Abstract
A highly accurate and precise method for the simultaneous detection of 18 neonicotinoids and their metabolites in meat was developed using liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). To improve the pretreatment step of the method, five different commercially available clean-up materials (including C18+PSA (primary secondary amine), Z-Sep (with Discover DSC-C18), EMR-Lipid, SHIMSEN QuEChERS, and Clean-up LPAS) were studied in the treatment of three meat matrices: pork, duck and yellow croaker. Based on the recovery data, we found that among the five purification materials, SHIMSEN QuEChERS was slightly more effective than the others for 18 neonicotinoids. Therefore, SHIMSEN QuEChERS was used as the purification sorbent, and the extraction solvents, extraction methods and chromatographic and mass spectrometric conditions were optimized. A matrix-matched calibration method was applied for quantification. In three different meat matrixes (pork, duck, and yellow croaker), all the target compounds showed good linearity, both with values of r2 > 0.995. The average recovery of all neonicotinoids ranges from 63.4 to 114.2% (pork), 63.0–113.2% (duck), and 63.9–110.5% (yellow croaker). Relative standard deviations were all <15% for intraday and interday precision. The values of limit of detection (LOD) and limit of quantification (LOQ) were, respectively, ranging from 0.04 to 1.0 μg kg−1 and 0.10 to 2.0 μg kg−1. Compared with previous reports, this method has advantage in LOQs, indicating that it it may be a preferred choice for the detection of neonicotinoid pesticides in meat samples.
Introduction
Neonicotinoids, prevalent in global usage, have been frequently identified in diverse environmental and biological matrices, thereby posing potential ecological and human health risks [1, 2]. Recent research indicates that neonicotinoid insecticides present hazards not only pose a hazard to the environment and non-target organisms but also contaminate children's cerebrospinal fluid, correlate with leukemia and lymphoma [3]. In addition, they contribute to a decrease in testosterone levels in the bloodstream and influence glucose metabolism and insulin secretion through mechanisms involving pancreatic beta-cell dysfunction, oxidative stress, and interference [4, 5]. Meanwhile, studies on the metabolites of neonicotinoid insecticides were conducted, with some metabolites demonstrating higher toxicity than their parent compounds [6, 7]. For instance, 6-chloronicotinic acid, a metabolite of imidacloprid, has been experimentally proven to be more toxic to crustaceans than imidacloprid itself [8]. Given the risks associated with neonicotinoid insecticides, countries, and regions such as China, the United States, and the European Union have gradually established and refined food safety standards, including restrictions and even bans in certain areas. China, for example, has been updating the limitation standards for neonicotinoid insecticides in food since 2014, with revisions in 2016, 2019, and 2021. The U.S. Environmental Protection Agency has revoked the registration of 12 pesticide products containing thiamethoxam, and the European Union has resolved to prohibit the outdoor application of several neonicotinoid insecticides [9].
Current studies on neonicotinoid residues are primarily focused on plant-based foods such as grains, teas, fruits, vegetables, and bee products, since neonicotinoids are commonly used in the treatment of plant stems, leaves, and soil. Given the extensive application of neonicotinoids, livestock feeding on these plants are at high risk for bioaccumulation of these insecticides. Countries and regions including China, Japan, Europe, and the United States have instituted regulations for various neonicotinoid insecticides in meat products, with considerable disparities in the established limit values across these nations. For example, in China, the provisional maximum residue limit (MRL) for fludioxonil in mammalian meat is established at 0.3 mg kg−110. In contrast, Japan has set the MRLs for lamb, horsemeat, and beef at 0.05 mg kg−1, while there is no specific regulation for pork [10]. In the United States, the MRL is 0.03 mg kg−1 for pork and 0.08 mg kg−1 for lamb, horse, and beef [10]. Meanwhile, the European Union has set the limit at 0.03 mg kg−1 for pork and 0.05 mg kg−1 for lamb, horse, and beef, demonstrating the variations in international regulatory standards [10]. Therefore, to fulfill food safety requirements, it is imperative to develop a sensitive and adaptable method to quantify multiple residues of neonicotinoid insecticides in meat.
