Next Article in Journal
The Genus Capsicum: A Review of Bioactive Properties of Its Polyphenolic and Capsaicinoid Composition
Previous Article in Journal
A Quick Method to Synthesize Extrachromosomal Circular DNA In Vitro
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 28
Issue 10
10.3390/molecules28104235
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Highly-Selective Analytical Strategy for 90 Pesticides and Metabolites Residues in Fish and Shrimp Samples

by
Yage Guo
1,
Jun Xie
1,
Fengshou Dong
1,
Xiaohu Wu
1,
Xinglu Pan
1,
Xingang Liu
1,
Yongquan Zheng
1,
Jie Zhang
2,* and
Jun Xu
1,*
1
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
2
Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
*
Authors to whom correspondence should be addressed.
Molecules 2023, 28(10), 4235; https://doi.org/10.3390/molecules28104235
Submission received: 26 April 2023 / Revised: 14 May 2023 / Accepted: 16 May 2023 / Published: 22 May 2023
(This article belongs to the Section Analytical Chemistry)
Browse Figures
Versions Notes

Abstract

:
The analysis of pesticide residues in aquatic products is challenging due to low residue levels and the complex matrix interference. In this study, we developed a simple, fast method for the trace analysis of 90 pesticides and metabolites in aquatic products. The analytes covered a wide polarity range with log Kow (log octanol-water partition coefficient) ranging from −1.2 to 6.37. Grass carp (Ctenopharyngodon idellus) and prawn (Penaeus chinensis) samples were chosen to validate the quantification method. The samples were extracted by 0.2% formic-acetonitrile, cleaned by solid-phase extraction (PRiME HLB), and analyzed by high performance liquid chromatography−tandem mass spectrometry. The results showed good linearities for the analytes and were observed in the range of 0.05–50 μg/L. The recoveries of the method were within 50.4–118.6%, with the relative standard deviations being lower than 20%. The limits of quantifications (LOQs) of the method were in the range of 0.05–5.0 μg/kg, which were superior to values compared with other research. The developed method was applied to detect pesticide residues in prawn samples from eastern coastal areas of China. Three herbicide residues of diuron, prometryn, and atrazine were detected in prawn samples. The method was sensitive and efficient, which is of significance in expanding the screening scope and improving the quantitative analysis efficiency in aquatic products.

Graphical Abstract

1. Introduction

Pesticides play a key role in enhancing crop production and controlling pests. However, the extensive use of pesticides in modern agriculture has resulted in agricultural nonpoint source pollution causing risk hazards for water [1,2]. Pesticides can enter the aquatic environment through various ways, such as drift, irrigation, wash-off, drainage, and container and equipment cleaning practices [3]. These pesticides in the aquatic ecosystems may cause adverse effects on water pollution and the diversity of aquatic biota. More importantly, these pesticides in water could bioaccumulate in non-target species and even amplify at higher concentrations along the food chain; ultimately, they could cause risks to humans [1,4].
In aquatic products, fish and shrimp, the main providers of high-quality protein, polyunsaturated fatty acids, and vitamins and minerals, are an important part of a healthy diet [5,6]. Nonetheless, they can also be exposed to pesticides. For example, fish species are at a higher nutritional level in the aquatic food chain and easily bioaccumulate pesticides [3], and shrimp are considered benthic organisms that easily accumulate pesticides from sediments [7,8,9]. Therefore, it is very vital to develop analytical methods to detect pesticide residues in aquatic products for health reasons or ecological reasons [10].
In the past, and presently, the top concerns were relevant to the analytical methods of persistent organic pollutants because of their specific semi-volatile, persistent, and bio-accumulative characteristics [11,12,13,14,15,16,17,18]. As for the currently used pesticides, a comparison of the method with other methods in terms of analyte species, analysis time, sample pretreatment method, and LOQs is shown in Table 1. Previous research has focused on a limited number of pesticides [19,20,21,22] or has been restricted to a certain class of pesticides, such as pyrethroids pesticides [23], organofluorine pesticides [24], and triazine herbicides [25], which is a disadvantage for the large-scale screening of pesticide residues in aquatic products. In this study, 90 pesticides and metabolites were selected, including 45 insecticides, 18 fungicides, 23 herbicides, 2 plant growth regulators, and 2 acaricides. More importantly, metabolites with toxicity were chosen and analyzed. For example, phorate (the definition of the residue for compliance with the MRL and for dietary risk assessment for plant and animal commodity) content is defined as the sum of phorate and its oxygen analogue, and their sulfoxides and sulfones, expressed as phorate [19]. Likewise, the total residual amounts of fipronil include the concentration of fipronil, fipronil-sufone, fipronil-sufoxide, and fipronil-sufoxide [19]. Furthermore, fipronil-sulfone was found to be 3.3 times more toxic to bluegill sunfish and 6.6 times more toxic to freshwater invertebrates than fipronil [20]. The degradation products of atrazine are mainly desethylatrazine and hydroxyatrazine, which are 2–10 times more hazardous than the parent atrazine [21].
In the meantime, most of the analysis time was longer than 20 min. In the study, analysis time was reduced to 17 min for 90 pesticides and metabolites, which greatly improved work efficiency. Aquatic products are relatively complex matrices with a high protein and lipid content. The protein and fat can be extracted by organic solvent and this produces a strong matrix effect (ME) affecting the test results [4]. The methods of fat removal mainly include liquid–liquid extraction (LLE), gel permeation chromatography (GPC), solid-phase extraction (SPE), and dispersive solid-phase extraction (d-SPE) [22]. Among these methods, Maia et al. employed LLE for purification of the samples [8], while Chang et al. employed GPC for the purification of the samples [9]. Although these methods were effective in removing fat, they were time-consuming and inefficient. Most of the reported methods in the purification of aquatic products have focused on the d-SPE method with primary secondary amine (PSA) and octadecylsilane (C18) adsorbents. Although it had some advantages in terms of improving efficiency, it was still not effective in eliminating fat. Therefore, the LOQs of the methods were mainly above 1.0 μg/kg. Some studies have also optimized new absorbents (Z-Sep, mZrO2@Fe3O and so on) instead of PSA and C18. However, these adsorbents were not available. This study employed PRiME HLB for the purification of the samples, which had significant advantages in removing fat and showed lower LOQs in comparison to previous methods. The LOQs of the method were as low as 0.05 μg/kg, which can be used to monitor pesticide residues in aquatic products at trace levels.
In this study, an analysis method concerning multi-residue in fish and shrimp was developed. These pesticides were developed for 77 pesticides and 13 metabolites. The different extraction and purification methods were optimized to obtain precise and accurate results. The developed method was validated according to the SANTE guidelines by the European Union, in terms of linearity, recovery, relative standard deviation (RSD), LOQ, and ME [23]. The optimized method was successfully applied to analyze prawn samples, which provided a basis for detecting multi–pesticide residues in aquatic products at trace levels.
Table
Table 1. Comparison of this method to other sample methods for analysis of pesticides and metabolites in aquatic products.
Table 1. Comparison of this method to other sample methods for analysis of pesticides and metabolites in aquatic products.
CategoryCompoundAquatic BiotaAnalysis TimeExtractionCleanupInstrumentLOQ
(µg/kg)		References
42
7		pesticides
PCBs		fish38 ACNMSPE: mZrO2@Fe3O4)GC-MS/MS0.02–4.40 *[14]
18
37
13		pesticides
PCB/PAH/PDBE
flame retardants		catfish9.025ACNdSPE: Z-SepGC-MS/MS0.5–5.0[16]
15pyrethroidstilapia, tainha25 ACNdSPE:C18, PSA and
Z-Sep 		GC-MS5.0 or 10.0[24]
6organofluorinegrass carp,
white shrimp,
European eel		14.5ACNSPE: NH2LC-MS/MS0.3–0.5[25]
14triazinebivalves20.54% acidic
ACN		d-SPE:MIPsGC-MS/MS0.1–1.59[26]
32pesticidesflatfish, eel, oyster, and shrimp271% FA/ACNd-SPE: PSA, C18 LC-HRMS0.5–5.0[27]
52pesticidesfish25 ACNd-SPE: PSA, C18 LC-MS/MS0.04–49.8[28]
13pesticidesroach, perch and
carp		17 ACNHexane LLELC-MS/MS1.0–7.4[29]
33pesticidesCyprinus carpio20 Ethylacetate/ACN(1:1)GPCLC-MS/MS0.003–0.247[30]
22pesticides and
metabolites		freshwater fish
marine fish		8 1%acetic acid/ACN.SPE+dSPELC-MS/MS0.5–5.0[22]
20pesticidesEel and shrimp30 ACNd-SPE: PSA, C18 LC-MS/MS<5.0[31]
67pesticidesfish331% acidic ACNd-SPE: PSA, C18LC-MC/MC1.0–15.0[32]
90pesticides and
metabolites		fish, shrimp17 0.2% FA/ACNSPE: PRiME HLBLC-MS/MS0.05–5.0this method
* refer to LOD.

