peak at the 10 ppb level and then identified the compound as having been found. An example of this noise integration is shown in Figure 2 (top); however, in all of these cases, when the compound was examined at the 50 ppb level, the peak was present at sufficient signal-to-noise ratio to be correctly identified, Figure 2 (bottom). In these cases, the detections for the 10 ppb sample were manually changed to nondetects prior to the analysis of false negatives. The false negative rate for each pesticide at each concentration level is defined by eq 1, where F is the false negative rate, N is the number of missed detections at a given concentration, and D is the number of possible detections at a given concentration. For most pesticides, D is 27, but for those pesticides for which there was an incurred residue, the data was adjusted to only use the matrixes without that incurred residue (D = 18 in those cases). F = 100 × N /D
Table 4. Comparison of the Minimum Concentration with Zero False Negatives with the Tandem MS MDL as Listed by Sack et al.10 for the Same Compounda
Table 3. Number of Pesticides with the Specified False Negative Rate Using the ToxID Software at Each Concentration 0%
0−25%
25−50%
50−75%
75−100%
129 189 211
34 18 9
30 16 13
16 14 11
37 9 2
acephate
50
4
acetamiprid
50
5
acibenzolar S-methyl
50
8
b
8
aldicarb
50
6
aldicarb sulfoxide
50
4
aminocarb
10
4
azoxystrobin
10
4
benalaxyl
10
5
bendiocarb
50
4
benfuracarb
50
3
benzoximate
10
6
bifenazate
200
5
bitertanol
10
6
boscalid
50
6
bromuconazole-cis
50
7
bromuconazole-trans
50
7
bupirimate
10
7
buprofezin
10
5
butafenacil
50
5
butocarboxim
50
9
b
6
carbaryl
50
5
carbendazim
10
5
carbetamide
10
4
carbofuran
10
4
carbofuran-3OH
50
5
200
5
chlorfluazuron
50
6
chloroxuron
10
7
clethodim
50
5
clofentezine
200
5
clothianidin
200
7
cyazofamid
50
8
cycluron
10
5
cyflufenamid
10
5
butoxycarboxim
The false negative rate for each pesticide at each concentration is included in the Supporting Information. The data is summarized in Table 3 showing the number of pesticides
10 50 200
tandem MS MDL (ppb)
alanycarb
(1)
concn
minimum concentration with zero false negatives
compound
chlorantraniliprole
within a specified false negative percentage range. The method was tested with a mixed pesticide standard containing 247 different pesticides along with two internal standards. Seven compounds did not respond on the LC/MS system while five others gave split peaks creating a total of 246 distinct ToxID entries to examine for false negatives. At the 10 ppb spiking level, just over half (129) of the pesticides were seen in all spiked samples, with this number increasing to about 75% (189) at 50 ppb, and 85% (211) at 200 ppb. This table also shows that there were only a couple of truly problematic compounds that had false negative rates of over 50% at the higher concentration level. Several of these compounds also had poor (though) detectable response in the standards so likely lacked sensitivity to be detected at lower concentrations in matrix. It is also possible that a couple of these pesticides may have had problems with the chosen extraction procedure, though this was not evaluated. It should be noted that this version of QuEChERS has an overall dilution of the sample by a factor of 2. Some of the other versions of QuEChERs result in
b
5
cyproconazole
10
7
cyprodinil
10
6
cyromazine
b
4
desmedipham
50
5
diclobutrazol
10
7
dicrotophos
10
4
diethofencarb
10
5
difenoconazole
10
4
diflubenzuron
b
5
dimethoate
10
4
dimethomorph
50
6
dimoxystrobin
10
5
dinotefuran
50
5
b
6
cymoxanil
dioxacarb diuron doramectin 84
50
6
200
13
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Table 4. continued
Table 4. continued minimum concentration with zero false negatives
tandem MS MDL (ppb)
emamectin benzoate B1a
10
6
monolinuron
emamectin benzoate B1b
200
6
moxidectin
ethiofencarb
10
4
ethiprole
50
ethirimol
10
compound
compound
minimum concentration with zero false negatives
tandem MS MDL (ppb)
10