Several analytical instruments such as gas chromatography (GC) [11], high-performance liquid chromatography with ultraviolet detection (HPLC-UV) [12, 13], high-performance liquid chromatography-mass spectrometry (HPLC-MS) [14], ion chromatography (IC) [15], capillary electrophoresis (CE) [16] and immunoassay [17] are routinely employed for the detection of neonicotinoid residues in food. Among these devices, HPLC-MS is the most favored due to its reliability, sensitivity, and selectivity. While the accuracy of the results is positively related to the performance of the instrument, the impact of sample pre-treatment techniques is much greater. Traditional sample pre-treatment techniques, such as liquid-liquid extraction (LLE) [18], solid-phase extraction (SPE) [19–21], etc., are considered time consuming and lengthy operations, and gradually being replaced by the QuEChERS (Quick, Easy, Cheap, Efficient, Rugged and Safe) method [22–24]. Nevertheless, the detection of multiple pesticide residues in lipid-rich matrices, such as meat, continues to pose a challenge, even for the QuEChERS method. In recent years, new commercial dispersive sorbents such as Merck's Z-Sep and Z-Sep+ (zirconium oxide) [25], Agilent's EMR-Lipid (unknown composition) [26], are proved to remove major lipid classes from sample matrix without unwanted analyte loss. Subsequently, Japanese company Shimadzu and the Chinese company Knorth also launched the competing products SHIMSEN QuEChERS (Salt: MgSO4, NaCl, Disodium hydrogen citrate (DHS), and Sodium citrate (TSCD); dSPE purification tube (unknown composition)) and Clean-up LPAS (unknown composition) respectively. However, the efficacy of their lipid removal abilities warrants further validation.
In this study, we evaluated the performance of various sorbents in the cleanup stage of neonicotinoids analysis across three types of matrices: prok, duck, and yellow croaker. After extracted with acetonitrile, the sample was exposed to five different sorbents: C18+PSA (primary secondary amine), Z-Sep (with Discover DSC-C18), EMR-Lipid, SHIMSEN QuEChERS, and Clean-up LPAS. These sorbents were compared based on the recovery rates for 18 nenicotioids and their metabolites, and the best of these was used to investigate matrix effects, precision, and sensitivity using HPLC-MS/MS. To the best of our knowledge, this is the first report comparing five types of commercially available sorbents for meat matrices.
Materials and methods
Materials and chemicals
The analytical standards for 18 neonicotinoids and their metabolites (Fig. 1), including IMI (≥99.8%), CYC (≥92.7%), THX (≥99.0%), CLO (≥99.8%), and FLO (≥99.0%), were procured from Dr. Ehrenstorfer Gmbh (Germany). Additionally, 5-OH-IMI (≥99.7%), DN (≥99.0%), and UF (≥99.0%) were obtained from A ChemTek (USA); 6-CHL (≥99.0%), ACE (≥99.2%), DM-ACE (≥99.5%), THA (≥99.4%), IMIT (≥98.0%), DM-CLO (≥98.2%), and DNT (≥99.9%) were sourced from Tan-Mo Technology (China); and SUL (≥99.0%) were acquired from CATO Research Chemicals (USA); and IM-1-4 (≥98.0%) was purchased from AltaScientific (China). Sorbents such as MgSO4, C18, and PSA were obtained from Biocomma (China); SHIMSEN QuEChERS from Shimsen (Japan); Z-Sep (with Discovery DSC-C18) from Merck (USA); and Captiva EMR-Lipid from Agilent (USA). HPLC grade acetonitrile, acetic acid, and methanol were procured from Merk (USA). Deionized water was produced using a Milli-Q purification system (Millipore, USA). Prok, duck, and yellow croaker samples were sourced from a local supermarket.
Chemical structures of 18 neonicotinoids and their metabolites
Citation: Acta Chromatographica 2024; 10.1556/1326.2023.01170
Standard solutions
Individual standard solutions of 18 neonicotinoids and their metabolites were prepared by separately dissolving the technical grade materials in acetonitrile and stored at −18 °C (100 mg L−1). Mixed standard solutions containing each target compound for this study were prepared in a mixture of appropriate amounts of the individual stock solutions with 20% aqueous acetonitrile (containing 0.1% formic acid). A series of working solutions (mixed standard solutions and matrix-matched standard solutions) were prepared at the concentration of 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, 40, 100, 200, 300 ug L−1.
Sample preparation
The meat samples were subjected to homogenization, individually packed in sealed bags, and then stored at −20 °C until analysis.