2. Results and Discussion

2.1. Optimization of the LC–MS/MS

For the analysis of 90 pesticides and metabolites, a standard solution of each analyte (10 μg/L) was prepared and directly injected into the autosampler to obtain the optimal mass spectrum conditions. The MS/MS conditions of each analyte were optimized through the following pattern: MS2 SCAN mode, SIM mode, Product ion mode, and MRM mode. Among the analytes, 83 analytes were detected using the ESI positive ion mode and 7 analytes were detected using the ESI negative ion mode. The multiple reaction monitoring (MRM) transitions, retention time, fragmentor, and collision energy of each analyte are summarized in Table S1. For the mobile phase, water was chosen as the aqueous phase because of the presence of the ESI negative ion mode. The MRM chromatograms of 90 pesticides and metabolites were shown in Figure 1.
The dynamic multiple reaction monitoring (dMRM) data acquisition mode can yield a higher response, symmetrical peak shape because the mass spectrometer scans only certain channels within a specified time window so that more scans can be obtained [33,34]. In this study, the MRM mode and dMRM mode were compared. As shown in Figure S1, the dMRM mode indicated a 3- to 10-fold higher response than the MRM mode. Therefore, the dMRM mode was used to achieve a high sensitivity and superior peak shape.

2.2. Optimization of Sample Preprocessing

2.2.1. Extracting Procedure

The choice of extraction solvents was significant in obtaining a satisfactory recovery. In the residue analysis, acetonitrile (ACN), ethyl acetate, and acetone were the common extraction solvents. However, ACN was chosen as the preferred solvent because it extracted fewer coextracted matrix components [22]. Meanwhile, it was also the major component of the mobile phase and could be directly injected into the column without evaporation and reconstitution [35]. In this study, the extraction efficiencies were examined using pure ACN, 0.2% formic acid/ACN (FA/ACN), ACN/water (8:2), and ACN/water (8:2) with 0.2% FA. ACN/water (8:2) was selected because it had been used to analyze pesticide residues in fishery products [10]. The results of different extraction solvents are shown in Figure 2a. The recoveries of 90 pesticides and metabolites using ACN/water (8:2) and ACN/water (8:2) with 0.2% FA were not satisfactory, and the recoveries of almost 26% and 27% of analytes were less than 60%. The recoveries of two analytes (methamidophos and hydroxyatrazine) using ACN were less than 60%. The addition of formic acid in ACN improved the recovery of methamidophos (74%) and hydroxyatrazine (64%). The RSDs of ACN and 0.2% FA/ACN as extraction solvents are compared in Figure 2b. Furthermore, smaller RSDs were observed when 0.2% FA/ACN was used as the extracting solvent. Thus, 0.2% FA/ACN was selected as the optimal extraction solvent for the target analytes.

2.2.2. Clean-Up Conditions

Aquatic samples were rich in fat components, and they had an adverse impact on quantitative results. In this study, different types of SPE were tested for recovery. In the initial experiment, SPE cartridges (the Oasis HLB cartridge and Cleanert PEP cartridge), which belonged to hydrophilic–lipophilic balanced copolymer, were selected for the purification of the 90 analytes [4]. It was previously reported that the efficiency of SPE was determined by the variety of sorbents, type of the loading solvents and elution solvents, etc. [29,36]. Therefore, different varieties of combinations (including the variety of sorbents, type of the loading solvents and the elution solvents) were designed to optimize several relevant conditions affecting the clean-up efficiency. As shown in Figure 3a, with the PEP SPE clean-up (S1, S2, S3, and S4 treatment), 30%, 51%, 52%, and 70% of the analytes were in the recovery of 70–120%, respectively. With the HLB SPE cleanup (S5, S6, S7, and S8 treatment), 57%, 49%, 64%, and 47% of the analytes were in the recovery of 70–120%, respectively. The recoveries of the target compounds that could not meet the recovery requirements under different types of clean-up conditions were displayed in Figure 3b. The results may be due to the fact that the target analytes have broad physical and chemical properties (log Kow: −1.2–6.37), therefore, very hydrophobic analytes were too strongly retained to be eluted with ACN or methanol (MEA) (Figure 3b). In the case of PRiME HLB, the recoveries of 85% of the analytes were in the range of 70–120%, and the recoveries of 14% of the analytes were 60–70%. Thus, we preliminarily adopted PRiME HLB SPE for purification based on the consideration of recoveries.
In this next experiment, different volumes of eluent were optimized including 1 mL, 2 mL, 3 mL, and 4 mL. The results showed that 85% of the analytes were in the recovery of 70–120% with only 3 mL eluent volumes (Figure 3c). Therefore, 3.0 mL was chosen as the elution volume for PRiME HLB in order to save organic solvents.
Conventional SPE procedures consist of four steps: pre-equilibrating, loading, rinsing, and eluting steps for purification of the sample. The passthrough PRiME HLB column consists of two steps: loading the extraction solution onto the cartridges, and then collecting the eluates. Therefore, this method avoids the tedious steps and reduces solvent consumption and saving in comparison with traditional SPE [22].

2.3. Validation of the Proposed Method

The validation results of the analytical method in terms of linearity, sensitivity, precision, and accuracy are presented in Table 2 and Table S2. Good linearities (R2 > 0.9902) were observed in solvent- and matrix-matched calibration curves. The LOQs of all the analytes were 0.05–5.0 μg/kg. Our results showed that the selectivity and linearity were favorable for the residue analysis at all target concentrations of the pesticides.
All analytes were pre–spiked into samples at the concentration levels of 0.05, 0.5, 5.0, and 50.0 μg/kg (n = 5). The average recovery values ranged from 50.4% to 117.4%, with the intra-RSDs being less than 19.8% in grass carp samples. The average recovery values ranged from 60.5% to 118.6%, with intra-RSDs being less than 19.5% in prawn samples. The inter-RSD of the method ranged from 0.5% to 19.1% in grass carp, and 0.3% to 18.5% in prawn, respectively. Our results showed that the recoveries and RSDs can meet the requirements of residue analysis. The matrix effects were assessed by comparing the slope of the matrix standard curve to the solvent standard calibration curve. The ME ranged from 9% to 115% in grass carp, and ranged from 10% to 166% in prawn. The reason for the difference in matrix effect may be related to the physicochemical properties of analytes and the texted matrix, which was observed in other studies [37,38,39]. To eliminate the effect of matrix effects, the matrix-matched calibration curve was selected for the quantitative analysis. Therefore, the developed method can meet the requirements of pesticide residues in grass carp and prawn samples.

