Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry

Saito-Shida, Shizuka; Nemoto, Satoru; Akiyama, Hiroshi

doi:10.3390/foods10010078

Open AccessArticle

Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry

by

Shizuka Saito-Shida

^*

,

Satoru Nemoto

and

Hiroshi Akiyama

Division of Foods, National Institute of Health Sciences, Tonomachi 3-25-26, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan

^*

Author to whom correspondence should be addressed.

Foods 2021, 10(1), 78; https://doi.org/10.3390/foods10010078

Submission received: 9 December 2020 / Revised: 24 December 2020 / Accepted: 30 December 2020 / Published: 3 January 2021

(This article belongs to the Special Issue Instrument Analysis Applied in Food Science)

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Abstract

For controlling pesticide residues in food and ensuring food safety, multiresidue methods that can monitor a wide range of pesticides in various types of foods are required for regulatory monitoring. In this study, to demonstrate the applicability of liquid chromatography–quadrupole time-of-flight mass spectrometry (LC–QTOF-MS) for quantitative and confirmatory analysis of pesticide residues in cereal grains and legumes, the LC–QTOF-MS method using full-scan acquisition was validated for 151 pesticides in brown rice, soybeans, and peanuts at a spiked level of 0.01 mg/kg. With the exception of 5 out of 151 target pesticides, sufficiently high signal intensities were obtained at 0.005 μg/mL (corresponding to 0.01 mg/kg). Trueness was in the range 70–95%, with intra- and inter-day precisions below 16% and 24%, respectively, with the exception of 7 pesticides in brown rice, 10 pesticides in soybeans, and 9 pesticides in peanuts. No interfering peaks were observed near the retention times of the target pesticides. Furthermore, information on accurate fragment-ion masses obtained by a data-independent acquisition enabled unambiguous confirmation. The results suggest that the LC-QTOF-MS method is suitable for pesticide residues’ analysis of cereal grains and legumes, and can be utilized for regulatory routine analysis.

Keywords:

pesticides; liquid chromatography–quadrupole-time-of-flight mass spectrometry; multiresidue method; cereal grains; legumes

1. Introduction

Pesticides are used worldwide to increase crop yields by protecting crops from pests, including insects, rodents, fungi, and weeds; however, the intake of pesticide residues contained in foods may adversely affect human health [1]. To ensure food safety and protect consumer health, international organizations such as the Codex Alimentarius Commission, established by the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO), and the European Union (EU), as well as many individual countries, including Japan, have established maximum residue limits (MRLs) to regulate pesticide residue levels in foods. In Japan, MRLs are currently established for various foods with respect to more than 750 agricultural compounds, i.e., pesticides, veterinary drugs, and feed additives. Therefore, the need for multiresidue methods, which detect a wide range of pesticides in various types of foods, is increasing in laboratories concerned with the regulatory monitoring of pesticide residues.

Nowadays, liquid chromatography (LC) and gas chromatography (GC) coupled with triple quadrupole mass spectrometry (MS/MS) operated in selected reaction monitoring (SRM) mode are the most widely used techniques for analyzing pesticide residues in foods. They are highly sensitive and selective, which enables the robust quantification of trace amounts of pesticide residues in complex matrices. In recent years, LC and GC coupled with high-resolution mass spectrometry (HR-MS) methods, such as time-of-flight (TOF)-MS, quadrupole-TOF-MS (QTOF-MS), Orbitrap-MS, and quadrupole-Orbitrap-MS (QOrbitrap-MS) have also been employed for the screening and quantification of pesticide residues [2,3,4,5,6,7,8,9,10,11,12,13,14]. LC and GC coupled with HR-MS operating in full-scan mode with high mass accuracy have several advantages over LC–MS/MS and GC–MS/MS operating in SRM mode: (1) There are no limits on the number of target compounds that can be analyzed simultaneously [7,8]. (2) The optimization of MS parameters, for example, SRM transitions, cone voltage, or collision energy, for individual analytes is not needed [8,14]. (3) The adjustment of retention time windows for the target analytes is not required even if the mobile phase or analytical column is changed. (4) The methods allow retrospective analysis for nontarget or unknown compounds by reprocessing previously acquired data without re-injection of the samples [15,16,17]. Furthermore, hybrid HR-MS, such as QTOF-MS and QOrbitrap-MS, offer fragment-ion information, which could be used for confirmation purposes [2,11,18]. Accordingly, numerous methods based on LC or GC coupled with HR-MS have been published recently for analyzing pesticide residues in vegetables and fruits [3,4,5,8,9,10,11,13,14]. In our previous work, we reported the quantitative analyses of pesticide residues in tea [19] using LC–QTOF-MS and LC–Orbitrap-MS. However, to the best of our knowledge, few papers have reported the application and validation of LC coupled with HR-MS for the quantitative analysis of pesticide residues in cereal grains and legumes, such as rice, soybeans, and peanuts. Cereal grains and legumes comprise complex matrices, containing high amounts of lipids and/or starch, which can potentially interfere with the analyses and cause matrix effects. Therefore, they are considered to be difficult matrices for the analysis of trace amounts of pesticide residues [20].

The aim of the current study is to evaluate the applicability of LC–QTOF-MS for the quantitative analyses of pesticide residues in cereal grains and legumes containing high amounts of lipids and/or starch. Brown rice, soybeans, and peanuts are selected as representative foods, and the LC–QTOF-MS method is validated for 151 pesticides at a concentration of 0.01 mg/kg. In addition, data-independent acquisition (DIA) is carried out to obtain information regarding the fragment ions, and to demonstrate the capability of LC–QTOF-MS for confirmative analyses.

2. Materials and Methods

2.1. Reagents and Chemicals

Pesticide analytical grade toluene and acetonitrile, LC-MS grade water, and methanol were obtained from Kanto Chemical (Tokyo, Japan). Diatomaceous earth (Celite^® 545), analytical grade ammonium acetate, dipotassium hydrogen phosphate, potassium dihydrogen phosphate, and pesticide analytical grade sodium chloride were purchased from FUJIFILM Wako Pure Chemical (Osaka, Japan).

Pesticide standards, except for aramite and etrimfos, were procured from Hayashi Pure Chemical (Osaka, Japan), Kanto Chemical, FUJIFILM Wako Pure Chemical, Dr. Ehrenstorfer (Augsburg, Germany), Riedel-de Haën (Seelze, Germany), and Sigma-Aldrich (St. Louis, MO, USA). Stock standard solutions of each pesticide were prepared in acetonitrile or methanol, depending on their solubility, at a concentration of 1 mg/mL. Standard solutions (100 μg/mL in methanol) of aramite and etrimfos were obtained from AccuStandard (New Haven, CT, USA). A mixed standard solution (1 μg/mL) was prepared by mixing the stock standard solutions and diluting with acetonitrile.

Leucine–enkephalin, used as a reference compound in LC–QTOF-MS analyses, was obtained from Waters (Milford, MA, USA). A 1-μg/mL leucine–enkephalin standard solution was prepared in methanol/water (1:1, v/v).

2.2. Materials

Brown rice and soybeans were purchased from a local market in Tokyo (Japan), and peanuts cultivated in Chiba (Japan) were obtained via the Internet. Brown rice and soybeans were ground using a centrifugal mill (Ultra Centrifugal Mill ZM 200; Retsch, Haan, Germany). Peanuts were milled using a laboratory mill (SCM-40A, Shibata, Japan).

Tandem graphitized carbon black (GCB)/primary secondary amine (PSA) cartridges (InertSep GC/PSA, 500 mg/500 mg) were bought from GL Sciences (Tokyo, Japan) and octadecylsilyl silica gel (ODS) cartridges (Mega Bond Elut C18, 1000 mg) were purchased from Agilent Technologies (Palo Alto, CA, USA).

2.3. Apparatus

LC–QTOF-MS analyses were performed using an Acquity UPLC I-class system (Waters) coupled to a Xevo G2-S QTOF mass spectrometer (Waters). The chromatographic separation was carried out using an Inertsil ODS-4 column (100 × 2.1 mm, 2 μm; GL Sciences). The mobile phases consisted of 5 mmol/L ammonium acetate in water (A) and 5 mmol/L ammonium acetate in methanol (B). The mobile phase was pumped at a flow rate of 0.3 mL/min with the following gradient profile: 5% B followed by increasing B to 95% at 10 min and holding it at this concentration for 3 min, increasing to 100% at 13.01 min and holding for 5 min, and finally, returning to 5% at 18.01 min. The column temperature was set to 40 °C. The injection volume was 3 μL. The retention times of the target pesticides are presented in Table 1.

The QTOF mass spectrometer was operated in resolution mode, providing a resolving power of >30,000 at full width at half maximum (FWHM), at m/z 556.2766. The following MS conditions were used: ionization mode, electrospray ionization in positive mode (ESI(+)); scan range, m/z 50–1000; source temperature, 120 °C; desolvation gas temperature, 450 °C; capillary voltage, 1000 V; cone voltage, 20 V; collision energy, low energy (4 eV) and high energy (ramp from 10 to 40 eV); desolvation gas (nitrogen), 800 L/h; cone gas (nitrogen), 50 L/h; collision gas, argon. Leucine–enkephalin (m/z 556.2766) was used as a reference compound, being introduced from a lock spray probe during analyses. The mass window of ±5 mDa was used for the extraction of chromatograms for each target pesticide. The calculated exact mass and retention time for each pesticide are summarized in Table 1.

2.4. Sample Preparation

Samples were prepared according to the official Japanese multiresidue method, namely, “Multi-residue Method I for Agricultural Chemicals by LC-MS (Agricultural Products),” except for the use of a tandem GCB/PSA cartridge instead of a GCB/aminopropylsilyl silica gel (NH₂) cartridge for cleanup.