Procedure I: C18 and PSA as sorbents
C18 and PSA were employed as adsorbents in accordance with the official QuEChERS method (Association of Analytical Communities (AOAC.2007.01)), adapted for pesticide analysis in lipid-rich matrices [27]. A 10.00 g weight of the homogenized sample was measured into a 50 mL centrifuge tube, to which 10 mL of acetonitrile solution (containing 1% acetic acid), 4 g of MgSO4, and 1 g of NaCl were added. This mixture was immediately shaken for 1 min and centrifuged at 5,000 rpm for 5 min. The upper 5 mL of the solution (organic phase) was transferred to a 15 mL centrifuge tube pre-loaded with 250 mg PSA, 250 mg C18, and 750 mg MgSO4. After shaking for 30 s and centrifuged for 5 min at 5,000 rpm, 3 mL of the supernatant (acetonitrile) was transferred to a test tube, nearly dried under nitrogen at 35 °C, and reconstituted with 1 mL of 20% aqueous acetonitrile (containing 0.1% formic acid). The supernatant was then filtered through a 0.22 μm membrane in preparation for mass spectrometry analysis.
Procedure II: Merck's Z-Sep as sorbents
10.00 g of the sample was measured into a 50 mL centrifuge tube, 10 mL of acetonitrile was added and shaken vigorously for 1 min. Following this, the mixture was sonicated for 15 min, and 5 mL was extracted and transferred to a 10 mL glass tube. After adding 20 mg of Z-Sep and 50 mg of DSC-C18 and vortexing for 1 min, the mixture was allowed to stand for 5 min 4 mL of the upper liquid layer was taken, nearly dried with nitrogen 35 °C, reconstituted with 1 mL of 20% aqueous acetonitrile (containing 0.1% formic acid), and filtered through a 0.22 μm membrane in preparation for mass spectrometry analysis.
Procedure III: Agilent's EMR-lipid as sorbents
4.00 g of the sample was measured into a 50 mL centrifuge tube, 10 mL of acetonitrile was added, and vortexed for 1 min. The mixture was then centrifuged at 5,000 rpm for 5 min, and 5 mL of the supernatant solution was transferred to the first purification tube (containing 1 g of pre-activated EMR sorbent with 5 mL of Milli-Q H2O), vortexed for 10 min, and centrifuged for an additional 10 min. Subsequently, 5 mL of the supernatant solution was transferred into the second purification tube and vortexed for 10 min. The supernatant was then evaporated to dryness at 35 °C, reconstituted in 1 mL of 20% aqueous acetonitrile (containing 0.1% formic acid), and filtered through a 0.22 μm membrane in preparation for mass spectrometry analysis.
Procedure IV: Shimadzu's SHIMSEN QuEChERS as sorbents
5.00 g of sample was measured into a 50 mL centrifuge tube, 5 mL of water was added, and the mixture was vortexed. Then 10 mL of acetonitrile solution (containing 0.4% acetic acid) was added, vortexed for 1 min, and sonicated for 15 min. After adding the SHIMSEN QuEChERS extraction salt packet and vortexing for 1 min, the mixture was centrifuged at 10,000 rpm for 5 min. 8 mL of the supernatant was transferred to the SHIMSEN QuEChERS dSPE purification tube. After vortexing for 1 min and centrifuging at 10,000 rpm for 5 min, 4 mL of the upper solution was collected, evaporated to dryness under nitrogen at 35 °C, reconstituted with 1 mL of 20% aqueous acetonitrile (containing 0.1% formic acid), and filter it through a 0.22 μm membrane in preparation for mass spectrometry analysis.
Procedure V: Knorth's clean-up LPAS as sorbents
4.00 g of the sample was measured into a 50 mL centrifuge tube, mixed with 4 mL of water using a vortex mixer, and then supplemented with 12 mL of acetonitrile solution (containing 0.4% acetic acid). The mixture was vortexed for 1 min and sonicated for 15 min, and 5 mL of the supernatant was then filtered through an SPE column. This filtrate was collected and passed through a 0.22 μm membrane in preparation for mass spectrometry analysis.
LC-MS/MS analysis
Sample analysis was performed using a 2040C HPLC (Shimadzu, Japan) coupled with an 8045 Triple Quadrupole mass spectrometer (Shimadzu, Japan). Chromatographic separation was achieved at 30 °C on a Shim-pack GIST C18-AQ column (2.1 mm × 150 mm, 3.0 µm, Shimadzu, Japan). The mobile phase was composed of 2 mM ammonium acetate and 0.1% formic acid in water (mobile phase A) and acetonitrile (mobile phase B). The gradient program was developed with a 5 µL injection volume and a flow rate of 0.3 mL min−1. The program began with 0–25% B over 0.2 min, increased to 25–45% B over 2.8 min, then to 45–75% B over 4.2 min, followed by re-equilibration at 20% B for 0.5 min, and maintained at 20% B for 4.5 min.