2.4. Real Samples Analysis and Health Risk Assessment

Of the pesticides studied, three pesticides were detected at values greater than the LOQ. The concentration of dinuron from Tianjin was detected among all samples, with 0.92 μg/kg maximum concentration. Prometryn were detected in all samples from Dalian, and the concentration ranged from 0.05 to 0.06 μg/kg. The concentration of atrazine ranged from LOQ to 0.05 μg/kg in Tianjin, from LOQ to 0.58 μg/kg in Dalian, and from LOQ to 0.28 μg/kg in Qingdao. In the study, dinuron (log Know = 2.87, BCF = 9.7), prometryn (log Know = 3.34, BCF = 85), and atrazine (log Know = 2.7, BCF = 4.3) were detected in samples, probably because of its high log Kow and BCF values. Similarly, dinuron were detected in rice–crayfish systems in Jiangsu Province, with the highest value being 11.5 μg/kg [40]. Prometryn also were detected in the shellfish samples and the concentration ranged from 1.0 μg/kg to 33.6 μg/kg in coastal areas in China [26]. The concentrations of atrazine in aquatic organisms in the Xiangshan Harbor ranged from 2.37 to 39.2 μg/kg (dw), with an average concentration of 13.2 ± 11.8 μg/kg [41].
The risk assessment of detected pesticides in prawn samples was shown in Table S3. The acute and chronic risk quotients were calculated, ranging from 0.0029% to 0.0288%, and 0.0002% to 0.0289%, respectively. This indicated that there was not a significant risk to human health.

3. Materials and Methods

3.1. Reagents and Chemicals

High purity (>98%) standards of all 90 pesticides and metabolites were purchased from China Standard Material Center (BeiJing, China), Dr. Ehrenstorfer (Augsburg, Germany), Sigma–Aldrich (St. Louis, MO, USA), Fluka (Buchs, Switzerland), AccuStandard (New Haven, CT, USA), and Wako Pure Chemical Industries Inc. (Osaka, Japan). High performance liquid chromatography (HPLC) grade ACN and FA were supplied by Sigma–Aldrich (Steinheim, Germany). Analytical grade ACN, MEA, sodium chloride (NaCl), and anhydrous magnesium sulfate (MgSO4) were obtained from Beihua Fine chemicals Co. (BeiJing, China). Ultra-pure water was obtained from a Milli-Q system (Bedford, MA, USA). SPE column, Oasis HLB was purchased from Waters Corporation (Waters Corp., Milford, MA), and Cleanert PEP, PRiME HLB were obtained from Agela Technologies Inc. (Agela, Tianjin, China). Ordinary nitrogen (N2) was purchased from Haike Yuanchang Gas (Beijing, China). The Poroshell 120 PFP chromatographic column was purchased from Agilent Technologies (Santa Clara, CA, USA).

3.2. Sample Preparation

Grass carp and prawn samples were selected to validate the method. In addition, prawn samples collected from eastern coastal areas of China were used for the real sample analysis. The areas were mainly distributed in Dalian (Liaoning Province), Tianjin, Yantai (Shandong Province), and Qingdao (Shandong Province). A total of 32 prawn samples comprising 8 of each area were used for this work. The prawn meat was homogenized, and stored at −20 °C for the next analysis.

3.3. Extraction and Cleanup

An amount of 2.0 g of each sample was weighed into a 10 mL centrifuge tube and extracted with 4 mL of 0.2% FA/ACN solution. The samples were vortexed on the multi-functional vortex mixer at 2500 rpm for 10 min. Subsequently, 0.4 g of MgSO4 and 0.1 g of NaCl were added, after which the samples were vortexed for 5 min and centrifuged at 8000 rpm for 5 min. An amount of 2 mL of supernatant was prepared for purification.
Method 1: The supernatant (2.0 mL) was evaporated to dryness at 30 °C and was redissolved in 2 mL loading solution for subsequent clean-up. Before sample loading, Oasis HLB and Cleanert PEP cartridges were preequilibrated with 3 mL of methanol and 3 mL of deionized water. Then, the redissolved extracts were loaded into a SPE cartridge. The cartridge was eluted with different solvents and then the eluent was evaporated to dryness by nitrogen at 30 °C. The extract was adjusted to 1 mL with ACN. After filtration with 0.22 μm nylon syringe filters, the final extracts were transferred into an autosampler vial at −20 °C until analysis.
Method 2: The supernatant (2.0 mL) was passed through a PRiME HLB column and there was no activation requirement for PRiME HLB cartridges. Then, the column was eluted by 3.0 mL of ACN. All of the solutions were collected and evaporated to dryness by nitrogen at 30 °C. The extract was adjusted to 1 mL with ACN. After filtration with 0.22 μm nylon syringe filters, the final extracts were transferred into an autosampler vial at −20 °C until analysis.

3.4. LC-MS/MS Analysis

The analysis of 90 pesticides and metabolites was performed on an Agilent 1290 UPLC system coupled to a G6470A triple quadrupole mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Separation was achieved with a Poroshell PFP column (2.1 × 100 mm, 2.7 µm particle size, Agilent) under 30 °C column temperature. The mobile phase was made up of water and ACN. The gradient of mobile phase is shown in Table S4. The injection volume was 5.0 μL, and the flow in the analysis procedure was 0.3 mL/min.
MS/MS acquisition was carried out by dMRM mode for 90 pesticides and metabolites and the cycle time was 500 ms. The instrument allows for the simultaneous detection of positive and negative ionization modes without affecting method sensitivity, and 90 pesticides and metabolites were analyzed in a single needle injection and the whole analysis time was 17 min (83 analytes in the positive mode and 7 analytes in the negative mode). Other MS detection conditions were set as follows: sheath gas temperature, 350 °C; sheath gas flow, 11 L/min; drying gas temperature, 350 °C; drying gas flow, 10 L/min; nebulizer, 45 psi; and nozzle voltage, 500 V.

3.5. Method Validation

The optimized method was verified to evaluate the applicability of the method according to SANTE guidelines [23]. These parameters included linearity, recovery, RSD, LOQ, and ME. The solvent- and matrix-matched calibration curves were set at concentration levels of 0.05, 0.1, 0.5, 1, 2, 5, 10, 20, and 50 μg/L. Good linearity was observed when the correction coefficient (R2) of the analyte was greater than 0.99. Four different spiking concentrations on a single day (intra-day) and over three consecutive days (inter-day) were performed to verify the accuracy and precision of the method. The LOQ was the lowest spiked level of the validation that meets the method criteria for recovery and precision. ME was confirmed by comparing the slope of the matrix standard curve to the solvent standard calibration curve.

3.6. Risk Assessment

The acute and chronic risk assessment of consuming these prawn samples was investigated. The results were calculated according to the formula [42]:
IESTI = (HR × LP)/bw
%HQa = IESTI/ARfD × 100
where, IESTI is the estimate of short term intake (mg/kg bw), HR indicates the highest concentration (mg/kg), LP means large portion (g/d), and the large portion of prawn is 300 g/d for the general population [43]. bw is the average weight for the general population (60 kg) [42], %HQa is the acute risk quotient, and ARfD is the acute reference dose (mg/kg bw).
EDI = (STMP × F)/bw
%HQC = EDI/ADI × 100
Here, EDI is the estimated daily intake (mg/kg bw), STMR indicates the median concentration (mg/kg), F means the average consumption of prawn (g/d), and the average consumption of prawn is 100 g/d for the general population [43]. bw is the average weight for the general population (60 kg) [42], %HQc is the chronic risk quotient, and ADI is the acceptable daily intake (mg/kg bw).

4. Conclusions

In this study, a strategy based on the purification of the SPE method combined with HPLC-MS/MS for the analysis of 90 pesticides and metabolites in fish and shrimp was proposed. The developed method, with good linearity (>0.9902), specificity, trueness (50.4–118.6%), precision (<19.8%), and sensitivity (0.05–5.0 μg/kg), could meet the requirements for the simultaneous analysis of 90 pesticides and metabolites. The method had the advantages of simple operation and high sensitivity. The results of this study may be helpful for understanding the residue levels in aquatic biota, and the residues in aquatic biota would help us perform risk assessments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules28104235/s1, Figure S1: Total ion response through MRM mode and dynamic MRM mode (red: MRM; blue, dMRM mode); Table S1: Experimental parameters and HPLC–MS/MS conditions of 90 pesticides and metabolites in ESI; Table S2: Inter RSDs and matrix effect of 90 pesticides and metabolites in grass carp and prawn samples; Table S3: Acute and chronic risk assessment of detected pesticides in prawn samples; Table S4: The gradient of mobile phase for the instrumental analysis by HPLC-MS/MS.