A 10.0 g sample was weighed in a glass tube and water (20 mL) was added; subsequently, it was left to stand for 30 min. Acetonitrile (50 mL) was added to the mixture; then it was homogenized using a homogenizer (Polytron PT 10–35 GT; Kinematica, Lucerne, Switzerland) for 1 min. The homogenate was filtered with suction, and then the residue was rehomogenized with acetonitrile (20 mL) before being filtered with suction. The filtrates were combined, and the resulting volume was adjusted to 100 mL by the addition of acetonitrile.

A 20 mL aliquot of the extract was added to a 50 mL polypropylene (PP) centrifuge tube containing sodium chloride (10 g) and phosphate buffer (pH 7.0, 0.5 mol/L). The mixture was shaken for 5 min by a shaker (SR-2w; Taitec, Saitama, Japan) and centrifuged for 5 min at 3000 rpm (Centrifuge 8100, Kubota, Japan). The resultant acetonitrile layer was loaded onto an ODS cartridge, which was preconditioned with acetonitrile (10 mL), and then eluted with acetonitrile (5 mL). The resultant eluates were combined and concentrated to approximately 0.5 mL by a rotary evaporator (NVC-2100/N-1000, Eyela, Tokyo, Japan) at <40 °C; it was then dried by evaporation under a nitrogen stream. The residue was redissolved in acetonitrile/toluene (3:1, v/v, 2 mL) and loaded onto a GCB/PSA cartridge, which was preconditioned with acetonitrile/toluene (3:1, v/v, 10 mL) and then eluted with acetonitrile/toluene (3:1, v/v, 20 mL). The eluate was concentrated to approximately 0.5 mL by a rotary evaporator at <40 °C and evaporated to dryness under a nitrogen stream; finally, the resultant residue was redissolved in methanol (4 mL) prior to LC–QTOF-MS analysis.

2.5. Method Validation

The LC–QTOF-MS method was validated using a nested experimental design for brown rice, soybeans, and peanuts. Samples were spiked in duplicate at a level of 0.01 mg/kg, and the recovery experiments were repeated on five different days. To prepare the spiked samples, a 1 mL aliquot of the 0.1 μg/mL mixed standard solution was added to 10.0 g of sample, and the mixture was allowed to stand for 30 min before proceeding with the subsequent sample preparation steps. The quantification was carried out using six-point calibration curves with solvent-based standard solutions prepared in methanol. The concentrations of the standard solutions used to construct the calibration curves, to allow quantification, were 0.00125, 0.0025, 0.00375, 0.005, 0.00625, and 0.0075 μg/mL. The linearity of each calibration curve over a wider range was examined in the range of 0.002–0.1 μg/mL.

Matrix-matched standards were prepared by evaporating a 100 μL aliquot of blank solution under a nitrogen stream and then redissolving it in 100 μL of the mixed standard solution in methanol. Matrix effects were evaluated by comparing peak areas of the matrix-matched standards with standards in solvents as follows: average peak area (n = 5) of matrix-matched standard/average peak area (n = 5) of standard in solvent.

3. Results and Discussion

3.1. Optimization of LC–QTOF-MS Conditions

A total of 151 LC-amenable pesticides, which had molecular weights from 189 to 746, were selected as target pesticides for this study. Because most of the target pesticides produced high-intensity signals under positive-mode operation of the instrument (c.f., negative-mode operation), and since the instrument used in this study was unable to simultaneously operate in both the positive and negative modes, LC–QTOF-MS analyses were carried out only in the positive mode, using the MS parameters optimized in a previous study [14]. The calculated exact mass of each pesticide is presented in Table 1. Quantification was performed by operating in full-scan acquisition mode using ions with the highest intensity among [M+H]⁺, [M+Na]⁺, and [M+NH₄]⁺. For most of the target pesticides, the highest intensity was obtained for [M+H]⁺; only 10 compounds were observed to have their highest intensity for [M+NH₄]⁺ and none of the compounds were seen at their highest intensity for [M+Na]⁺. The mass window for extracting the chromatograms of each pesticide was optimized by comparing the repeatability of the peak areas of the target compounds, obtained by replicate analyses (n = 5, 0.01 μg/mL) for the extraction of mass windows of ±2.5, ±5, and ±10 mDa. It should be noted that, in general, a narrow mass window for the extraction of chromatograms will result in low background noise and allow the discrimination of coeluting matrix components. This will increase sensitivity and selectivity; however, the use of a disproportionately narrow window will result in peak shape deterioration and low repeatability. Hence, mass windows of ±5 and ±10 mDa resulted in relative standard deviations (RSDs) of <5% for all the target pesticides; whereas the RSD values for 10 pesticides were >5% with a mass window of ±2.5 mDa. In addition, narrow mass windows produced higher signal-to-noise (S/N) ratios. Therefore, considering these results, the mass window was set to ±5 mDa, as a trade-off between S/N and peak area repeatability.

3.2. Method Validation

As mentioned earlier, the samples were prepared according to the official Japanese multiresidue method “Multi-residue Method I for Agricultural Chemicals by LC-MS (Agricultural Products)” prior to analysis by LC–QTOF-MS, except for the modification in the cleanup step. A tandem GCB/PSA cartridge was used instead of a tandem GCB/NH₂ cartridge for cleanup because the PSA sorbent can more effectively remove acidic matrix components, such as organic acids and fatty acids, compared to a NH₂ sorbent. The LC–QTOF-MS method was validated in terms of linearity, matrix effect, trueness, intra- and inter-day precisions, and selectivity for detection of the spiking at a concentration level of 0.01 mg/kg with 151 pesticides of brown rice, soybeans, and peanuts. Quantification was carried out using solvent-based calibration curves in this study.

Injecting a standard solution of 0.005 μg/mL, which corresponds to 0.01 mg/kg, five pesticides, i.e., hexaconazole, isoprocarb, methidathion, pentoxazone, and quizalofop ethyl, exhibited insufficient sensitivities, i.e., S/N < 10. Among them, the low sensitivity of quizalofop ethyl could be a consequence of a high background noise level due to polysiloxane contamination, which has a similar calculated exact mass (m/z 373.0981, [C₁₀H₃₁Si₄³⁰SiO₅]⁺). Therefore, the validation method was continued for 146, of the original 151, pesticides and achieved the required sensitivity of 0.005 μg/mL (corresponding to 0.01 mg/kg). Furthermore, because ferimzone and tricyclazole were detected at concentrations of 0.02 mg/kg and <0.01 mg/kg, respectively, in the brown rice sample used for the method validation in this study, ferimzone and tricyclazole were also excluded from the target compounds for method validation in brown rice. It should be noted that the residue levels of ferimzone and tricyclazole detected in brown rice were below the MRLs (2 ppm and 3 ppm, respectively) established in Japan.

The results of the recovery experiments are shown in Table 2. The trueness of the target pesticides was in the range of 70 to 120% and within the acceptable range of the criteria required by the Japanese [21] and EU [22] method validation guidelines, except for the cases of 7 pesticides in brown rice, 10 pesticides in soybeans, and 9 pesticides in peanuts. The intra- and inter-day precisions (expressed as RSD) were in most cases <10%. All target pesticides that achieved satisfactory trueness values fulfilled the precision criteria of the Japanese validation guideline, namely <25% for intra-day and <30% for inter-day precisions at 0.01 mg/kg [21]. Calibration curves for the target pesticides in the concentration range 0.00125–0.0075 μg/mL demonstrated sufficient linearity, with coefficients of determination (r²) of >0.99, with the exception of the five pesticides (hexaconazole, isoprocarb, methidathion, pentoxazone, and quizalofop-ethyl) for which the detection sensitivity was deemed to be insufficiently high. In addition, calibration curves were also linear in the wider range 0.002 to 0.1 μg/mL with r² > 0.99, except for the cases of the aforementioned five pesticides. These five pesticides resulted in linear calibration curves in the range 0.01 to 0.1 μg/mL with r² > 0.995.

It is well known that LC-MS/MS with ESI is susceptible to ion suppression, especially in complex food matrices, mainly due to the competition between analyte and coeluting matrix components [23]. Because the LC–QTOF-MS analyses were conducted using ESI in this study, ion suppression might also have occurred during these measurements. Thus, matrix effects were evaluated by comparing the peak areas of the matrix-matched standard solution at 0.005 μg/mL (corresponding to 0.01 mg/kg) to those of the standard solution prepared in methanol at the same concentration. The matrix effect values are shown in Table 3 and ranged from 0.8 to 1.1 for 134 (out of 144), 141 (out of 146), and 142 (out of 146) pesticides in brown rice, soybeans, and peanuts, respectively. The results indicate that no significant matrix effect occurred for most of the target pesticides studied, even though the soybean and peanut samples contained high amounts of lipids. Thus, these results suggested that the low trueness values for the acrinathrin (peanuts), cycloprothrin (brown rice), deltamethrin (peanuts), epoxiconazole (brown rice), fenpropathrin (brown rice), fipronil (brown rice), fluvalinate (brown rice, soybeans, and peanuts), hexythiazox (soybeans), imibenconazole (brown rice), and spinosyn A (brown rice and peanuts) samples were mainly caused by ion suppression.

Figure 1 shows the extracted ion chromatograms of representative pesticides in soybeans. No interfering peaks were detected in the extracted ion chromatograms of blank samples at the retention times of the target pesticides, which indicate the high selectivity of the method. The only exceptions were tridemorph in soybeans and peanuts. The interfering peaks were, however, less than 1/10 of the peak areas of the 0.005 μg/mL (corresponding to 0.01 mg/kg) standard solution of the target pesticides, conforming to the criteria of the Japanese validation guideline [21]. In addition, the retention times of the target pesticides in the matrices were found to be in good agreement with those in the solvent standard solutions (within ±0.02 min). Furthermore, the RSDs of retention times were <0.5% in brown rice, soybeans, and peanuts, except five pesticides (hexaconazole, isoprocarb, methidathion, pentoxazone, and quizalofop-ethyl) that showed low sensitivity.