The MS/MS detection was performed in multiple reaction monitoring (MRM) mode with positive ESI. Data collection was monitored using the LabSolution Insight software (5.91), and the optimized source parameters were as follows:
interface voltage of 4 kV
desolvation line temperature of 250 °C
heat block temperature of 400 °C
nebulizer gas (nitrogen) flow at 3 L min−1
drying gas (nitrogen) flow at 10 L min−1
heating gas (air) flow at 9.5 L min−1
Results and discussion
Optimization of HPLC-MS/MS conditions
Detection of multi-residue insecticides in complex matrices requires efficient chromatographic separation and sensitive mass spectrometric detection. Therefore, it is essential to optimize instrument parameters to augment both selectivity and sensitivity.
A direct infusion of both single and mixed neonicotinoid standard solutions was used to tune mass spectrometry for each insecticide. The mass spectrometer was operated in Multiple Reaction Monitoring (MRM) mode to select the most abundant m/z value. It was observed that the signals of the 18 neonicotinoid insecticides were significantly higher in the ESI positive ion mode compared to the ESI negative ion mode. Consequently, for each analyte, the protonated molecular ion (M + H)+ was identified and utilized as the precursor ion. A pair of product ions exhibiting the highest abundance and stability were selected for confirmation, while the ion with the highest intensity was used for quantification. The optimized MS2 parameters are presented in Table 1.
Mass spectrometry parameters for the analysis of 18 neonicotinoids and their metabolites
| Analyte | Precursor ion (m/z) | Product ion (m/z) | CE(V) | Retention time (min) |
| DN | 158.1 | 102.1*,57.1 | −17, −25 | 3.381 |
| IM-1-4 | 157.1 | 126.1*,73.0 | −15, −44 | 3.546 |
| UF | 159.1 | 102.1*,67.1 | −13, −18 | 4.332 |
| FLO | 230.1 | 203.1*,174.2 | −13, −25 | 5.814 |
| DNT | 203.1 | 129.0*,157.1 | −16, −12 | 5.965 |
| NTP | 271.1 | 126.0*,225.1 | −29, −15 | 7.071 |
| 6-CHL | 158.0 | 122.0*,51.1 | −21, −36 | 8.172 |
| THM | 292.0 | 211.1*,181.1 | −15, −21 | 8.623 |
| 5-OH-IMI | 272.1 | 225.1*,134.1 | −15, −41 | 8.830 |
| THCP | 253.0 | 126.1*,99.0 | −18, −42 | 9.122 |
| DM-CLO | 236.0 | 132.0*,113.0 | −13, −26 | 9.306 |
| CYC | 323.2 | 151.1*,277.1 | −25, −10 | 9.450 |
| CLO | 250.0 | 169.1*, 131.9 | −15, −18 | 10.541 |
| IMI | 256.1 | 209.1*,175.1 | −15, −17 | 10.689 |
| DM-ACE | 209.1 | 126.0*,90.1 | −17, −33 | 11.370 |
| IMIT | 262.0 | 181.1*,122.1 | −17, −30 | 11.617 |
| ACE | 223.1 | 126.0*,56.1 | −20, −15 | 12.452 |
| SUL | 278.1 | 174.0*,154.0 | −8, −28 | 14.622 |
* Quantitation ion.
Livestock meat, which is high in fat, protein, cholesterol, vitamins, and a variety of metabolites, can interfere with the separation and detection of target analytes, and ultimately affect their recoveries. Therefore, the choice of chromatograph columns is of crucial importance for improving the sensitivity of the target analytes, especially regarding the quality of the chromatographic peak shapes. To achieve optimal chromatographic conditions, two lengths of InertSustain AQ-C18 columns (2.1 × 50 mm, 3.0 μm; 2.1 × 100 mm, 3.0 μm), and a Shim-pack GIST C18-AQ (2.1 × 150 mm, 3.0 μm) were compared. The Shim-pack GIST C18-AQ (2.1 × 150 mm, 3.0 μm) column was observed to effectively separate target analytes and minimize substrate interference. While the InertSustain AQ-C18 column (2.1 × 100 mm, 3.0 μm) is capable of separating the target compounds, in the context of matrices, FLO and SUL are susceptible to interference from impurities, making their separation difficult and resulting in trailing peaks. The retention time of each target on the InertSustain AQ-C18 column (2.1 × 50 mm, 3.0 μm) was too short and associated with serious matrix interference and was therefore not considered. As a result, Shim-pack GIST C18-AQ (2.1 mm × 150 mm, 3.0 μm) column was selected.