Author Contributions

Conceptualization, Y.G.; funding acquisition, F.D. and J.X. (Jun Xu); methodology, Y.G.; project administration, X.W., X.P. and X.L.; resources, J.Z.; supervision, F.D., X.W., X.P., X.L., Y.Z., J.X. (Jun Xu) and J.Z.; validation, J.X. (Jun Xie); writing—original draft, Y.G.; writing—review and editing, J.X. (Jun Xie). All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Nature Science Foundation of China (NSFC32072467).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Sample Availability

Samples of the compounds are available from the authors.

References

  1. Tang, F.H.M.; Lenzen, M.; McBratney, A.; Maggi, F. Risk of pesticide pollution at the global scale. Nat. Geosci. 2021, 14, 206–210. [Google Scholar] [CrossRef]
  2. Kalyabina, V.P.; Esimbekova, E.N.; Kopylova, K.V.; Kratasyuk, V.A. Pesticides: Formulants, distribution pathways and effects on human health—a review. Toxicol. Rep. 2021, 8, 1179–1192. [Google Scholar] [CrossRef] [PubMed]
  3. Stanley, J.; Preetha, G. Pesticide Toxicity to Fishes: Exposure, Toxicity and Risk Assessment Methodologies. In Pesticide Toxicity to Non-Target Organisms; Springer: Dordrecht, The Netherlands, 2016; pp. 411–497. [Google Scholar] [CrossRef]
  4. Álvarez-Ruiz, R.; Picó, Y. Analysis of emerging and related pollutants in aquatic biota. Trends Environ. Anal. Chem. 2020, 25, e00082. [Google Scholar] [CrossRef]
  5. Mohanty, B.P.; Mahanty, A.; Ganguly, S.; Mitra, T.; Karunakaran, D.; Anandan, R. Nutritional composition of food fishes and their importance in providing food and nutritional security. Food Chem. 2019, 293, 561–570. [Google Scholar] [CrossRef]
  6. Li, N.; Wu, X.; Zhuang, W.; Xia, L.; Chen, Y.; Wu, C.; Rao, Z.; Du, L.; Zhao, R.; Yi, M.; et al. Fish consumption and multiple health outcomes: Umbrella review. Trends Food Sci. Technol. 2020, 99, 273–283. [Google Scholar] [CrossRef]
  7. Arisekar, U.; Shakila, R.J.; Shalini, R.; Jeyasekaran, G.; Arumugam, N.; Almansour, A.I.; Keerthana, M.; Perumal, K. Bioaccumulation of organochlorine pesticide residues (OCPs) at different growth stages of pacific white leg shrimp (Penaeus vannamei): First report on ecotoxicological and human health risk assessment. Chemosphere 2022, 308, 136459. [Google Scholar] [CrossRef]
  8. Maia, M.L.; Sousa, S.; Correia-Sa, M.L.; Delerue-Matos, C.; Calhau, C.; Domingues, V.F. Organochlorine pesticides, brominated flame retardants, synthetic musks and polycyclic aromatic hydrocarbons in shrimps. An overview of occurrence and its implication on human exposure. Heliyon 2020, 6, e04870. [Google Scholar] [CrossRef]
  9. Chang, H.Y.; Yang, W.C.; Xue, Y.J.; Tsai, M.Y.; Wang, J.H.; Chang, G.R. Phthalates and organophosphorus insecticide residues in shrimp determined by liquid/gas chromatography-Tandem mass spectrometry and a health risk assessment. Mar. Pollut. Bull. 2019, 144, 140–145. [Google Scholar] [CrossRef]
  10. Núñez, M.; Borrull, F.; Pocurull, E.; Fontanals, N. Sample treatment for the determination of emerging organic contaminants in aquatic organisms. Trends Anal. Chem. 2017, 97, 136–145. [Google Scholar] [CrossRef]
  11. Zhao, X.; Cui, T.; Guo, R.; Liu, Y.; Wang, X.; An, Y.X.; Qiao, X.; Zheng, B. A clean-up method for determination of multi-classes of persistent organic pollutants in sediment and biota samples with an aliquot sample. Anal. Chim. Acta 2019, 1047, 71–80. [Google Scholar] [CrossRef]
  12. Molbert, N.; Alliot, F.; Santos, R.; Chevreuil, M.; Mouchel, J.M.; Goutte, A. Multiresidue Methods for the Determination of Organic Micropollutants and Their Metabolites in Fish Matrices. Environ. Toxicol. Chem. 2019, 38, 1866–1878. [Google Scholar] [CrossRef] [PubMed]
  13. Azzouz, A.; Colon, L.P.; Souhail, B.; Ballesteros, E. A multi-residue method for GC-MS determination of selected endocrine disrupting chemicals in fish and seafood from European and North African markets. Environ. Res. 2019, 178, 108727. [Google Scholar] [CrossRef] [PubMed]
  14. Peng, X.T.; Jiang, L.; Gong, Y.; Hu, X.Z.; Peng, L.J.; Feng, Y.Q. Preparation of mesoporous ZrO2-coated magnetic microsphere and its application in the multi-residue analysis of pesticides and PCBs in fish by GC-MS/MS. Talanta 2015, 132, 118–125. [Google Scholar] [CrossRef]
  15. Molinaruiz, J.M.; Cieslik, E.; Cieslik, I.; Walkowska, I. Determination of pesticide residues in fish tissues by modified QuEChERS method and dual-d-SPE clean-up coupled to gas chromatography-mass spectrometry. Environ. Sci. Pollut. Res. 2015, 22, 369–378. [Google Scholar] [CrossRef]
  16. Sapozhnikova, Y.; Lehotay, S.J. Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants in fish using fast, low-pressure gas chromatography-tandem mass spectrometry. Anal. Chim. Acta 2013, 758, 80–92. [Google Scholar] [CrossRef]
  17. Nardelli, V.; dell’Oro, D.; Palermo, C.; Centonze, D. Multi-residue method for the determination of organochlorine pesticides in fish feed based on a cleanup approach followed by gas chromatography-triple quadrupole tandem mass spectrometry. J. Chromatogr. A 2010, 1217, 4996–5003. [Google Scholar] [CrossRef] [PubMed]
  18. Chen, S.; Shi, L.; Shan, Z.; Hu, Q. Determination of organochlorine pesticide residues in rice and human and fish fat by simplified two-dimensional gas chromatography. Food Chem. 2007, 104, 1315–1319. [Google Scholar] [CrossRef]