The results of method validation revealed that LOQs, defined as the lowest concentration that can be quantified with satisfactory trueness values and precision, are 0.01 mg/kg for most pesticides (Table S1). MRLs of the target pesticides in brown rice, soybeans, and peanuts established in Japan are shown in Table S1. For pesticide/food combinations whose MRLs are not established, a uniform limit of 0.01 mg/kg is applied. As can be seen, MRLs are ≥0.01 mg/kg. Therefore, the proposed method exhibits sufficient sensitivity for regulatory purpose analysis.

3.3. Confirmation

For discriminating analytes from coeluting matrix components in complex foods at low concentrations, information on the exact mass and retention times of the target analytes may not be sufficient, even when using LC-Orbitrap-MS, which, compared to LC-TOF-MS, provides a high resolving power [19]. The EU guidelines [22] state that two ions, preferably a molecular adduct and at least one fragment ion, are required for accurate mass measurement by high-resolution MS. Hybrid HR-MS, such as QTOF-MS and QOrbitrap-MS, provide fragment-ion information via data dependent acquisition (DDA) and/or DIA [2,18]. In DDA, precursor ions are sequentially selected from full scans based on user-selected criteria (e.g., minimal intensity threshold, m/z values). In contrast, in DIA, all ionized compounds are subjected to fragmentation, and thus, DIA provides information regarding the fragment ions derived from all ions. In a previous study into pesticide residue analyses in vegetables and fruits using LC–QTOF-MS [14], we demonstrated DIA using the MS^E technique (Waters) [24], which provided full-scan data on both the molecular adduct (at low collision energy) and fragment (at high collision energy) ions in a single run, without selecting the precursor ion. In the study reported herein, we further applied the MS^E technique to brown rice, soybeans, and peanuts spiked at a level of 0.01 mg/kg. Figure 2 shows extracted ion chromatograms of molecular adduct and fragment ions from the soybean samples; Table 1 shows that the fragment ions could be detected at 0.005 μg/mL in the presence of the matrices. Among the 146 target pesticides, for 126 pesticides, we were able to detect one or more fragment ions; for 84 pesticides, we were able to detect one or more isotopic ions, and for 134 pesticides, we were able to detect one or more fragment ions and/or isotopic ions. Figure 3 shows extracted ion chromatograms of incurred ferimzone residue found in the rice sample used for validation in this study. As can be seen, the [M+H]⁺ (m/z 255.1604) for ferimzone together with its two fragments ions, i.e., [C₉H₁₀N]⁺ (m/z 132.0808) and [C₆H₁₀N₃]⁺ (m/z 124.0869), were clearly detected, suggesting that the MS^E technique could be a useful tool for obtaining fragment ion information for confirmation purposes. However, because the sensitivities of the fragment ion peaks for several pesticides were low, more sensitive methods, such as LC-MS/MS in SRM mode, may be required for confirmation, especially for the pesticides that were shown to be detected with low sensitivities using the LC–QTOF-MS technique described in this study.

4. Conclusions

In this study, the multiresidue method using LC–QTOF-MS in full-scan acquisition mode was validated for the determination of 151 pesticides in cereal grains and legumes. Sufficiently high sensitivities were achieved at 0.005 μg/mL (corresponding to 0.01 mg/kg), with the exception of 5 of the 151 pesticides. Excellent results were obtained in terms of trueness, intra- and inter-day precision, and selectivity for most of the target pesticides at 0.01 mg/kg. The results revealed that the LC–QTOF-MS method offers reliable quantitative analysis of pesticide residues in cereal grains and legumes. In addition, we demonstrated the usefulness of the MS^E technique for obtaining information on fragment ions for confirmation. Although we were unable to detect several parent and fragment ions owing to low S/N at 0.01 mg/kg, the LC–QTOF-MS method was shown to be suitable for regulatory-purpose analysis for most of the target pesticides.

Supplementary Materials

The following are available online at https://www.mdpi.com/2304-8158/10/1/78/s1, Table S1: MRLs set in Japan and LOQs of the target pesticides. Table S2: Results of method validation at MRL.

Author Contributions

Conceptualization, S.S.-S.; methodology, S.S.-S.; validation, S.S.-S.; investigation, S.S.-S.; writing—original draft preparation, S.S.-S.; writing—review and editing, S.S.-S. and H.A.; supervision, H.A.; funding acquisition, S.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Health, Labour, and Welfare of Japan, grant number H25-Syokuhin-ippan-002.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in the article or supplementary material.

Conflicts of Interest

The authors declare no conflict of interest.

References

Mostafalou, S.; Abdollahi, M. Pesticides and human chronic diseases: Evidences, mechanisms, and perspectives. Toxicol. Appl. Pharmacol. 2013, 268, 157–177. [Google Scholar] [CrossRef] [PubMed]
Wong, J.W.; Wang, J.; Chow, W.; Carlson, R.; Jia, Z.; Zhang, K.; Hayward, D.G.; Chang, J.S. Perspectives on liquid chromatography-high-resolution mass spectrometry for pesticide screening in foods. J. Agric. Food Chem. 2018, 66, 9573–9581. [Google Scholar] [CrossRef] [PubMed]
García López, M.; Fussell, R.J.; Stead, S.L.; Roberts, D.; McCullagh, M.; Rao, R. Evaluation and validation of an accurate mass screening method for the analysis of pesticides in fruits and vegetables using liquid chromatography-quadrupole-time of flight-mass spectrometry with automated detection. J. Chromatogr. A 2014, 1373, 40–50. [Google Scholar] [CrossRef] [PubMed]
Wang, Z.; Chang, Q.; Kang, J.; Cao, Y.; Ge, N.; Fan, C.; Pang, G.-F. Screening and identification strategy for 317 pesticides in fruits and vegetables by liquid chromatography-quadrupole time-of-flight high resolution mass spectrometry. Anal. Methods 2015, 7, 6385–6402. [Google Scholar] [CrossRef]
Sivaperumal, P.; Anand, P.; Riddhi, L. Rapid determination of pesticide residues in fruits and vegetables, using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry. Food Chem. 2015, 168, 356–365. [Google Scholar] [CrossRef]
Kaufmann, A.; Dvorak, V.; Crüzer, C.; Butcher, P.; Maden, K.; Walker, S.; Widmer, M.; Schürmann, A. Study of high-resolution mass spectrometry technology as a replacement for tandem mass spectrometry in the field of quantitative pesticide residue analysis. J. AOAC Int. 2012, 95, 528–548. [Google Scholar] [CrossRef]
Mezcua, M.; Malato, O.; García-Reyes, J.F.; Molina-Díaz, A.; Fernández-Alba, A.R. Accurate-mass databases for comprehensive screening of pesticide residues in food by fast liquid chromatography time-of-flight mass spectrometry. Anal. Chem. 2009, 81, 913–929. [Google Scholar] [CrossRef]
Rajski, Ł.; del Mar Gómez-Ramos, M.; Fernández-Alba, A.R. Large pesticide multiresidue screening method by liquid chromatography-Orbitrap mass spectrometry in full scan mode applied to fruit and vegetables. J. Chromatogr. A 2014, 1360, 119–127. [Google Scholar] [CrossRef]
Lacina, O.; Urbanova, J.; Poustka, J.; Hajslova, J. Identification/quantification of multiple pesticide residues in food plants by ultra-high-performance liquid chromatography-time-of-flight mass spectrometry. J. Chromatogr. A 2010, 1217, 648–659. [Google Scholar] [CrossRef]
Wang, J.; Chow, W.; Chang, J.; Wong, J.W. Development and validation of a qualitative method for target screening of 448 pesticide residues in fruits and vegetables using UHPLC/ESI Q-Orbitrap based on data-independent acquisition and compound database. J. Agric. Food Chem. 2017, 65, 473–493. [Google Scholar] [CrossRef]
Wang, J.; Chow, W.; Chang, J.; Wong, J.W. Ultrahigh-performance liquid chromatography electrospray ionization Q-Orbitrap mass spectrometry for the analysis of 451 pesticide residues in fruits and vegetables: Method development and validation. J. Agric. Food Chem. 2014, 62, 10375–10391. [Google Scholar] [CrossRef]
Pérez-Ortega, P.; Lara-Ortega, F.J.; García-Reyes, J.F.; Beneito-Cambra, M.; Gilbert-López, B.; Martos, N.R.; Molina-Díaz, A. Determination of over 350 multiclass pesticides in jams by ultra-high performance liquid chromatography time-of-flight mass spectrometry (UHPLC-TOFMS). Food Anal. Methods 2016, 9, 1939–1957. [Google Scholar] [CrossRef]
Gómez-Ramos, M.M.; Ferrer, C.; Malato, O.; Agüera, A.; Fernández-Alba, A.R. Liquid chromatography-high-resolution mass spectrometry for pesticide residue analysis in fruit and vegetables: Screening and quantitative studies. J. Chromatogr. A 2013, 1287, 24–37. [Google Scholar] [CrossRef]
Saito-Shida, S.; Nemoto, S.; Teshima, R.; Akiyama, H. Quantitative analysis of pesticide residues in vegetables and fruits by liquid chromatography quadrupole time-of-flight mass spectrometry. Food Addit. Contam. A 2016, 33, 119–127. [Google Scholar] [CrossRef]
Gómez-Pérez, M.L.; Romero-González, R.; Vidal, J.L.; Frenich, A.G. Identification of transformation products of pesticides and veterinary drugs in food and related matrices: Use of retrospective analysis. J. Chromatogr. A 2015, 1389, 133–138. [Google Scholar] [CrossRef]
Ibáñez, M.; Portolés, T.; Rúbies, A.; Muñoz, E.; Muñoz, G.; Pineda, L.; Serrahima, E.; Sancho, J.V.; Centrich, F.; Hernández, F. The power of hyphenated chromatography/time-of-flight mass spectrometry in public health laboratories. J. Agric. Food Chem. 2012, 60, 5311–5323. [Google Scholar] [CrossRef][Green Version]
Polgár, L.; García-Reyes, J.F.; Fodor, P.; Gyepes, A.; Dernovics, M.; Abrankó, L.; Gilbert-López, B.; Molina-Díaz, A. Retrospective screening of relevant pesticide metabolites in food using liquid chromatography high resolution mass spectrometry and accurate-mass databases of parent molecules and diagnostic fragment ions. J. Chromatogr. A 2012, 1249, 83–91. [Google Scholar] [CrossRef]
Álvarez-Ruiz, R.; Pico, Y. Sequential window acquisition of all theoretical fragments versus information dependent acquisition for suspected-screening of pharmaceuticals in sediments and mussels by ultra-high pressure liquid chromatography-quadrupole time-of-flight-mass spectrometry. J. Chromatogr. A 2019, 1595, 81–90. [Google Scholar] [CrossRef]
Saito-Shida, S.; Hamasaka, T.; Nemoto, S.; Akiyama, H. Multiresidue determination of pesticides in tea by liquid chromatography-high-resolution mass spectrometry: Comparison between Orbitrap and time-of-flight mass analyzers. Food Chem. 2018, 256, 140–148. [Google Scholar] [CrossRef]
Huertas Pérez, J.F.; Sejerøe-Olsen, B.; Fernández Alba, A.R.; Schimmel, H.; Dabrio, M. Accurate determination of selected pesticides in soya beans by liquid chromatography coupled to isotope dilution mass spectrometry. Talanta 2015, 137, 120–129. [Google Scholar] [CrossRef]
Ministry of Health, Labour and Welfare, Japan (MHLW). Guideline for the Validation of Analytical Methods for Agricultural Chemical Residues in Food. Available online: https://www.mhlw.go.jp/english/topics/foodsafety/positivelist060228/dl/181130_21.pdf (accessed on 24 November 2020).
European Commission. Analytical Quality Control and Method Validation Procedures for Pesticides Residues Analysis in Food and Feed. Document No. SANTE/12682/2019. Available online: https://www.eurl-pesticides.eu/userfiles/file/EurlALL/AqcGuidance_SANTE_2019_12682.pdf (accessed on 24 November 2020).
Furey, A.; Moriarty, M.; Bane, V.; Kinsella, B.; Lehane, M. Ion suppression; a critical review on causes, evaluation, prevention and applications. Talanta 2013, 115, 104–122. [Google Scholar] [CrossRef]
Masiá, A.; Ibáñez, M.; Blasco, C.; Sancho, J.V.; Picó, Y.; Hernández, F. Combined use of liquid chromatography triple quadrupole mass spectrometry and liquid chromatography quadrupole time-of-flight mass spectrometry in systematic screening of pesticides and other contaminants in water samples. Anal. Chim. Acta 2013, 761, 117–127. [Google Scholar] [CrossRef]