The mobile phase plays a pivotal role in method development, affecting the separation efficiency and retention time of the targets. By adjusting the ratio of the aqueous to organic phases, the response values and peak shapes of the targets can be further optimized. The impacts of methanol and acetonitrile as organic phases on 18 targets were compared. When methanol served as the organic phase, FLO, 6-CHL, and SUL exhibited broad peaks, low response values, and unsatisfactory peak shapes. However, when the organic phase was switched to acetonitrile, the peak shapes of these three targets significantly improved, and the response values increased by more than 50%. Simultaneously, the other 15 targets also demonstrated superior response values. Neonicotinoid insecticides are hydrophilic and stable under acidic conditions. Two types of mobile phase A were compared: 0.1% formic acid aqueous solution and 0.1% formic acid aqueous solution containing 2 mmol L−1 ammonium acetate. The inclusion of a certain concentration of ammonium acetate in the aqueous phase improved the peak shapes of DN, 6-CHL, and SUL, among others. The target chromatographic peaks were more symmetrical, and the response values were higher. Increasing the concentration of ammonium acetate to 4 mmol L−1 did not significantly affect DN, 6-CHL, and SUL, but it did suppress the response signals of DNT and THCP.
Overall, a Shim-pack GIST C18-AQ (2.1 mm × 150 mm, 3.0 μm) column plus mobile phases (A (H2O + 0.1% formic acid +2 mM ammonium acetate) and B (acetonitrile) were used for chromatographic separation.
Optimization of the extraction solvent
Three different solvents (methanol, acetonitrile, ethyl acetate) were examined to obtain satisfactory recoveries of 18 neonicotinoids and their metabolites in three meat matrices: pork, duck, and yellow croaker. The experiment was conducted as described in sample preparation section, but without a clean-up step. As shown in Table S1, methanol demonstrated lower recovery rates of 5-OH-IMI in pork when compared to acetonitrile and ethyl acetate, while acetonitrile and ethyl acetate exhibited minimal differences in the recovery rates of the 18 neonicotinoids (ranging between 28.6 and 67.7%). However, acetonitrile proved more effective than ethyl acetate in the removal of lipids and pigments. Consequently, acetonitrile turns out to be a more sensible choice as an extraction solvent.
It was observed that by incorporating a certain proportion of water to acetonitrile, the recovery could be further enhanced [28, 29]. In this study, recoveries were compared under four ratios of acetonitrile to water (V:V) – 4:0, 4:1, 4:2, and 4:3. The highest extraction recoveries, ranging from 47.3 to 70.5%, were found at a ratio of 4:2. The influence of acetic acid on recoveries was also evaluated, and the addition of acetic acid increased the recoveries of the 18 targets. Optimal extraction was achieved when the amount of acetic acid added reached 0.4%, with recoveries ranging from 62.1 to 94.2%. Furthermore, the impact of ultrasonic extraction time on recovery was tested at intervals of 5, 10, 15, and 20 min. The results indicated that the average recovery increased and reached an optimum value with an ultrasonic time of 15 min, ranging from 60.3 to 98.4%, as depicted in Table S2.
Evaluation of the sample treatment procedures
Before mass spectrometric detection, the concentrated analyte must be redissolved by adding a solvent. The choice of solvent can affect peak shape and mass spectrometry response. Our findings indicate that using neat acetonitrile as the solvent results in poor peak shapes with the appearance of hump and trailing peaks, and low response values. This issue can be alleviated by adding a certain proportion of water to acetonitrile. We have experimented with the ratios of acetonitrile to water at 0:10, 1:9, 2:8, 3:7, and 4:6, and found that the highest response value for each target was achieved when the ratio was 2:8. In Addition, it's crucial to consider that after the dispersive solid phase extraction, a certain volume of the solvent has to be provided for concentration. Our results indicate that the volume of this solvent can affect the recovery rate. We compared the effects of 2 mL, 4 mL, and 6 mL of purified solution and found that the optimal recovery rate for each analyte was achieved with a volume of 4 mL. Interestingly, increasing the volume to 6 mL resulted in a decrease in recovery (Table S3). By integrating the above conditions, the final method was able to achieve recovery rates ranging from 59.2 to 103.8%.