  19. FAO/WHO. Joint FAO/WHO Food Standards Programme Codex Committee on Pesticide Residues 52nd Session Maximum Residue Limits for Pesticides in Foods and Feeds; FAO: Rome, Italy, 2021; pp. 1–386.
  20. Li, M.; Li, P.; Wang, L.; Feng, M.; Han, L. Determination and dissipation of fipronil and its metabolites in peanut and soil. J. Agric. Food Chem. 2015, 63, 4435–4443. [Google Scholar] [CrossRef]
  21. Bhatti, P.; Duhan, A.; Pal, A.; Monika; Beniwal, R.K.; Kumawat, P.; Yadav, D.B. Ultimate fate and possible ecological risks associated with atrazine and its principal metabolites (DIA and DEA) in soil and water environment. Ecotoxicol. Environ. Saf. 2022, 248, 114299. [Google Scholar] [CrossRef]
  22. Guo, Y.; Zhang, J.; Xu, J.; Wu, X.; Dong, F.; Liu, X.; Zheng, Y. An Integrated Strategy for Purification by Combining Solid-Phase Extraction with Dispersive-Solid-Phase Extraction for Detecting 22 Pesticides and Metabolite Residues in Fish. J. Agric. Food Chem. 2021, 69, 7199–7208. [Google Scholar] [CrossRef]
  23. EU (Eupropean Commission). Guidance Document on Analytical Quality Control and Method Validation Procedures for Pesticide Residues and Analysis in Food and Feed (SANTE/11813/2017); EU: Brussels, Belgium, 2017. [Google Scholar]
  24. De Oliveira, L.G.; Kurz, M.H.S.; Guimaraes, M.C.M.; Martins, M.L.; Prestes, O.D.; Zanella, R.; Ribeiro, J.; Goncalves, F.F. Development and validation of a method for the analysis of pyrethroid residues in fish using GC-MS. Food Chem. 2019, 297, 124944. [Google Scholar] [CrossRef] [PubMed]
  25. Gan, J.; Lv, L.; Peng, J.; Li, J.; Xiong, Z.; Chen, D.; He, L. Multi-residue method for the determination of organofluorine pesticides in fish tissue by liquid chromatography triple quadrupole tandem mass spectrometry. Food Chem. 2016, 207, 195–204. [Google Scholar] [CrossRef] [PubMed]
  26. Yu, X.; Zhang, R.; Liu, H.; Zhang, Z.; Shi, X.; Sun, A.; Chen, J. Highly-selective complex matrices removal via a modified QuEChERS for determination of triazine herbicide residues and risk assessment in bivalves. Food Chem. 2021, 347, 129030. [Google Scholar] [CrossRef] [PubMed]
  27. Kim, K.; Choi, Y.; Mok, S.; Moon, H.B.; Jeon, J. Optimization of the QuEChERS method for multi-residue analysis of pharmaceuticals and pesticides in aquaculture products. Food Chem. 2023, 399, 133958. [Google Scholar] [CrossRef] [PubMed]
  28. Barbieri, M.V.; Postigo, C.; Guillemargiles, N.; Monlloralcaraz, L.S.; Simionato, J.I.; Stella, E.; Barcelo, D.; Lopez de Alda, M. Analysis of 52 pesticides in fresh fish muscle by QuEChERS extraction followed by LC-MS/MS determination. Sci. Total Environ. 2019, 653, 958–967. [Google Scholar] [CrossRef] [PubMed]
  29. Lazartigues, A.; Wiest, L.; Baudot, R.; Thomas, M.; Feidt, C.; Cren-Olive, C. Multiresidue method to quantify pesticides in fish muscle by QuEChERS-based extraction and LC-MS/MS. Anal. Bioanal. Chem. 2011, 400, 2185–2193. [Google Scholar] [CrossRef]
  30. Fu, L.; Lu, X.; Tan, J.; Wang, L.; Chen, J. Multiresidue determination and potential risks of emerging pesticides in aquatic products from Northeast China by LC-MS/MS. J. Environ. Sci. 2018, 63, 116–125. [Google Scholar] [CrossRef] [PubMed]
  31. Cho, H.R.; Park, J.S.; Kim, J.; Han, S.B.; Choi, Y.S. Multiresidue method for the quantitation of 20 pesticides in aquatic products. Anal. Bioanal. Chem. 2015, 407, 9043–9052. [Google Scholar] [CrossRef]
  32. Colazzo, M.; Alonso, B.; Ernst, F.; Cesio, M.V.; Perez-Parada, A.; Heinzen, H.; Pareja, L. Determination of multiclass, semi-polar pesticide residues in fatty fish muscle tissue by gas and liquid chromatography mass spectrometry. Methods X 2019, 6, 929–937. [Google Scholar] [CrossRef]
  33. Wang, Z.; Li, H.; Yun, Y.; Wang, H.; Meng, B.; Mu, Y.; Gao, S.; Tao, X.; Chen, W. A dynamic multiple reaction monitoring strategy to develop and optimize targeted metabolomics methods: Analyzing bile acids in capecitabine-induced diarrhea. J. Pharm. Biomed. Anal. 2022, 219, 114938. [Google Scholar] [CrossRef]
  34. Qie, M.; Zhao, Y.; Yang, S.; Wang, W.; Xu, Z. Rapid simultaneous determination of 160 drugs in urine and blood of livestock and poultry by ultra-high-performance liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 2019, 1608, 460423. [Google Scholar] [CrossRef] [PubMed]
  35. Taujenis, L.; Olšauskaitė, V.; Padarauskas, A. Determination of nicotine and three minor alkaloids in tobacco by hydrophilic interaction chromatography-tandem mass spectrometry. Acta Chromatogr. 2015, 27, 373–385. [Google Scholar] [CrossRef]
  36. Dai, J.; Lin, H.; Pan, Y.; Sun, Y.; Wang, Y.; Qiao, J.Q.; Lian, H.Z.; Xu, C.X. Determination of chlorpromazine and its metabolites in animal-derived foods using QuEChERS-based extraction, EMR-Lipid cleanup, and UHPLC-Q-Orbitrap MS analysis. Food Chem. 2023, 403, 134298. [Google Scholar] [CrossRef] [PubMed]
  37. Wu, C.; Dong, F.; Mei, X.; Ning, J.; She, D. Isotope-labeled internal standards and grouping scheme for determination of neonicotinoid insecticides and their metabolites in fruits, vegetables and cereals—A compensation of matrix effects. Food Chem. 2020, 311, 125871. [Google Scholar] [CrossRef] [PubMed]