Figure 1. Extracted ion chromatograms (mass window ±5 mDa) of representative compounds. (a) Azoxystrobin (m/z 404.1241); (b) Diazinon (m/z 305.1083), (c) Indoxacarb (m/z 528.0780). Upper plots: standard solution in solvent (0.005 μg/mL, corresponding to 0.01 mg/kg). Middle plots: soybeans spiked with 0.01 mg/kg of the pesticide. Lower plots: soybean blank extract.

Figure 2. Extracted ion chromatograms (mass window ±5 mDa) of parent and fragment ions of (a) boscalid and (b) cyazofamid in 0.01 mg/kg-spiked soybean blank extract. Upper plots: [M + H]⁺ ((a) m/z 343.0399, (b) m/z 325.0521). Lower plots: fragment ions ((a) m/z 307.0633, (b) m/z 108.0114).

Figure 3. Extracted ion chromatograms (mass window ±5 mDa) of incurred ferimzone residue in a rice sample: (a) parent ion ([M+H]⁺, m/z 255.1604) and (b,c) its fragment ions ((b) m/z 132.0808, (c) m/z 124.0869).

Table 1. Elemental composition, retention time, and calculated exact mass of the target pesticides.

Compound	Retention Time (min)	Molecular Formula	Type of Ion	Calculated Exact Mass (m/z)	Fragment Ion 1		Fragment Ion 2
Compound	Retention Time (min)	Molecular Formula	Type of Ion	Calculated Exact Mass (m/z)	Elemental Composition	Calculated Exact Mass (m/z)	Elemental Composition	Calculated Exact Mass (m/z)
Acetamiprid	5.5	C₁₀H₁₁ClN₄	[M+H]⁺	223.0745	C₆H₅ClN	126.0105	C₆H₄N	90.0338
Acetochlor	9.3	C₁₄H₂₀ClNO₂	[M+H]⁺	270.1255	C₁₂H₁₅ClNO	224.0837	C₁₀H₁₄N	148.1121
Acibenzolar-S-methyl	9.1	C₈H₆N₂OS₂	[M+H]⁺	210.9994	C₆H₄N₂S	136.0090
Acrinathrin	11.0	C₂₆H₂₁F₆NO₅	[M+NH₄]⁺	559.1662	C₁₃H₉O	181.0648
Ametryn	8.8	C₉H₁₇N₅S	[M+H]⁺	228.1277	C₆H₁₂N₅S	186.0808	C₄H₆N₃	96.0556
Anilofos	9.6	C₁₃H₁₉ClNO₃PS₂	[M+H]⁺	368.0305	C₄H₈O₃PS₂	198.9647	C₂H₆O₂PS	124.9821
Aramite	10.4	C₁₅H₂₃ClO₄S	[M+NH₄]⁺	352.1344	C₁₃H₁₉O	191.1430
Atrazine	8.1	C₈H₁₄ClN₅	[M+H]⁺	216.1010	C₅H₉ClN₅	174.0541	C₄H₆N₃	96.0556
Azoxystrobin	8.7	C₂₂H₁₇N₃O₅	[M+H]⁺	404.1241	C₂₁H₁₄N₃O₄	372.0979	C₁₉H₁₁N₃O₃	329.0795
Benalaxyl	9.7	C₂₀H₂₃NO₃	[M+H]⁺	326.1751	C₁₀H₁₄N	148.1121	C₁₂H₁₈NO₂	208.1332
Bendiocarb	7.1	C₁₁H₁₃NO₄	[M+H]⁺	224.0917	C₆H₅O₂	109.0284
Benzofenap	10.3	C₂₂H₂₀Cl₂N₂O₃	[M+H]⁺	431.0924	C₈H₉	105.0699	C₈H₇O	119.0491
Bitertanol	9.8	C₂₀H₂₃N₃O₂	[M+H]⁺	338.1863
Boscalid	8.7	C₁₈H₁₂Cl₂N₂O	[M+H]⁺	343.0399	C₁₈H₁₂ClN₂O	307.0633	C₆H₃ClNO	139.9898
Bromacil	7.1	C₉H₁₃BrN₂O₂	[M+H]⁺	261.0233	C₅H₆BrN₂O₂	204.9607
Buprofezin	10.4	C₁₆H₂₃N₃OS	[M+H]⁺	306.1635	C₉H₁₇N₂OS	201.1056	C₇H₈N	106.0651
Butafenacil	9.1	C₂₀H₁₈ClF₃N₂O₆	[M+NH₄]⁺	492.1144	C₁₃H₇ClF₃N₂O₃	331.0092	C₈H₃ClNO₂	179.9847
Cadusafos	10.0	C₁₀H₂₃O₂PS₂	[M+H]⁺	271.0950	C₂H₈O₂PS₂	158.9698	H₄O₂PS₂	130.9385
Carbaryl	7.2	C₁₂H₁₁NO₂	[M+H]⁺	202.0863
Carpropamid	9.6	C₁₅H₁₈Cl₃NO	[M+H]⁺	334.0527	C₈H₈Cl	139.0309	C₇H₁₂Cl₂NO	196.0290
Chlorfenvinphos (E, Z)	9.7, 9.8	C₁₂H₁₄Cl₃O₄P	[M+H]⁺	358.9768	C₄H₁₂O₄P	155.0468
Chloridazon	5.6	C₁₀H₈ClN₃O	[M+H]⁺	222.0429
Chloroxuron	9.0	C₁₅H₁₅ClN₂O₂	[M+H]⁺	291.0895	C₃H₆NO	72.0444	C₉H₁₂N₂O	164.0944
Chlorpyrifos	10.7	C₉H₁₁Cl₃NO₃PS	[M+H]⁺	349.9336	C₅H₃Cl₃NO	197.9275	H₂O₂PS	96.9508
Chlorpyrifos methyl	10.1	C₇H₇Cl₃NO₃PS	[M+H]⁺	321.9023
Chromafenozide	9.2	C₂₄H₃₀N₂O₃	[M+H]⁺	395.2329	C₁₁H₁₁O₂	175.0754
Clomeprop	10.4	C₁₆H₁₅Cl₂NO₂	[M+H]⁺	324.0553
Cloquintocet mexyl	10.5	C₁₈H₂₂ClNO₃	[M+H]⁺	336.1361	C₁₁H₉ClNO₃	238.0265	C₁₀H₇ClNO	192.0211
Clothianidin	5.0	C₆H₈ClN₅O₂S	[M+H]⁺	250.0160	C₆H₉N₄S	169.0542	C₄H₃ClNS	131.9669
Cumyluron	9.0	C₁₇H₁₉ClN₂O	[M+H]⁺	303.1259	C₈H₁₀ClN₂O	185.0476	C₇H₆Cl	125.0153
Cyanazine	6.9	C₉H₁₃ClN₆	[M+H]⁺	241.0963	C₈H₁₃ClN₅	214.0854	C₄H₆N₃	96.0556
Cyazofamid	9.3	C₁₃H₁₃ClN₄O₂S	[M+H]⁺	325.0521	C₂H₆NO₂S	108.0114
Cycloprothrin	10.9	C₂₆H₂₁Cl₂NO₄	[M+NH₄]⁺	499.1186
Cyflufenamid	9.8	C₂₀H₁₇F₅N₂O₂	[M+H]⁺	413.1283	C₁₂H₁₂F₅N₂O	295.0864	C₈H₆F₅N₂O	241.0395
Cyproconazole	8.8, 9.0	C₁₅H₁₈ClN₃O	[M+H]⁺	292.1211	C₇H₆Cl	125.0153
Cyprodinil	9.9	C₁₄H₁₅N₃	[M+H]⁺	226.1339
Daimuron	8.9	C₁₇H₂₀N₂O	[M+H]⁺	269.1648	C₈H₁₁N₂O	151.0866
Deltamethrin	11.0	C₂₂H₁₉Br₂NO₃	[M+NH₄]⁺	523.0049
Diazinon	9.8	C₁₂H₂₁N₂O₃PS	[M+H]⁺	305.1083	C₅H₁₅NO₃S	169.0767	H₂O₂PS	96.9508