The performance of the different sorbents proposed in the matrices studied (prok, duck, and yellow croaker) was evaluated based on the recovery rate (Table S4). In the pork sample, the recovery rates of the 18 targets using the SHIMSEN QuEChERS ranged from 62.7 to 101.9%, exhibiting slightly better recovery for each target compared to the other four sorbents. The typical QuEChERS (AOAC) yielded recovery rates less than 60% for four insecticides, such as UF, DM-CLO, CYC, and IMIT, with overall recovery spanning from 54.6 to 98.1%. The EMR-Lipid demonstrated recovery rates less than 60% for two insecticides, DM-CLO and DM-ACE, with overall recovery rates ranging from 53.5 to 97.7%. The Clean-up LPAS only achieved a 43.2% recovery for IMIT, and the QuE Z-Sep/C18 only achieved a recovery of 44.1% for UF. However, the recovery rates for the remaining 16 compounds were comparable to those achieved by the three sorbents mentioned above. In duck and yellow croaker samples, the recovery of the 18 neonicotinoids were not significantly different from those in pork, with SHIMSEN QuEChERS again demonstrating superior performance, yielding recovery rates between 63.6 to 91.7% and 63.3–101.7%, respectively. Based on comprehensive consideration of recovery rate and cost-effectiveness, SHIMSEN QuEChERS was selected for sample purification in subsequent experiments.
Matrix effects (ME)
The slope of the solvent calibration curve and the matrix-matched blank extract calibration curve were used to determine the ME, according to the equation: ME (%) = [(slope of matrix-matched calibration curve - slope of solvent standard calibration curve)/slope of solvent standard calibration curve] × 100. This study evaluated the matrix effects of 18 neonicotinoids and their metabolites in three kinds of meat: pork (belly and rear leg), duck (breast and leg), and yellow croaker. As illustrated in Table S5, the highest ME value was assigned to pork belly, and the lowest was duck breast. Among the targets, NTP, 5-OH-IMI, DM-CLO, CYC, IMI, CLO, and ACE were minimally influenced by matrix effects. However, six others, such as DN, FLO, DNT, 6-CHL, THM, and SUL, exhibited matrix effects exceeding 100%, indicating significant matrix interference. Therefore, to ensure the accuracy of the quantification of neonicotinoid insecticides and their metabolites in meat products, it is advisable to calibrate the detection samples using matrix-matched standard curves.
Linearity, limit of detection (LOD), and limit of quantification (LOQ)
A matrix-matched standard solution (pork belly) was used to test the linearity of each insecticide. Calibration curves were constructed by plotting the insecticide peak area ratios against the concentration of the corresponding calibration standards at several different levels (0.10–300 ug L−1) (6 replicates per level). For all 18 analytes, regression lines with coefficient of determination (r2) above 0.9952 were obtained as shown in Table 2. LODs and LOQs of the present method correspond to the signal-to-noise ratios of 3 and 10, respectively.