4
200
8
myclobutanil
10
7
7
neburon
10
5
3
nitenpyram
50
5
etoxazole
10
4
novaluron
b
7
fenamidone
10
6
nuarimol
10
7
fenazaquin
10
5
omethoate (dimethoate oxon)
10
4
fenbuconazole
10
6
oxadixyl
50
6
fenhexamid
50
6
oxamyl
50
4
fenobucarb
10
4
paclobutrazol
10
8
fenoxycarb
10
4
phenmedipham
50
5
fenpyroximate
10
4
picoxystrobin
10
6
fenuron
10
5
piperonyl butoxide
10
5
flonicamid
50
6
pirimicarb
10
5
flubendiamide
50
9
prochloraz
10
4
flufenoxuron
200
5
promecarb
10
5
fluometuron
10
5
propamocarb
10
4
fluoxastrobin
10
5
propargite
b
4
flusilazole
10
5
propiconazole
10
6
flutolanil
10
6
propoxur
10
3
flutriafol
10
6
pyracarbolid
10
4
forchlorfenuron
10
6
pyraclostrobin
10
4
formetanate
10
5
pyridaben
10
5
fuberidazole
10
4
pyrimethanil
10
6
furathiocarb
10
5
pyriproxyfen
10
5
halofenozide
50
5
rotenone
10
6
hexaflumuron
b
9
siduron
10
9
10
4
hexythiazox
50
4
spinetoram
hydramethylnon
50
5
spirodiclofen
50
17
5
spiromefisen
50
7
6
spirotetramat
50
6
200
7
imazalil imidacloprid
10 50
indoxacarb
50
8
spiroxamine
ipconazole
10
7
sulfentrazone
b
10
4
tebuconazole
10
7
3
tebufenozide
10
6
tebuthiuron
10
4
b
9
iprovalicarb isoprocarb
10 10
isoproturon
10
5
isoxaflutole
b
30
teflubenzuron
ivermectin
temephos
200
10
b
6
kresoxim-methyl
50
6
thiabendazole
10
5
linuron
50
6
thiacloprid
50
5
malathion
50
7
thiamethoxam
50
6
mandipropamid
10
6
thidiazuron
10
7
mepanipyrim
10
6
thiophanate-methyl
metaflumizone
b
7
metalaxyl
10
metconazole
10
200
5
triadimefon
10
6
5
triadimenol
10
9
6
tricyclazole
10
3
200
4
trifloxystrobin
10
4
methiocarb
50
5
triflumizole
10
5
methomyl
50
5
triflumuron
200
6
methoxyfenozide
10
6
triticonazole
10
7
metobromuron
50
4
vamidothion
10
5
mevinphos
50
6
zoxamide
50
7
mexacarbate
10
6
monocrotophos
10
4
methamidophos
a
Average of two transitions used. bNever reached, there was always at least one false negative.
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Table 5. List of Compounds Tested for Which a Tolerance Has Been Established for One of the Matrixes Studied along with the Minimum Concentration with Zero False Negativesa compound acetamiprid acibenzolar S-methyl aldicarb aldicarb sulfone aldicarb sulfoxide azoxystrobin boscalid buprofezin carbaryl carfentrazone-ethyl chlorantraniliprole clethodim clothianidin cyazofamid cyprodinil desmedipham diazinon difenoconazole dimethoate dinotefuran diuron emamectin benzoate B1a emamectin benzoate B1b EPTC (eptam) ethofumesate fenamidone fenazaquin fenbuconazole fenhexamid fenpyroximate flonicamid flubendiamide flufenoxuron a b
minimum zero false negative concn (ppb)
carrots tolerance (ppb)
50 50 50 50 50 10 50 10 50 b 200 50 200 50 10 50 10 10 10 50 50 10
500 1000 2000
1000 800 90 750 750
oranges tolerance (ppb)
3000 1000
500
1000 35 000 22 000 100 13 000 2000 3000 9000 30 000 6000 700
300 300 300 10 000 1600 2500 10 000 100 1400
8000
600 2000 5000 50 100
200 10 b 10 10 10 50 10 50 50 200
spinach tolerance (ppb)
100 100 7000 150 500 1000 30 000 600 600
9000 11 000 300
compound
minimum zero false negative concn (ppb)
hexythiazox imazalil2 imidacloprid3 indoxacarb linuron malathion mandipropamid metalaxyl methidation methomyl methoxyfenozide metribuzin mevinphos myclobutanil norflurazon oxamyl phenmedipham piperonyl butoxide prometryn propiconazole pymetrozine pyraclostrobin pyridaben pyrimethanil pyriproxyfen spinetoram spinosad5 spirodiclofen spiromefisen spirotetramat tebufenozide thiabendazole thiamethoxam trifloxystrobin triflumizole
50 10 50 50 50 50 10 10 b 50 10 10 50 10 50 50 50 10 10 10 200 10 10 10 10 10 10 50 50 50 10 10 50 10 10
carrots tolerance (ppb)
400 1000 8000 500 200 100 300 30
spinach tolerance (ppb)
3500 14 000 8000 20 000 10 000 20030 000
oranges tolerance (ppb) 350 10 000 700
8000 10 000 4000 2000 10 000
1000 30 200 3000
100 4000
8000 450 250 400
5000 600 29 000
150 100 100
3000 8000 8000 12 000 9000 10 000
50 100
4000
2000 500 10 000 300 300 300 500 600 800 10 000 400 600
3500
21