Difenoconazole	9.7, 10.0	C₁₉H₁₇Cl₂N₃O₃	[M+H]⁺	406.0720	C₁₃H₉Cl₂O	251.0025	C₁₇H₁₅Cl₂O₃	337.0393
Diflubenzuron	9.3	C₁₄H₉ClF₂N₂O₂	[M+H]⁺	311.0393	C₇H₆F₂NO	158.0412	C₇H₃F₂O	141.0146
Diflufenican	10.1	C₁₉H₁₁F₅N₂O₂	[M+H]⁺	395.0813	C₁₃H₇F₃NO₂	266.0423	C₁₃H₆F₂NO₂	246.0361
Dimethirimol	7.8	C₁₁H₁₉N₃O	[M+H]⁺	210.1601	C₈H₁₄NO	140.1070	C₅H₈NO	98.0600
Dimethoate	5.4	C₅H₁₂NO₃PS₂	[M+H]⁺	230.0069	C₄H₈O₃PS₂	198.9647	C₂H₆O₂PS	124.9821
Dimethomorph (E, Z)	8.6, 8.8	C₂₁H₂₂ClNO₄	[M+H]⁺	388.1310	C₁₇H₁₄ClO₃	301.0626	C₉H₉O₃	165.0546
Diuron	8.1	C₉H₁₀Cl₂N₂O	[M+H]⁺	233.0243	C₃H₆NO	72.0444	C₆H₄Cl₂N	159.9715
Edifenphos	9.7	C₁₄H₁₅O₂PS₂	[M+H]⁺	311.0324	C₁₂H₁₂O₂PS₂	283.0011	C₆H₅S	109.0106
Epoxiconazole	9.2	C₁₇H₁₃ClFN₃O	[M+H]⁺	330.0804	C₅H₁₀ClO	121.0415
Ethion	10.6	C₉H₂₂O₄P₂S₄	[M+H]⁺	384.9949	CH₄O₂PS₂	142.9385	C₅H₁₂O₂PS₂	199.0011
Ethiprole	8.5	C₁₃H₉Cl₂F₃N₄OS	[M+H]⁺	396.9899	C₁₁H₄Cl₂F₃N₄S	350.9480	C₈H₄Cl₂F₃N₂	254.9698
Etoxazole	10.8	C₂₁H₂₃F₂NO₂	[M+H]⁺	360.1770	C₇H₃F₂O	141.0146	C₁₇H₁₆F₂NO₂	304.1144
Etrimfos	9.8	C₁₀H₁₇N₂O₄PS	[M+H]⁺	293.0719	C₈H₁₄N₂O₄PS	265.0406	C₂H₆O₂PS	124.9821
Fenamidone	8.7	C₁₇H₁₇N₃OS	[M+H]⁺	312.1165	C₁₅H₁₄N₃	236.1182	C₆H₆N	92.0495
Fenamiphos	9.3	C₁₃H₂₂NO₃PS	[M+H]⁺	304.1131
Fenarimol	9.2	C₁₇H₁₂Cl₂N₂O	[M+H]⁺	331.0399	C₄H₅N₂	81.0447
Fenbuconazole	9.2	C₁₉H₁₇ClN₄	[M+H]⁺	337.1215	C₇H₆Cl	125.0153	C₂H₄N₃	70.0400
Fenobucarb	8.5	C₁₂H₁₇NO₂	[M+H]⁺	208.1332
Fenoxaprop ethyl	10.3	C₁₈H₁₆ClNO₅	[M+H]⁺	362.0790	C₁₅H₁₁ClNO₃	288.0422
Fenoxycarb	9.5	C₁₇H₁₉NO₄	[M+H]⁺	302.1387	C₃H₆NO₂	88.0393	C₅H₁₀NO₂	116.0706
Fenpropathrin	10.8	C₂₂H₂₃NO₃	[M+H]⁺	350.1751
Fenpropimorph	11.4	C₂₀H₃₃NO	[M+H]⁺	304.2635
Ferimzone (E, Z)	8.9(E), 9.0(Z)	C₁₅H₁₈N₄	[M+H]⁺	255.1604	C₉H₁₀N	132.0808	C₆H₁₀N₃	124.0869
Fipronil	9.3	C₁₂H₄Cl₂F₆N₄OS	[M+NH₄]⁺	453.9725	C₁₁H₅Cl₂F₃N₄OS	367.9508
Flamprop methyl	9.0	C₁₇H₁₅ClFNO₃	[M+H]⁺	336.0797	C₇H₅O	105.0335
Fludioxonil	8.8	C₁₂H₆F₂N₂O₂	[M+NH₄]⁺	266.0736
Flufenacet	9.1	C₁₄H₁₃F₄N₃O₂S	[M+H]⁺	364.0737	C₈H₇FNO	152.0506	C₁₁H₁₃FNO	194.0976
Fluquinconazole	9.1	C₁₆H₈Cl₂FN₅O	[M+H]⁺	376.0163	C₁₄H₆Cl₂FN₂O	306.9836
Fluridone	8.6	C₁₉H₁₄F₃NO	[M+H]⁺	330.1100	C₁₉H₁₄F₂NO	310.1038
Fluvalinate	11.1	C₂₆H₂₂ClF₃N₂O₃	[M+H]⁺	503.1344	C₁₃H₉O	181.0648
Furametpyr	7.9	C₁₇H₂₀ClN₃O₂	[M+H]⁺	334.1317	C₆H₆ClN₂O	157.0163	C₁₅H₁₇ClN₃O	290.1055
Hexaconazole	9.7	C₁₄H₁₇Cl₂N₃O	[M+H]⁺	314.0821
Hexaflumuron	10.1	C₁₆H₈Cl₂F₆N₂O₃	[M+H]⁺	460.9889	C₇H₆F₂NO	158.0412
Hexythiazox	10.6	C₁₇H₂₁ClN₂O₂S	[M+H]⁺	353.1085	C₉H₁₁ClN	168.0575	C₁₀H₁₁ClNOS	228.0244
Imazalil	9.6	C₁₄H₁₄Cl₂N₂O	[M+H]⁺	297.0556	C₁₁H₉Cl₂N₂O	255.0086
Imibenconazole	10.4	C₁₇H₁₃Cl₃N₄S	[M+H]⁺	410.9999	C₇H₆Cl	125.0153	C₈H₈ClS	171.0030
Indanofan	9.3	C₂₀H₁₇ClO₃	[M+H]⁺	341.0939	C₁₁H₁₁O₂	175.0754
Indoxacarb	10.0	C₂₂H₁₇ClF₃N₃O₇	[M+H]⁺	528.0780	C₈H₄F₃NO₂	203.0189	C₉H₇F₃NO₂	218.0423
Iprovalicarb	9.1	C₁₈H₂₈N₂O₃	[M+H]⁺	321.2173
Isoprocarb	7.9	C₁₁H₁₅NO₂	[M+H]⁺	194.1176
Isoxathion	10.0	C₁₃H₁₆NO₄PS	[M+H]⁺	314.0610	C₇H₅O	105.0335	C₁₁H₁₃NO₄PS	286.0297
Kresoxim methyl	9.6	C₁₈H₁₉NO₄	[M+H]⁺	314.1387	C₁₅H₁₂NO	222.0913	C₁₆H₁₁O₂	235.0754
Lactofen	10.3	C₁₉H₁₅ClF₃NO₇	[M+NH₄]⁺	479.0827	C₁₄H₆ClF₃NO₄	343.9932	C₈H₃ClF₃O₂	222.9768
Linuron	8.7	C₉H₁₀Cl₂N₂O₂	[M+H]⁺	249.0192	C₈H₇ClN₂O	182.0241	C₆H₄Cl₂N	159.9715
Lufenuron	10.5	C₁₇H₈Cl₂F₈N₂O₃	[M+H]⁺	510.9857	C₇H₆F₂NO	158.0412
Malathion	9.0	C₁₀H₁₉O₆PS₂	[M+H]⁺	331.0433	C₆H₇O₃	127.0390	C₄H₃O₃	99.0077
Mepanipyrim	9.4	C₁₄H₁₃N₃	[M+H]⁺	224.1182	C₇H₈N	106.0651	C₁₃H₁₁N₃	209.0947
Metalaxyl	8.0	C₁₅H₂₁NO₄	[M+H]⁺	280.1543	C₁₃H₁₈NO₂	220.1332	C₁₂H₁₈NO	192.1383
Methabenzthiazuron	8.1	C₁₀H₁₁N₃OS	[M+H]⁺	222.0696	C₈H₉N₂S	165.0481	C₇H₆N₂S	150.0246
Methidathion	8.4	C₆H₁₁N₂O₄PS₃	[M+H]⁺	302.9691
Methiocarb	8.7	C₁₁H₁₅NO₂S	[M+H]⁺	226.0896	C₈H₉O	121.0648	C₉H₁₃OS	169.0682
Metolachlor	9.4	C₁₅H₂₂ClNO₂	[M+H]⁺	284.1412	C₁₄H₁₉ClNO	252.1150	C₁₂H₁₈N	176.1434
Monolinuron	7.7	C₉H₁₁ClN₂O₂	[M+H]⁺	215.0582	C₆H₅ClN	126.0105	C₈H₈N₂O	148.0631
Myclobutanil	8.8	C₁₅H₁₇ClN₄	[M+H]⁺	289.1215	C₇H₆Cl	125.0153
Naproanilide	9.5	C₁₉H₁₇NO₂	[M+H]⁺	292.1332	C₁₂H₁₁O	171.0804	C₈H₁₀N	120.0808