Linear range, correlation coefficient, matrix-matched calibration curves, LOD and LOQ of 20 insecticides
| Analytes | Linear range (μg L−1) | Calibration equation | (r2) | LOD (μg kg−1) | LOQ (μg kg−1) |
| DN | 1.0–100 | Y = 2.3665 × 104X −3.9710 × 104 | 0.99757 | 0.40 | 1.0 |
| IM-1-4 | 0.10–100 | Y = 1.6827 × 104X −2.3972 × 104 | 0.99659 | 0.03 | 0.10 |
| UF | 0.10–100 | Y = 3.8215 × 104X −5.8217 × 104 | 0.99521 | 0.04 | 0.10 |
| FLO | 0.20–300 | Y = 6.4000 × 104X +2.5774 × 105 | 0.99826 | 0.07 | 0.20 |
| DNT | 0.20–300 | Y = 1.5760 × 104X +1.5134 × 104 | 0.99917 | 0.07 | 0.20 |
| NTP | 0.20–300 | Y = 3.675 × 103X +3.203 × 103 | 0.99929 | 0.05 | 0.20 |
| 6-CHL | 2.0–200 | Y = 9.2295 × 104X +1.6524 × 104 | 0.99914 | 1.0 | 2.0 |
| THM | 0.10–300 | Y = 7.1170 × 104X −1.3022 × 105 | 0.99994 | 0.04 | 0.10 |
| 5-OH-IMI | 0.10–300 | Y = 5.0962 × 104X −3.9062 × 104 | 0.99921 | 0.04 | 0.10 |
| THCP | 0.10–200 | Y = 3.6665 × 104X −9.0354 × 104 | 0.99936 | 0.03 | 0.10 |
| DM-CLO | 0.10–200 | Y = 5.1780 × 104X −1.7295 × 104 | 0.99863 | 0.04 | 0.10 |
| CYC | 0.20–300 | Y = 7.013 × 103X −3.4808 × 104 | 0.99942 | 0.06 | 0.20 |
| CLO | 0.20–300 | Y = 1.0424 × 104X −1.4925 × 104 | 0.99957 | 0.10 | 0.20 |
| IMI | 0.10–200 | Y = 3.0728 × 104X +1.1508 × 104 | 0.99976 | 0.04 | 0.10 |
| DM-ACE | 0.10–300 | Y = 5.6864 × 104X +8.8938 × 104 | 0.99954 | 0.05 | 0.10 |
| IMIT | 1.0–300 | Y = 1.9571 × 104X +1.0125 × 104 | 0.99966 | 0.40 | 1.0 |
| ACE | 0.15–200 | Y = 4.7269 × 104X +3.4121 × 104 | 0.99946 | 0.04 | 0.15 |
| SUL | 0.15–300 | Y = 1.9285 × 104X −1.1101 × 104 | 0.99922 | 0.04 | 0.15 |
| DN | 1.0–100 | Y = 2.3665 × 104X −3.9710 × 104 | 0.99757 | 0.40 | 1.0 |
| IM-1-4 | 0.10–100 | Y = 1.6827 × 104X −2.3972 × 104 | 0.99659 | 0.03 | 0.10 |
LODs for the 18 targets ranged from 0.04 to 1.0 μg kg−1, and the LOQs varied from 0.10 to 2.0 μg kg−1. These ranges are sufficient to meet the demands of both qualitative and quantitative analyses.
Accuracy and precision
A recovery rate experiment was conducted to test the method's accuracy. Blank samples (pork belly, duck breast, and yellow croaker) were spiked with three levels (2.0, 4.0, and 20.0 μg kg−1) following 6 replications to determine the recovery rate. The method's precision was calculated as the relative standard deviation (RSD) of the six samples spiked at three concentration levels. As can be seen in Table 3, the intraday average recovery of all pesticide ranges from 63.4 to 114.2% (pork belly) (RSD: 4.27–11.4%), 63.0–113.2% (duck breast) (RSD: 3.12–11.8%), and 63.9–110.5% (yellow croaker) (RSD: 3.58–11.2%). The interday recoveries were from 61.3 to 108.9% (pork belly) (RSD: 3.14–11.7%), 64.8–113.1% (duck breast) (RSD: 3.80–11.8%), and 60.5–109.0% (yellow croaker) (Tables 3 and 4). Based on the requirements for validation parameters of analytical method for validated residue detection matrices in the USA, EU, and China (Table 5), the results mentioned above indicate the method's efficiency, and reliability, as it can be used to identify 18 neonicotinoids and their metabolites in meat products simultaneously. Typical multiple reaction monitoring (MRM) chromatograms of a spiked sample are shown in Fig. 2.