  38. Marin, J.M.; Gracia-Lor, E.; Sancho, J.V.; Lopez, F.J.; Hernandez, F. Application of ultra-high-pressure liquid chromatography-tandem mass spectrometry to the determination of multi-class pesticides in environmental and wastewater samples. Study of matrix effects. J. Chromatogr. A 2009, 1216, 1410–1420. [Google Scholar] [CrossRef] [PubMed]
  39. Pan, X.; Dong, F.; Xu, J.; Liu, X.; Chen, Z.; Liu, N.; Chen, X.; Tao, Y.; Zhang, H.; Zheng, Y. Simultaneous determination of chlorantraniliprole and cyantraniliprole in fruits, vegetables and cereals using ultra-high-performance liquid chromatography-tandem mass spectrometry with the isotope-labelled internal standard method. Anal. Bioanal. Chem. 2015, 407, 4111–4220. [Google Scholar] [CrossRef]
  40. Liu, C.; Zhu, X.; Xu, Z.; Ren, D.; Meng, Y.; Liu, Y.; Tang, J. Dynamic non-target analysis and occurrence of herbicides residues in rice-crayfish and rice-crab co-culture systems in Jiangsu province. J. Ecol. Rural Environ. 2022, 38, 933–943. [Google Scholar]
  41. Xue, Y.; Zhang, Z.M.; Zhang, R.R.; Li, Y.Q.; Sun, A.L.; Shi, X.Z.; Chen, J.; Song, S. Aquaculture-derived distribution, partitioning, migration, and transformation of atrazine and its metabolites in seawater, sediment, and organisms from a typical semi-closed mariculture bay. Environ. Pollut. 2021, 271, 116362. [Google Scholar] [CrossRef]
  42. Tao, Y.; Jia, C.; Jing, J.; Zhang, J.; Yu, P.; He, M.; Wu, J.; Chen, L.; Zhao, E. Occurrence and dietary risk assessment of 37 pesticides in wheat fields in the suburbs of Beijing, China. Food Chem. 2021, 350, 129245. [Google Scholar] [CrossRef]
  43. Wu, Y.; Liu, P.; Sun, J.; Li, J. Dietary Exposure Assessment and Total Diet Study; Chemical Industry Press: Beijing, China, 2019. [Google Scholar]
Molecules 28 04235 g001 550
Figure 1. MRM chromatograms of 90 pesticides and metabolites obtained by HPLC–MS/MS.
Figure 1. MRM chromatograms of 90 pesticides and metabolites obtained by HPLC–MS/MS.
Molecules 28 04235 g001
Molecules 28 04235 g002 550
Figure 2. Effect of sample extraction: (a) The recoveries distribution of 90 pesticides and metabolites under different types of extraction solvents (S1: ACN; S2: 0.2% FA/ACN; S3: ACN/water (8:2); S4: ACN/water (8:2) with 0.2% FA). (b) The RSDs of 90 pesticides and metabolites under different types of extraction solvents ((S1: ACN; S2: 0.2% FA/ACN).
Figure 2. Effect of sample extraction: (a) The recoveries distribution of 90 pesticides and metabolites under different types of extraction solvents (S1: ACN; S2: 0.2% FA/ACN; S3: ACN/water (8:2); S4: ACN/water (8:2) with 0.2% FA). (b) The RSDs of 90 pesticides and metabolites under different types of extraction solvents ((S1: ACN; S2: 0.2% FA/ACN).
Molecules 28 04235 g002
Molecules 28 04235 g003 550
Figure 3. Effect of sample cleanup: (a) Recoveries distribution of 90 pesticides and metabolites under different types of cleanup conditions (S1–S4: Cleanert PEP (sorbent), S1: MEA: Water = 8:2 (loading solvent), MEA (elution solvent); S2: MEA: Water = 8:2 (loading solvent), ACN (elution solvent); S3: ACN: Water = 8:2 (loading solvent), MEA (elution solvent); S4: ACN: Water = 8:2 (loading solvent), ACN (elution solvent); S5–S8: Oasis HLB, S5: MEA: Water = 8:2 (loading solvent), MEA (elution solvent); S6: MEA: Water = 8:2 (loading solvent), ACN (elution solvent); S7: ACN: Water = 8:2 (loading solvent), MEA (elution solvent); S8: ACN: Water = 8:2 (loading solvent), ACN (elution solvent); S9: PRiME HLB (sorbent), ACN (elution solvent)); (b) recoveries of target compounds under different types of cleanup conditions; (c) recoveries of distribution of 90 pesticides and metabolites with different elution volumes.
Figure 3. Effect of sample cleanup: (a) Recoveries distribution of 90 pesticides and metabolites under different types of cleanup conditions (S1–S4: Cleanert PEP (sorbent), S1: MEA: Water = 8:2 (loading solvent), MEA (elution solvent); S2: MEA: Water = 8:2 (loading solvent), ACN (elution solvent); S3: ACN: Water = 8:2 (loading solvent), MEA (elution solvent); S4: ACN: Water = 8:2 (loading solvent), ACN (elution solvent); S5–S8: Oasis HLB, S5: MEA: Water = 8:2 (loading solvent), MEA (elution solvent); S6: MEA: Water = 8:2 (loading solvent), ACN (elution solvent); S7: ACN: Water = 8:2 (loading solvent), MEA (elution solvent); S8: ACN: Water = 8:2 (loading solvent), ACN (elution solvent); S9: PRiME HLB (sorbent), ACN (elution solvent)); (b) recoveries of target compounds under different types of cleanup conditions; (c) recoveries of distribution of 90 pesticides and metabolites with different elution volumes.
Molecules 28 04235 g003
Table
Table 2. Linearity, precision, and accuracy of 90 pesticides and metabolites in grass carp and prawn samples.
Table 2. Linearity, precision, and accuracy of 90 pesticides and metabolites in grass carp and prawn samples.
AnalyteLinearity *Grass CarpPrawn
Linearity **Recovery (Intra-RSD, %)LOQLinearity ***Recovery (Intra-RSD, %)LOQ
0.05 μg/kg0.5 μg/kg5 μg/kg50 μg/kg0.05 μg/kg0.5 μg/kg5 μg/kg50 μg/kg
Chlorpyrifos0.99950.9994 77.6 (9.3)85.3 (16.1)5.0 0.9996 94.1 (5.2)81.4 (13.8)5.0
Phoxim0.99980.9989 105.9 (9.9)77.7 (4.7)84.9 (16.9)0.5 0.9998 88.2 (18.5)85.2 (8.1)79.0 (14.2)0.5
Triazophos0.99860.9999 88 (3.6)83.4 (3.5)79.9 (10.2)0.5 0.9960 90.5 (6.8)89.8 (4.1)79.6 (15.9)0.5
Malathion0.99260.9999 89.7 (5.5)84.2 (3.3)89.1 (10.6)0.5 0.9954 60.5 (3.7)83.6 (7.6)78.7 (14.2)0.5