Napropamide	9.3	C₁₇H₂₁NO₂	[M+H]⁺	272.1645	C₁₂H₁₁O	171.0804	C₁₃H₁₁O₂	199.0754
Norflurazon	8.3	C₁₂H₉ClF₃N₃O	[M+H]⁺	304.0459	C₁₂H₉ClF₂N₃O	284.0397	C₇H₅F₃N	160.0369
Novaluron	10.1	C₁₇H₉ClF₈N₂O₄	[M+H]⁺	493.0196	C₇H₆F₂NO	158.0412	C₇H₃F₂O	141.0146
Oxadixyl	6.6	C₁₄H₁₈N₂O₄	[M+H]⁺	279.1339	C₁₂H₁₅N₂O₂	219.1128
Oxaziclomefone	10.3	C₂₀H₁₉Cl₂NO₂	[M+H]⁺	376.0866	C₁₁H₁₂NO₂	190.0863	C₁₀H₉O₂	161.0597
Paclobutrazol	8.7	C₁₅H₂₀ClN₃O	[M+H]⁺	294.1368	C₂H₄N₃	70.0400
Penconazole	9.5	C₁₃H₁₅Cl₂N₃	[M+H]⁺	284.0716	C₇H₅Cl₂	158.9763
Pencycuron	9.9	C₁₉H₂₁ClN₂O	[M+H]⁺	329.1415
Pentoxazone	10.3	C₁₇H₁₇ClFNO₄	[M+H]⁺	354.0903
Phenmedipham	8.3	C₁₆H₁₆N₂O₄	[M+H]⁺	301.1183	C₇H₆NO₂	136.0393	C₈H₁₀NO₃	168.0655
Phenthoate	9.6	C₁₂H₁₇O₄PS₂	[M+H]⁺	321.0379	C₉H₁₂O₂PS₂	247.0011
Phosalone	9.8	C₁₂H₁₅ClNO₄PS₂	[M+H]⁺	367.9941	C₈H₅ClNO₂	182.0003	C₇H₅ClN	138.0105
Phosphamidon	6.7	C₁₀H₁₉ClNO₅P	[M+H]⁺	300.0762	C₈H₁₃ClNO	174.0680	C₂H₈O₄P	127.0155
Piperonyl butoxide	10.6	C₁₉H₃₀O₅	[M+NH₄]⁺	356.2431	C₁₁H₁₃O₂	177.0910
Pirimicarb	7.9	C₁₁H₁₈N₄O₂	[M+H]⁺	239.1503	C₃H₆NO	72.0444
Pirimiphos methyl	10.0	C₁₁H₂₀N₃O₃PS	[M+H]⁺	306.1036	C₉H₁₄N₃	164.1182	C₅H₆N₃	108.0556
Prochloraz	9.8	C₁₅H₁₆Cl₃N₃O₂	[M+H]⁺	376.0381	C₁₂H₁₃Cl₃NO₂	308.0006	C₉H₇Cl₃NO₂	265.9537
Profenofos	10.3	C₁₁H₁₅BrClO₃PS	[M+H]⁺	372.9424	C₆H₆BrClO₃PS	302.8642	C₉H₁₂BrClO₃PS	344.9111
Prometryn	9.3	C₁₀H₁₉N₅S	[M+H]⁺	242.1434	C₄H₈N₅S	158.0495	C₇H₁₄N₅S	200.0964
Propachlor	8.1	C₁₁H₁₄ClNO	[M+H]⁺	212.0837	C₈H₉ClNO	170.0367
Propanil	8.6	C₉H₉Cl₂NO	[M+H]⁺	218.0134	C₆H₆ClN	127.0183	C₆H₆Cl₂N	161.9872
Propaquizafop	10.4	C₂₂H₂₂ClN₃O₅	[M+H]⁺	444.1321	C₅H₁₀NO	100.0757
Propargite	10.7	C₁₉H₂₆O₄S	[M+NH₄]⁺	368.1890
Propiconazole	9.6	C₁₅H₁₇Cl₂N₃O₂	[M+H]⁺	342.0771
Propyzamide	8.9	C₁₂H₁₁Cl₂NO	[M+H]⁺	256.0290	C₇H₆Cl₂NO	189.9821	C₇H₃Cl₂O	172.9555
Pyraclofos	9.8	C₁₄H₁₈ClN₂O₃PS	[M+H]⁺	361.0537	C₉H₇ClN₂O₃P	256.9877
Pyraclostrobin	9.9	C₁₉H₁₈ClN₃O₄	[M+H]⁺	388.1059	C₁₀H₁₂NO₃	194.0812
Pyrazophos	10.1	C₁₄H₂₀N₃O₅PS	[M+H]⁺	374.0934	C₁₀H₁₂N₃O₃	222.0873	C₈H₈N₃O₃	194.0560
Pyriftalid	8.7	C₁₅H₁₄N₂O₄S	[M+H]⁺	319.0747	C₆H₇N₂O₂	139.0502	C₁₅H₁₃N₂O₃S	301.0641
Pyrimethanil	8.9	C₁₂H₁₃N₃	[M+H]⁺	200.1182
Pyriproxyfen	10.7	C₂₀H₁₉NO₃	[M+H]⁺	322.1438	C₅H₆NO	96.0444	C₁₂H₉O₂	185.0597
Quinalphos	9.7	C₁₂H₁₅N₂O₃PS	[M+H]⁺	299.0614	C₈H₇N₂O	147.0553
Quinoxyfen	10.7	C₁₅H₈Cl₂FNO	[M+H]⁺	308.0040	C₁₅H₈ClFNO	272.0273	C₉H₅Cl₂N	196.9794
Quizalofop ethyl	10.3	C₁₉H₁₇ClN₂O₄	[M+H]⁺	373.0950
Simazine	7.3	C₇H₁₂ClN₅	[M+H]⁺	202.0854	C₆H₁₀N₃	124.0869	C₄H₇ClN₃	132.0323
Simeconazole	9.0	C₁₄H₂₀FN₃OSi	[M+H]⁺	294.1432	C₂H₄N₃	70.0400
Spinosyn A	11.4	C₄₁H₆₅NO₁₀	[M+H]⁺	732.4681	C₈H₁₆NO	142.1226
Spinosyn D	11.7	C₄₂H₆₇NO₁₀	[M+H]⁺	746.4838	C₈H₁₆NO	142.1226
Spiroxamine	10.4, 10.5	C₁₈H₃₅NO₂	[M+H]⁺	298.2741	C₈H₁₈NO	144.1383	C₆H₁₄N	100.1121
Tebuconazole	9.5	C₁₆H₂₂ClN₃O	[M+H]⁺	308.1524	C₂H₄N₃	70.0400
Tebufenpyrad	10.4	C₁₈H₂₄ClN₃O	[M+H]⁺	334.1681	C₄H₆ClN₂	117.0214
Tebuthiuron	7.2	C₉H₁₆N₄OS	[M+H]⁺	229.1118	C₇H₁₄N₃S	172.0903	C₃H₆N₃S	116.0277
Teflubenzuron	10.4	C₁₄H₆Cl₂F₄N₂O₂	[M+H]⁺	380.9815
Terbutryn	9.4	C₁₀H₁₉N₅S	[M+H]⁺	242.1434	C₆H₁₂N₅S	186.0808	C₅H₈N₅	138.0774
Tetrachlorvinphos	9.4	C₁₀H₉Cl₄O₄P	[M+H]⁺	366.9036	C₂H₈O₄P	127.0155	C₈H₃Cl₃	203.9295
Tetraconazole	9.1	C₁₃H₁₁Cl₂F₄N₃O	[M+H]⁺	372.0288	C₇H₅Cl₂	158.9763	C₂H₄N₃	70.0400
Thiacloprid	6.0	C₁₀H₉ClN₄S	[M+H]⁺	253.0309	C₆H₅ClN	126.0105	C₆H₄N	90.0338
Tolfenpyrad	10.5	C₂₁H₂₂ClN₃O₂	[M+H]⁺	384.1473	C₁₄H₁₃O	197.0961	C₆H₁₀ClN₂	145.0527
Triadimefon	8.9	C₁₄H₁₆ClN₃O₂	[M+H]⁺	294.1004	C₁₁H₁₄ClO	197.0728
Triadimenol	8.9	C₁₄H₁₈ClN₃O₂	[M+H]⁺	296.1160	C₂H₄N₃	70.0400
Triazophos	9.2	C₁₂H₁₆N₃O₃PS	[M+H]⁺	314.0723	C₈H₈N₃O	162.0662	C₇H₇N₂	119.0604
Tricyclazole	6.4	C₉H₇N₃S	[M+H]⁺	190.0433	C₈H₇N₂S	163.0324	C₇H₆NS	136.0215
Tridemorph	11.9, 12.3	C₁₉H₃₉NO	[M+H]⁺	298.3104
Trifloxystrobin	10.1	C₂₀H₁₉F₃N₂O₄	[M+H]⁺	409.1370	C₉H₇F₃N	186.0525	C₁₁H₁₂NO₃	206.0812
Triflumizole	10.1	C₁₅H₁₅ClF₃N₃O	[M+H]⁺	346.0929	C₁₂H₁₂ClF₃NO	278.0554
Triflumuron	9.8	C₁₅H₁₀ClF₃N₂O₃	[M+H]⁺	359.0405	C₇H₇ClNO	156.0211	C₇H₄ClO	138.9945
Triticonazole	9.1	C₁₇H₂₀ClN₃O	[M+H]⁺	318.1368	C₂H₄N₃	70.0400