Tolerance values from Pesticide Chemical News Guide, Arlington VA; also available from Code of Federal Regulations Title 40, 24, Part 180.22 Never reached, there was always at least one false negative.
over 50% of these 161 pesticides had a zero false positives at a concentration on the same order of magnitude as the reported MDL (i.e., minimum concentration of 10 vs an MDL of 5). There were clearly some pesticides that worked much better on the tandem MS system; further investigation is needed to see what is causing this to occur. Furthermore, the current methodology can easily be expanded to include many other pesticides (and other toxic organic contaminants). All that is necessary is the knowledge of the chemical formula of the compound; it can then be easily added to the screen (provided it extracts and is detectable by the LC/HRMS system). In fact, because the instrumental conditions do not change, items can be added to the list retroactively. This is one item that the triple quadrupole system could not do because it needs to have screened for a specific (and preset) transition. It is also useful to examine the validated level in terms of what detection level is actually needed. This can be done by comparing the validated level to the tolerance for a particular pesticide in a given matrix as determined by the EPA and listed in the Code of Federal Regulations.21 Table 5 compares the
concentration of the extract by a factor of 2−5. It is possible that using one of these other versions would further reduce the false negative rate and improve the overall sensitivity of the method at the possible cost of increasing false positives. It is useful to compare the overall performance of this HRMS method with that of the triple quadrupole MS/MS reported by Sack et al. for these same three matrixes using the same extraction scheme but with multiple reaction monitoring tandem mass spectrometry as the detection method.10 This comparison is shown in Table 4. It should be noted that the Sack et al. data were reported using operating conditions that were optimized for each individual pesticide, whereas the current data was collected using a single scan function on the mass spectrometer. Furthermore, the Sack data are actual reported MDLs, while the current data only report on the minimum concentration in this screening project that had no false negatives; for many compounds, the detection limit is likely lower because there were many pesticides with strong signals at the lowest tested level. Nevertheless, the current data show a robust response with the minimum detection limits; 86
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minimum validated levels with the tolerances for those compounds for which a tolerance has been established. Examination of this table shows that in almost all cases the minimum validated level is below (and in most cases, well below) the United States tolerance and for many compounds meets the 10 ppb default detection limit of Europe. Thus, use of the reported screening method would succeed in observing the pesticides at the levels where they would need to be found. This being a screening method, follow-up would be required to determine whether they are below tolerance when found. Overall, there clearly is utility in the HRMS screening procedures, though improvements still need to be made in the software for dealing with these large multiresidue screens.
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ASSOCIATED CONTENT
S Supporting Information *
All data from the three laboratories and the false negative rate for each pesticide at each concentration. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Funding
This work was supported by the Food and Drug Administration through the Food Emergency Response Networks Chemistry Cooperative Agreement Program. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank the personnel of the FDA’s Forensic Chemistry Center for suggestions during the study. REFERENCES
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