Phorate0.99950.9998 64.2 (5.2)65.2 (6.1)84.3 (19.0)0.5 0.9997 79.2 (14.3)78.9 (3.7)81.2 (15.1)0.5
Phorate-Sulfone0.99730.9919 76.8 (19.2)85.5 (14.9)5.0 0.9956 92.3 (10.9)83.6 (7.6)81.9 (13.2)0.5
Phorate-Sulfoxide0.99860.999994.0 (1.2)94.2 (6.1)93.8 (2.7)85.9 (17.5)0.05 0.9980 92.4 (11.4)95.6 (4.6)78.9 (3.7)81.0 (14.7)0.05
Isocarbophos0.99230.9998102.3 (11.1)90.0 (5.3)66.9 (12.1)83.0 (16.4)0.05 0.9976 85.6 (6.3)87.3 (3.4)90.2 (3.9)82.1 (13.0)0.05
Profenofos0.99890.9997 83.1 (6.1)70.4 (7.7)86.2 (17.8)0.5 0.9997 64.2 (9.9)89.5 (4.1)79.9 (15.2)0.5
Parathion0.99850.99793.9 (2.7)87.3 (3.6)82.8 (3.8)83.5 (18.7)0.05 0.9958 92.5 (2.7)98.7 (3.9)80.7 (14.1)0.5
Parathion–methyl0.99610.9913 96.3 (16.5)97.0 (14.8)5.0 0.9976 91.8 (4.2)81.3 (14.0)5.0
Methamidophos0.9950.9996 64.5 (7.5)86.3 (17.5)5.0 0.9999 76.9 (4.1)112.6 (10.8)5.0
Acephate0.99450.9972 67.7 (6.1)98.9 (3.4)5.0 0.9972 98.7 (4.5)90.7 (1.5)5.0
Terbufos0.99980.9959 117.4 (12.3)85.9 (15.3)5.0 0.9994 85.3 (5.2)78.2 (13.1)5.0
Fosthiazate0.99430.9999114.4 (3.2)89.6 (2.9)85.2 (1.5)87.9 (13.3)0.05 0.9989 103.2 (10.5)89.2 (2.4)88.9 (3.1)82.5 (18.5)0.05
Isazofos0.99860.9999 88.1 (3.6)82.8 (3.2)86.8 (16.4)0.5 0.9966 101.4 (3)90.9 (4.0)81.8 (13.3)0.5
Dichlorvos0.99980.9936 61.8 (15.9)85.4 (16.0)5.0 0.9996 84.3 (17.8)78.6 (8.2)81.6 (14.8)0.5
Dipterex0.99920.9911 92.9 (8.2)83.7 (16.2)5.0 0.9943 78.9 (7.5)96.7 (7.0)101.2 (13.9)0.5
Omethoate0.99660.9982 72.5 (2.1)92.5 (7.9)5.0 0.9957 88.3 (7.4)83.1 (15.4)5.0
Carbofuran0.99100.9999115.9 (13.4)109.8 (4.0)115.7 (3.9)87.9 (13.9)0.05 0.9972 72.5 (8.3)100.9 (9.3)117.2 (3.4)90.3 (8.4)0.05
3–hydroxycarbofuran0.99570.9997 85.1 (13.0)79.4 (4.6)90.2 (13.8)0.5 0.9975 78.9 (11.5)87.2 (3.4)84.5 (12.9)0.5
Aldicarb0.99440.9991 76.3 (10.7)77.0 (10.4)90.8 (6.5)0.5 0.9920 81.2 (12.6)82.3 (7.1)87.6 (4.3)82.4 (12.7)0.05
Aldicarb-Sulfone0.99860.9957 89.5 (11.1)83.8 (3.9)84.5 (16.5)0.5 0.9973 77.5 (15.3)70.2 (12.9)75.3 (3.4)85.3 (10.3)0.05
Aldicarb-Sulfoxide0.99470.9955 74.9 (8.5)94.8 (9.3)5.0 0.9992 79.5 (3.1)83.2 (13.3)5.0
Methomyl0.99480.9999 91.2 (7.7)83.6 (5.9)101.9 (10.7)0.5 0.9985 71.6 (6.5)82.3 (1.1)85.1 (3.6)86.4 (13.3)0.05
Isoprocarb0.99660.9999 87.5 (5.1)76.9 (6.2)75.4 (16.1)0.5 0.9986 116.2 (10.7)74.6 (3.7)84.6 (4.2)82.8 (13.0)0.05
Fenpropathrin0.99960.9946 83.6 (7.3)97.5 (5.9)5.0 0.9979 94 (8.1)84.3 (12.4)5.0
Chloantraniliprole0.99910.9988 78.5 (12.4)71.4 (4.8)90.8 (9.8)0.5 0.9994 76.7 (5.2)78.2 (14.8)92.8 (5.6)0.5
Flubendiamide0.99980.9997 108.9 (9.4)81.9 (16.5)90.5 (11.4)0.5 0.9998 76.1 (12.3)92.7 (1.1)85.4 (11.8)0.5
Acetamiprid0.99360.9989 57.5 (4.9)75.9 (5.8)81.4 (16.4)0.5 0.9974 83.3 (3.5)89.8 (13.3)5.0
Imidacloprid0.99820.9989 79.9 (9.7)78.3 (2.7)95.1 (9.0)0.5 0.9997 89.3 (13.5)77.3 (7.7)82.1 (14.7)0.5
Thiamethoxam0.99450.9979 101.8 (11.7)79.9 (3.6)75.2 (13.8)0.5 0.9994 77.2 (19.5)84.6 (6.8)78.5 (16.9)0.5
Clothianidin0.99260.9945 63.9 (15.8)82.4 (1.7)95.1 (5.2)0.5 0.9986 75.5 (18.9)85.4 (2.9)82.6 (15.3)0.5
Fipronil0.99940.9996 90.6 (2.9)80.3 (3.6)90.3 (12.9)0.5 0.9998 90.2 (2.8)89.9 (4.6)79.3 (15.1)0.5
Fipronil-Sufone0.99730.9999 90.6 (4.6)70.8 (13.0)83.2 (17.2)0.5 0.9984 93.6 (8.6)90.2 (4.8)86.7 (14.9)0.5
Fipronil-Sufoxide0.99970.9999 91.3 (7.4)70.2 (9.6)84.8 (15.1)0.5 0.9998 76.1 (14.9)83.1 (7.7)86.6 (12.1)0.5
Buprofezin0.99980.9999101.4 (5.2)84.5 (4.6)70.1 (7.7)85.9 (15.8)0.05 0.9998 95.2 (7.4)89.2 (0.2)87.8 (2.6)84.2 (13.9)0.05
Hexaflumuron0.99840.9959 109.7 (17.4)97.5 (10.3)99.7 (14.8)0.5 0.9995 94.8 (9.4)76.9 (14.1)5.0
Hydroxy Chlorothalonil0.98750.9948 83.7 (9.7)74.8 (10.6)5.0 0.9971 70.4 (4.1)82.1 (13.2)5.0
Tricyclazole0.99780.9999103.0 (6.6)86.5 (4.0)75.1 (2.6)96.6 (10.0)0.05 0.9971 110.2 (4.6)91.5 (2.1)85.8 (3.8)87.8 (9.9)0.05
Propiconazole0.99930.9999 94.8 (3.9)78.5 (6.2)90.6 (15.6)0.5 0.9991 84.5 (7.2)89.2 (3.1)82.9 (12.1)0.5
Difenoconazole0.99950.999797.7 (6.3)104.2 (7.6)75.6 (6.1)88.0 (14.2)0.05 0.9997 108.8 (11.9)90.2 (3.8)86.6 (6.7)84.3 (12.6)0.05
Triadimefon0.99970.9993 89.4 (3.4)87.3 (2.7)86.7 (13.6)0.5 0.9980 89.5 (4.0)80.5 (3.3)83.2 (13.4)0.5
Hexaconazole0.99950.9999 90.4 (3.0)82.3 (5.4)96.0 (17.3)0.5 0.9989 85.2 (4.5)87.3 (2.7)78.4 (13.1)0.5
Epoxiconazole0.99950.9999 97.7 (3.5)76.0 (10.9)97.2 (14.8)0.5 0.9992 92.5 (3.8)87.1 (3.3)84.6 (10.3)0.5
Tebuconazole0.99950.9996 90.3 (4.2)81.6 (5.8)83.5 (19.8)0.5 0.9991 91.3 (1.2)86.5 (3.8)84.8 (13.7)0.5
Carbendazim0.99510.998392.6 (7.6)65.3 (15.3)72.8 (9.1)84.7 (16.5)0.05 0.9995 101 (11.1)90.1 (18.2)91.6 (3.0)84.2 (8.3)0.05
Procymidone0.99910.9975 81.9 (4.6)97.4 (2.0)5.0 0.9995 83.3 (10.6)92.7 (4.3)84.4 (14.7)0.5
Azoxystrobin0.9970.9999 85.7 (9.6)91.8 (2.8)93.1 (11.3)0.5 0.9995 101.3 (5.1)86.9 (3.1)90.2 (3.8)83.6 (19.0)0.05
Pyraclostrobine0.99450.999989.4 (8.3)84.4 (3.3)86.6 (4.8)84.9 (17.2)0.05 0.9999 89.2 (7.8)83.9 (14.3)82.8 (13.4)0.5
Pyrimethanil0.99860.999990.6 (3.2)80.6 (3.5)70.4 (6.7)99.1 (4.8)0.05 0.9991 107.5 (17.6)88.5 (2.9)88.4 (2.6)84.6 (15.5)0.05
Isoprothiolane0.99820.9999104.9 (9.5)94.0 (3.9)79.3 (6.2)87.5 (14.2)0.05 0.9986 93.1 (9.9)88.1 (1.9)88.6 (3.1)83.2 (13.0)0.05
Alachlor0.99970.999 94.6 (17.5)62.3 (18.3)87.5 (14.3)0.5 0.9998 92.2 (15.8)84.9 (5.5)85.9 (12.5)0.5
Acetochlor0.99710.9966 97.5 (8.2)92.3 (1.6)107.3 (5.5)0.5 0.9989 81.2 (5.1)87.2 (3.5)82.8 (14.2)0.5