Table 2. Trueness and intra- and inter-day precision of the target pesticides.

Compound	Brown Rice			Soybeans			Peanuts
Compound	Trueness (%)	Intra-Day Precision (relative standard deviation (RSD)%)	Inter-Day Precision (RSD%)	Trueness (%)	Intra-Day Precision (RSD%)	Inter-Day Precision (RSD%)	Trueness (%)	Intra-Day Precision (RSD%)	Inter-Day Precision (RSD%)
Acetamiprid	87	13	13	77	11	17	85	7	11
Acetochlor	77	1	4	84	5	8	89	4	5
Acibenzolar S-methyl	82	5	10	80	8	8	82	4	4
Acrinathrin	72	9	9	78	5	20	62	4	4
Ametryn	86	4	4	87	6	6	89	2	2
Anilofos	84	3	3	84	5	7	90	2	2
Aramite	80	3	6	83	6	7	71	6	6
Atrazine	87	3	3	89	5	7	91	2	2
Azoxystrobin	88	3	3	90	4	6	92	2	2
Benalaxyl	86	4	4	88	7	8	91	2	2
Bendiocarb	81	6	9	86	6	14	87	4	6
Benzofenap	84	3	5	83	6	9	86	3	3
Bitertanol	84	3	18	80	4	23	82	3	6
Boscalid	86	3	3	86	4	6	91	2	3
Bromacil	81	2	2	83	3	7	87	3	3
Buprofezin	82	4	5	75	10	10	73	2	2
Butafenacil	86	3	3	88	4	7	93	1	2
Cadusafos	81	2	3	78	6	9	84	4	4
Carbaryl	86	3	5	89	4	6	92	2	3
Carpropamid	82	3	3	82	7	8	88	2	2
Chlorfenvinphos (E, Z)	85	2	3	85	6	9	90	2	3
Chloridazon	77	5	5	80	4	5	88	3	3
Chloroxuron	80	4	6	84	5	7	92	2	2
Chlorpyrifos	83	3	5	77	8	9	75	5	5
Chlorpyrifos methyl	77	7	12	83	5	10	80	8	9
Chromafenozide	73	5	6	83	5	9	84	4	8
Clomeprop	79	4	6	74	6	9	70	6	6
Cloquintocet mexyl	88	2	3	86	7	11	85	2	3
Clothianidin	71	7	7	77	3	5	81	2	2
Cumyluron	77	3	3	87	3	6	88	4	4
Cyanazine	85	3	4	87	5	7	85	4	4
Cyazofamid	71	4	5	78	3	6	80	4	5
Cycloprothrin	61	8	14	47	9	17	70	3	5
Cyflufenamid	81	2	3	81	5	10	88	2	3
Cyproconazole	76	3	3	82	5	8	88	1	3
Cyprodinil	84	2	2	42	37	37	78	1	2
Daimuron	71	6	12	91	4	5	91	6	7
Deltamethrin	85	2	8	81	7	13	38	3	11
Diazinon	86	5	5	86	5	6	87	3	3
Difenoconazole	75	3	3	76	4	10	80	2	3
Diflubenzuron	74	5	7	73	5	8	86	2	3
Diflufenican	78	3	4	77	5	8	77	3	3
Dimethirimol	72	5	8	79	5	6	79	2	3
Dimethoate	78	3	5	82	3	6	88	3	3
Dimethomorph (E, Z)	83	3	3	90	5	7	91	1	3
Diuron	84	3	4	86	4	7	91	2	3
Edifenphos	83	3	3	81	7	7	85	3	3
Epoxiconazole	65	6	11	71	6	8	84	3	4
Ethion	80	2	3	79	5	8	81	2	3
Ethiprole	85	3	4	86	4	5	87	2	3
Etoxazole	71	5	11	65	11	11	70	4	4
Etrimfos	81	4	5	84	2	7	84	3	4
Fenamidone	84	4	4	86	4	8	89	2	2
Fenamiphos	74	14	16	64	25	25	90	2	3
Fenarimol	74	2	3	73	3	7	71	3	3
Fenbuconazole	73	3	3	74	2	5	84	2	2
Fenobucarb	86	6	8	83	7	8	89	5	5
Fenoxaprop ethyl	77	3	3	77	5	7	80	5	5
Fenoxycarb	81	1	5	85	4	9	91	1	3
Fenpropathrin	61	7	8	36	14	19	60	3	7
Fenpropimorph	88	4	6	80	4	6	25	19	27
Ferimzone	—¹	—¹	—¹	89	4	5	91	2	3
Fipronil	66	5	9	70	7	10	79	2	2
Flamprop methyl	77	2	3	88	4	9	91	3	3
Fludioxonil	79	5	5	85	5	12	88	2	4
Flufenacet	80	3	4	84	3	11	86	3	4
Fluquinconazole	84	4	8	79	3	8	88	3	4
Fluridone	89	4	4	89	4	7	93	2	2
Fluvalinate	44	8	30	49	12	19	22	10	15
Furametpyr	86	3	3	88	4	7	92	2	2
Hexaconazole	—²	—²	—²	—²	—²	—²	—²	—²	—²
Hexaflumuron	77	10	13	83	7	9	80	10	10
Hexythiazox	76	3	4	55	7	14	70	3	4
Imazalil	78	4	4	81	7	9	91	4	4
Imibenconazole	66	3	9	65	8	13	60	10	10
Indanofan	75	4	4	71	4	6	77	6	11
Indoxacarb	84	2	5	85	4	8	85	2	3
Iprovalicarb	84	4	5	87	4	7	92	2	2
Isoprocarb	—²	—²	—²	—²	—²	—²	—²	—²	—²
Isoxathion	86	2	2	80	7	8	89	10	11
Kresoxim methyl	83	3	9	85	8	13	88	6	6
Lactofen	82	2	6	76	6	9	82	2	3
Linuron	85	3	3	86	4	6	90	2	3
Lufenuron	73	6	11	81	5	9	84	6	8
Malathion	78	4	8	87	6	12	92	4	4
Mepanipyrim	84	2	4	80	6	6	85	3	3
Metalaxyl	90	3	3	90	4	7	92	3	3
Methabenzthiazuron	84	4	4	87	6	6	89	3	3
Methidathion	—²	—²	—²	—²	—²	—²	—²	—²	—²
Methiocarb	83	8	8	88	7	10	89	4	4
Metolachlor	85	2	3	81	5	8	89	2	3
Monolinuron	86	5	7	86	6	6	91	3	3
Myclobutanil	85	3	4	87	5	7	89	1	2
Naproanilide	83	3	3	81	5	7	88	2	2
Napropamide	86	4	5	86	4	7	91	2	3
Norflurazon	86	3	4	86	2	6	92	2	2
Novaluron	79	8	8	79	6	10	80	3	3
Oxadixyl	88	7	8	90	4	4	92	2	3
Oxaziclomefone	86	2	6	82	8	10	83	6	6
Paclobutrazol	83	3	3	83	5	8	84	4	4
Penconazole	80	3	3	81	6	11	85	1	2
Pencycuron	84	3	3	83	5	9	86	2	2
Pentoxazone	—²	—²	—²	—²	—²	—²	—²	—²	—²
Phenmedipham	70	3	8	71	4	4	80	11	11
Phenthoate	82	3	9	79	7	8	88	4	4
Phosalone	81	3	4	81	5	9	85	2	4
Phosphamidon	84	4	4	89	4	4	90	2	2
Piperonyl butoxide	84	2	3	80	8	8	77	7	10
Pirimicarb	86	5	5	89	6	6	88	2	2
Pirimiphos methyl	89	2	3	85	6	7	84	3	3
Prochloraz	81	3	3	83	5	9	86	2	2
Profenofos	83	4	5	79	7	10	82	2	3
Prometryn	85	2	3	83	5	6	88	2	2
Propachlor	78	3	4	81	6	6	87	3	3
Propanil	81	5	5	85	5	10	90	3	4
Propaquizafop	83	3	4	82	5	9	83	3	3
Propargite	77	4	9	73	5	7	78	4	4
Propiconazole	81	3	3	82	6	8	86	1	2
Propyzamide	81	4	5	86	4	8	89	4	4
Pyraclofos	85	2	3	85	5	7	88	2	2
Pyraclostrobin	85	4	4	83	6	7	91	1	1
Pyrazophos	87	2	4	85	8	8	84	5	6
Pyriftalid	87	4	4	88	4	6	93	2	2
Pyrimethanil	95	3	4	85	5	5	85	3	3
Pyriproxyfen	79	1	2	71	8	10	71	2	3
Quinalphos	86	3	6	82	8	8	88	1	2
Quinoxyfen	72	2	2	61	9	13	60	3	4
Quizalofop ethyl	—²	—²	—²	—²	—²	—²	—²	—²	—²
Simazine	85	3	4	86	5	5	92	2	2
Simeconazole	79	2	4	82	5	5	84	7	9
Spinosyn A	62	5	12	70	4	8	58	4	4
Spinosyn D	75	2	5	71	4	6	79	3	3
Spiroxamine	83	3	15	74	2	10	73	2	6
Tebuconazole	78	3	3	81	5	8	86	1	2
Tebufenpyrad	80	2	4	73	7	11	72	3	3
Tebuthiuron	79	2	3	86	3	5	88	2	2
Teflubenzuron	74	6	13	70	15	15	72	8	13
Terbutryn	86	3	3	84	5	6	88	2	2
Tetrachlorvinphos	83	1	2	85	4	7	91	2	3
Tetraconazole	79	2	2	81	3	6	89	1	2
Thiacloprid	81	3	4	85	4	4	89	2	2
Tolfenpyrad	79	1	5	80	6	9	79	3	3
Triadimefon	87	5	5	86	4	5	91	2	2
Triadimenol	78	8	8	89	8	12	89	3	4
Triazophos	79	7	14	85	5	11	89	5	7
Tricyclazole	—¹	—¹	—¹	79	4	5	80	1	2
Tridemorph	71	10	16	65	6	23	36	6	16
Trifloxystrobin	87	3	3	85	4	7	87	2	3
Triflumizole	80	5	8	76	5	10	80	5	5
Triflumuron	72	3	3	79	7	8	84	3	3
Triticonazole	79	4	5	83	5	6	88	1	2

¹ Not evaluated due to residue being found in the sample. ² Not evaluated due to low sensitivity.