Bensulfuron methyl0.99930.9998105.7 (4.2)89.9 (4.5)88.5 (18.1)84.8 (6.2)0.05 0.9998 94.2 (13.1)85.4 (3.7)85.2 (3.1)82.8 (2.9)0.05
Pyrazosulfuron-ethyl0.99810.9999 86.8 (3.3)63.9 (16.7)89.3 (13.8)0.5 0.9986 91.6 (4.0)89.2 (3.4)78.3 (13.4)0.5
Tribenuron-methyl0.9930.9998 75.2 (4.9)65.0 (3.5)85.6 (17.9)0.5 0.9998 80.2 (11.1)73.2 (4.1)83.0 (12.7)0.5
Atrazine0.99930.9999 89.9 (4.9)74.9 (6.1)84.5 (15.0)0.5 0.9992 95.6 (5.4)88.6 (1.6)87.8 (3.1)80.6 (14.1)0.05
Desethylatrazine0.99890.9997 91.0 (3.5)81.5 (2.1)86.5 (16.2)0.5 0.9999 96.2 (16.5)89.5 (3.7)84.7 (5.4)83.7 (12.6)0.05
Desisopropylatrazine0.9980.9977 79.7 (14.1)71.7 (5.2)98.0 (15.3)0.5 0.9995 82.2 (10.7)83.4 (3.8)81.1 (15.1)0.5
Hydroxyatrazine0.9980.9992 70.5 (6.4)61.9 (2.8)95.6 (6.9)0.5 0.9990 89.5 (16.5)64.2 (10.1)68.2 (10.4)83.1 (13.8)0.05
Pendimethalin0.99940.9988 109.0 (9.6)71.8 (5.7)96.3 (9.6)0.5 0.9999 81.5 (15.8)91.5 (4.8)85.0 (11.6)0.5
Clomazone0.99860.999999.5 (10.9)88.1 (4.7)79.2 (3.9)87.9 (12.1)0.05 0.9997 107 (12.4)84.5 (5.0)85.5 (3.0)83.9 (2.4)0.05
Propargite0.99790.9924 79.3 (13.9)62.9 (12.6)92.6 (14.8)0.50 0.9997 76.8 (8.7)92.0 (11.4)5.0
Pyridaben0.99070.998984.7 (17.1)68.8 (19.8)60.3 (13.4)104.4 (8.8)0.05 0.9993 115.8 (7.7)88.2 (8.7)82.9 (9.0)88.7 (17.8)0.05
Paclobutrazol0.99980.999992.6 (19.4)92.9 (5.3)89.5 (2.2)83.7 (4.8)0.05 0.9995 98.8 (11.3)85.6 (5.2)88.2 (2.7)84.5 (17.6)0.05
Forchlorfenuron0.99540.999995.5 (7.1)79.4 (3.2)73.0 (3.3)100.8 (16.9)0.05 0.9949 76.5 (4.5)74.2 (1.8)77.4 (2.8)84.4 (12.9)0.05
Carbaryl0.99510.9999 82.1 (4.9)86.9 (5.8)88.9 (12.1)0.5 0.9976 68.5 (4.4)86.1 (3.1)88.0 (13.3)0.5
Indoxacarb0.99950.9998 99.7 (6.0)60.7 (10.3)98.3 (17.9)0.5 0.9995 90.2 (7.7)95.9 (4.0)78.0 (12.8)0.5
Dinotefuran0.9980.9987 98.2 (19.4)74.9 (3.4)84.6 (17.3)0.5 0.9999 76.1 (17.8)70.5 (5.7)82.5 (13.9)0.5
Emamectin-benzoate b1a0.99960.9997 99.2 (18.2)72.8 (10.3)96.0 (14.0)0.5 0.9999 79.2 (6.5)70.2 (5.8)84.6 (14.6)0.5
Emamectin-benzoate b1b0.99830.9992 91.6 (10.5)77.3 (8.7)90.6 (14.2)0.5 0.9993 74.6 (6.3)82.6 (12.2)5.0
Methoxyfenozide0.99930.9999 94.2 (12.8)82.1 (3.4)90.2 (3.8)0.5 0.9991 92.3 (1.8)90.7 (3.4)75.2 (16.8)0.5
Prothioconazole0.99950.9933 82.8 (9.1)84 (17.4)5.0 0.9997 80.9 (6.2)84.5 (11.5)5.0
Ametoctradin0.99960.9999 88.4 (3.3)73.5 (4.8)103.5 (8.9)0.5 0.9997 85.6 (3.5)87.8 (2.9)113.4 (13.6)0.5
Boscalid0.99010.9947 72.7 (17.3)101.4 (8.8)5.0 0.9933 83.5 (7.4)85.9 (3.1)83.6 (12.3)0.5
Flutolanil0.99750.9999 92.8 (4.2)85.7 (3.6)90.5 (10.1)0.5 0.9996 82.6 (5.5)86.9 (2.0)86.3 (10.4)0.5
Fomesafen0.99920.9991 105.4 (13.2)62.2 (19.1)99.8 (15.5)0.5 0.9995 92.1 (4.4)83.3 (12.8)5.0
Prometryn0.99970.9999103.6 (6.4)86.4 (3.9)72.6 (8.5)91.9 (7.6)0.05 0.9998 93.4 (7.5)87.2 (1.1)87.7 (3.2)84.9 (16.3)0.05
Imazethapyr0.99970.9998 76.8 (4.3)66.8 (3.3)97.1 (5.5)0.5 0.9981 60.9 (8.0)61.8 (5.4)82.2 (13.9)0.5
Nicosulfuron0.99940.9998105.7 (4.2)89.8 (3.6)87.7 (6.5)85.3 (14.6)0.05 0.9998 94.2 (13.1)85.2 (3.7)86.0 (2.6)83.1 (13.6)0.05
Sulfometuron-methyl0.99890.9999 87.1 (3.4)82.6 (1.8)89.3 (13.8)0.5 0.9994 112 (14.9)82.1 (1.4)84.9 (3.6)97.4 (13.4)0.05
Mesotrione0.99190.995 60.3 (16.9)75.6 (6.8)100 (13.3)0.5 0.9839 62.5 (5.6)63.3 (8.2)82.2 (13.6)0.5
Diuron0.99480.9999 80.7 (5.6)73.3 (10.5)76.5 (15.6)0.5 0.9939 108.5 (7.2)87.6 (1.1)88.7 (2.9)81.1 (12.8)0.05
Quinclorac0.99960.9993 50.4 (15.3)52.5 (2.6)88.9 (12.3)0.5 0.9974 75.7 (11.7)77.2 (5.9)87.9 (10.5)0.5
Pretilachlor0.99880.9982100.2 (8.1)84.8 (3.7)87.6 (3.5)98.4 (13.2)0.05 0.9990 105.4 (10.8)88.2 (3.4)87.9 (2.1)75.5 (12.8)0.05
Metolachlor0.99830.9998105.0 (4.6)87.5 (2.2)71.1 (11.8)85.3 (17.5)0.05 0.9996 118.6 (9.1)90.1 (3.8)88.8 (2.8)84.3 (12.7)0.05
Flumetsulam0.99380.9999100.3 (9.2)89.3 (4.5)77.5 (4.9)86.4 (16.5)0.05 0.9864 97.7 (6.8)82.4 (4.7)83.2 (4.4)85.9 (8.3)0.05
Penoxsulam0.99720.9999104.5 (6.3)93.3 (1.7)83.8 (3.1)88.7 (13.1)0.05 0.9990 109.9 (7.6)91.8 (2.3)89.6 (4.1)86.0 (10.5)0.05
Saflufenacil0.99910.9992 86.9 (4.2)70.6 (14.6)100.3 (16.7)0.5 0.9995 85.2 (1.6)91.9 (4.9)98.9 (12.5)0.5
* represents solvent calibration curves; ** represents matrix-matched calibration curves in grass carp; *** represents matrix-matched calibration curves in prawn.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, Y.; Xie, J.; Dong, F.; Wu, X.; Pan, X.; Liu, X.; Zheng, Y.; Zhang, J.; Xu, J. Highly-Selective Analytical Strategy for 90 Pesticides and Metabolites Residues in Fish and Shrimp Samples. Molecules 2023, 28, 4235. https://doi.org/10.3390/molecules28104235

AMA Style

Guo Y, Xie J, Dong F, Wu X, Pan X, Liu X, Zheng Y, Zhang J, Xu J. Highly-Selective Analytical Strategy for 90 Pesticides and Metabolites Residues in Fish and Shrimp Samples. Molecules. 2023; 28(10):4235. https://doi.org/10.3390/molecules28104235

Chicago/Turabian Style

Guo, Yage, Jun Xie, Fengshou Dong, Xiaohu Wu, Xinglu Pan, Xingang Liu, Yongquan Zheng, Jie Zhang, and Jun Xu. 2023. "Highly-Selective Analytical Strategy for 90 Pesticides and Metabolites Residues in Fish and Shrimp Samples" Molecules 28, no. 10: 4235. https://doi.org/10.3390/molecules28104235

APA Style

Guo, Y., Xie, J., Dong, F., Wu, X., Pan, X., Liu, X., Zheng, Y., Zhang, J., & Xu, J. (2023). Highly-Selective Analytical Strategy for 90 Pesticides and Metabolites Residues in Fish and Shrimp Samples. Molecules, 28(10), 4235. https://doi.org/10.3390/molecules28104235

Article Metrics

Back to TopTop