Table 3. Matrix effects of the target pesticides in brown rice, soybeans, and peanuts.

Compound	Brown Rice	Soybeans	Peanuts
Acetamiprid	0.99	0.88	1.03
Acetochlor	0.89	0.94	1.01
Acibenzolar S-methyl	0.98	1.01	0.98
Acrinathrin	0.70	0.72	0.68
Ametryn	0.98	0.99	1.02
Anilofos	0.94	0.96	1.01
Aramite	0.85	0.91	0.93
Atrazine	1.00	0.97	1.01
Azoxystrobin	0.98	0.98	1.00
Benalaxyl	0.95	0.98	0.99
Bendiocarb	0.92	1.01	1.02
Benzofenap	0.91	0.97	0.98
Bitertanol	0.89	0.93	0.93
Boscalid	0.95	0.97	0.98
Bromacil	0.95	0.94	0.97
Buprofezin	0.96	0.94	0.96
Butafenacil	0.94	0.97	0.99
Cadusafos	0.95	0.92	0.97
Carbaryl	0.99	0.97	1.04
Carpropamid	0.94	0.94	0.98
Chlorfenvinphos (E, Z)	0.92	0.96	0.99
Chloridazon	0.99	0.95	1.02
Chloroxuron	0.91	0.97	1.01
Chlorpyrifos	0.96	0.93	1.02
Chlorpyrifos methyl	1.05	0.95	0.92
Chromafenozide	0.85	0.90	0.90
Clomeprop	0.89	0.91	0.93
Cloquintocet mexyl	0.97	0.96	1.00
Clothianidin	0.98	0.88	1.00
Cumyluron	0.89	0.95	0.99
Cyanazine	0.98	0.96	1.00
Cyazofamid	0.80	0.95	0.97
Cycloprothrin	0.71	0.81	0.89
Cyflufenamid	0.90	0.91	0.97
Cyproconazole	0.87	0.93	0.98
Cyprodinil	0.99	0.98	0.98
Daimuron	0.81	1.05	1.04
Deltamethrin	0.63	0.70	0.53
Diazinon	0.99	0.97	0.99
Difenoconazole	0.86	0.86	0.89
Diflubenzuron	0.86	0.89	0.96
Diflufenican	0.84	0.92	0.90
Dimethirimol	0.99	0.99	1.01
Dimethoate	0.97	0.94	1.01
Dimethomorph (E, Z)	0.96	0.97	0.98
Diuron	0.95	0.96	1.00
Edifenphos	0.94	0.96	1.00
Epoxiconazole	0.69	0.91	0.98
Ethion	0.89	0.93	0.94
Ethiprole	0.96	0.95	0.96
Etoxazole	0.91	0.90	0.97
Etrimfos	1.01	0.94	1.01
Fenamidone	0.95	0.95	0.99
Fenamiphos	0.81	0.82	1.00
Fenarimol	0.85	0.87	0.87
Fenbuconazole	0.80	0.91	0.94
Fenobucarb	0.99	0.93	0.98
Fenoxaprop ethyl	0.89	0.89	0.95
Fenoxycarb	0.91	0.93	0.99
Fenpropathrin	0.71	0.73	0.93
Fenpropimorph	1.08	0.98	0.81
Ferimzone	—¹	1.02	1.00
Fipronil	0.67	0.87	0.95
Flamprop methyl	0.85	0.97	0.99
Fludioxonil	0.84	0.91	0.95
Flufenacet	0.91	0.93	0.93
Fluquinconazole	0.95	0.87	0.92
Fluridone	0.99	0.99	1.01
Fluvalinate	0.53	0.52	0.38
Furametpyr	0.99	0.96	1.00
Hexaconazole	—²	—²	—²
Hexaflumuron	0.84	0.91	0.84
Hexythiazox	0.81	0.77	0.94
Imazalil	0.98	0.98	1.04
Imibenconazole	0.79	0.85	0.86
Indanofan	0.80	0.94	0.97
Indoxacarb	0.91	0.93	0.94
Iprovalicarb	0.92	0.95	0.98
Isoprocarb	—²	—²	—²
Isoxathion	0.92	0.93	0.94
Kresoxim methyl	0.90	0.91	0.95
Lactofen	0.86	0.91	0.96
Linuron	0.94	0.95	0.99
Lufenuron	0.81	0.87	0.97
Malathion	0.88	0.96	1.00
Mepanipyrim	0.96	0.97	0.99
Metalaxyl	1.01	0.97	1.00
Methabenzthiazuron	0.98	0.98	0.99
Methidathion	—²	—²	—²
Methiocarb	0.94	0.96	0.97
Metolachlor	0.92	0.95	0.98
Monolinuron	1.02	1.01	0.99
Myclobutanil	0.94	0.95	0.97
Naproanilide	0.90	0.93	0.97
Napropamide	0.96	0.99	0.99
Norflurazon	0.98	0.96	1.00
Novaluron	0.82	0.92	0.88
Oxadixyl	1.00	1.01	1.00
Oxaziclomefone	0.89	0.91	0.96
Paclobutrazol	0.93	0.94	0.98
Penconazole	0.91	0.90	0.95
Pencycuron	0.96	0.96	0.95
Pentoxazone	—²	—²	—²
Phenmedipham	0.97	0.95	1.09
Phenthoate	0.93	0.96	1.00
Phosalone	0.88	0.93	0.96
Phosphamidon	1.00	1.00	1.01
Piperonyl butoxide	0.90	0.95	0.95
Pirimicarb	1.02	1.00	1.00
Pirimiphos methyl	0.99	0.99	0.99
Prochloraz	0.86	0.92	0.97
Profenofos	0.92	0.94	0.96
Prometryn	0.99	0.98	1.00
Propachlor	1.02	0.96	0.99
Propanil	0.92	0.95	0.99
Propaquizafop	0.91	0.96	0.99
Propargite	0.81	0.87	0.94
Propiconazole	0.90	0.94	0.96
Propyzamide	0.94	0.97	0.99
Pyraclofos	0.95	0.97	0.97
Pyraclostrobin	0.97	0.98	0.98
Pyrazophos	0.96	0.96	0.93
Pyriftalid	0.99	0.98	1.00
Pyrimethanil	1.02	0.99	1.00
Pyriproxyfen	0.88	0.91	0.94
Quinalphos	0.93	0.93	0.99
Quinoxyfen	0.83	0.80	0.89
Quizalofop ethyl	—²	—²	—²
Simazine	0.98	0.97	1.04
Simeconazole	0.87	0.93	0.98
Spinosyn A	0.72	0.83	0.63
Spinosyn D	0.90	0.90	0.92
Spiroxamine	0.97	0.96	0.97
Tebuconazole	0.88	0.93	0.96
Tebufenpyrad	0.88	0.94	0.95
Tebuthiuron	0.99	1.00	1.00
Teflubenzuron	0.77	0.91	0.90
Terbutryn	0.99	0.97	1.01
Tetrachlorvinphos	0.92	0.96	0.99
Tetraconazole	0.86	0.91	0.96
Thiacloprid	0.98	0.94	1.00
Tolfenpyrad	0.87	0.94	0.98
Triadimefon	0.92	0.98	0.98
Triadimenol	0.87	0.92	0.93
Triazophos	0.90	0.96	0.93
Tricyclazole	—¹	0.99	1.02
Tridemorph	1.05	1.03	0.98
Trifloxystrobin	0.96	0.96	0.96
Triflumizole	0.85	0.90	0.90
Triflumuron	0.80	0.90	0.98
Triticonazole	0.88	0.92	0.97

¹ Not evaluated due to residue being found in the sample. ² Not evaluated due to low sensitivity.

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Saito-Shida, S.; Nemoto, S.; Akiyama, H. Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry. Foods 2021, 10, 78. https://doi.org/10.3390/foods10010078

AMA Style

Saito-Shida S, Nemoto S, Akiyama H. Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry. Foods. 2021; 10(1):78. https://doi.org/10.3390/foods10010078

Chicago/Turabian Style

Saito-Shida, Shizuka, Satoru Nemoto, and Hiroshi Akiyama. 2021. "Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry" Foods 10, no. 1: 78. https://doi.org/10.3390/foods10010078

APA Style

Saito-Shida, S., Nemoto, S., & Akiyama, H. (2021). Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry. Foods, 10(1), 78. https://doi.org/10.3390/foods10010078

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Quantitative and Confirmatory Analysis of Pesticide Residues in Cereal Grains and Legumes by Liquid Chromatography–Quadrupole-Time-of-Flight Mass Spectrometry

Abstract

1. Introduction

2. Materials and Methods

2.1. Reagents and Chemicals

2.2. Materials

2.3. Apparatus

2.4. Sample Preparation

2.5. Method Validation

3. Results and Discussion

3.1. Optimization of LC–QTOF-MS Conditions

3.2. Method Validation

3.3. Confirmation

4. Conclusions

Supplementary Materials

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

References

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