Recovery rates of all target compounds spiked in three different matrices at three different concentrations (Intraday n = 6)
| Analyte | Pork | Duck | Yellow croaker | |||||||||||||||
| 2.0 μg kg−1 | 4.0 μg kg−1 | 20 μg kg−1 | 2.0 μg kg−1 | 4.0 μg kg−1 | 20 μg kg−1 | 2.0 μg kg−1 | 4.0 μg kg−1 | 20 μg kg−1 | ||||||||||
| Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | |
| DN | 87.9 | 6.48 | 81.2 | 7.21 | 81.7 | 5.67 | 80.3 | 5.16 | 79.9 | 8.34 | 81.9 | 7.26 | 78.0 | 9.76 | 82.0 | 10.68 | 85.4 | 7.67 |
| IM-1-4 | 91.0 | 5.98 | 90.0 | 7.83 | 94.8 | 6.04 | 97.7 | 7.50 | 91.2 | 3.39 | 96.9 | 5.47 | 93.0 | 10.7 | 93.1 | 5.20 | 93.5 | 6.14 |
| UF | 73.6 | 9.31 | 69.2 | 6.83 | 71.2 | 3.57 | 75.8 | 5.48 | 70.0 | 7.29 | 98.8 | 4.90 | 73.9 | 11.2 | 70.8 | 5.03 | 72.1 | 7.83 |
| FLO | 88.3 | 8.46 | 85.4 | 3.76 | 87.4 | 9.37 | 88.3 | 10.2 | 86.2 | 6.75 | 82.9 | 8.71 | 81.0 | 5.29 | 78.6 | 9.26 | 86.9 | 5.44 |
| DNT | 85.2 | 5.97 | 79.4 | 8.16 | 74.5 | 4.46 | 84.8 | 10.7 | 75.3 | 6.57 | 83.4 | 6.71 | 86.2 | 6.90 | 77.0 | 5.04 | 82.8 | 6.67 |
| NTP | 98.4 | 6.46 | 87.4 | 3.87 | 95.9 | 7.36 | 102.6 | 10.7 | 90.2 | 8.14 | 98.7 | 5.80 | 93.6 | 7.92 | 90.4 | 9.63 | 95.3 | 7.63 |
| 6-CHL | 90.3 | 11.4 | 81.8 | 9.44 | 79.8 | 8.14 | 85.9 | 8.41 | 82.0 | 8.35 | 84.8 | 7.66 | 83.6 | 5.87 | 84.0 | 5.51 | 82.1 | 7.40 |
| THM | 103.2 | 8.00 | 92.3 | 9.51 | 97.7 | 6.05 | 97.3 | 5.07 | 90.1 | 5.78 | 94.0 | 7.85 | 91.7 | 8.62 | 95.1 | 10.63 | 96.1 | 9.78 |
| 5-OH-IMI | 81.2 | 7.31 | 74.4 | 7.40 | 83.5 | 7.40 | 89.5 | 9.81 | 75.2 | 9.51 | 81.6 | 3.12 | 91.0 | 6.06 | 73.9 | 4.92 | 79.7 | 6.57 |
| THCP | 71.0 | 6.37 | 63.4 | 3.30 | 66.4 | 5.94 | 70.5 | 8.89 | 65.3 | 6.68 | 64.9 | 6.67 | 69.4 | 8.96 | 63.9 | 4.58 | 64.6 | 4.99 |
| DM-CLO | 69.2 | 10.0 | 67.1 | 7.49 | 71.7 | 5.64 | 70.9 | 11.8 | 72.2 | 4.72 | 74.3 | 5.64 | 79.8 | 11.0 | 71.6 | 9.03 | 71.8 | 7.22 |
| CYC | 90.7 | 7.18 | 83.2 | 10.1 | 84.1 | 6.19 | 85.2 | 10.7 | 75.9 | 7.66 | 80.3 | 5.09 | 90.2 | 8.51 | 73.2 | 8.00 | 82.4 | 5.89 |
| CLO | 114.2 | 5.07 | 104.6 | 3.48 | 106.9 | 4.27 | 113.2 | 10.7 | 102.2 | 5.51 | 107.3 | 6.46 | 105.7 | 11.2 | 107.5 | 3.94 | 110.5 | 4.94 |
| IMI | 88.7 | 6.77 | 81.1 | 5.10 | 85.9 | 7.51 | 82.7 | 6.95 | 75.3 | 10.4 | 83.7 | 5.61 | 81.8 | 9.66 | 75.7 | 9.53 | 84.4 | 3.86 |
| DM-ACE | 77.1 | 9.80 | 69.4 | 4.11 | 68.6 | 5.59 | 77.7 | 8.50 | 63.0 | 5.86 | 69.4 | 9.53 | 74.9 | 6.51 | 65.9 | 5.68 | 67.5 | 7.33 |
| IMIT | 98.4 | 8.30 | 84.5 | 5.83 | 92.9 | 7.36 | 100.5 | 6.10 | 87.3 | 5.04 | 93.6 | 4.78 | 95.9 | 7.20 | 84.9 | 4.44 | 90.3 | 3.58 |
| ACE | 67.2 | 6.35 | 68.3 | 6.99 | 67.6 | 6.18 | 76.6 | 10.9 | 65.4 | 9.85 | 70.7 | 7.46 | 68.4 | 7.23 | 66.3 | 5.34 | 69.4 | 5.70 |
| SUL | 82.2 | 7.41 | 79.9 | 6.01 | 84.3 | 6.92 | 84.4 | 5.89 | 85.0 | 5.66 | 85.0 | 5.06 | 85.9 | 10.3 | 82.1 | 5.41 | 87.2 | 7.38 |
Recovery rates of all target compounds spiked in three different matrices at three different concentrations (Interday n = 6)
| Analyte | Pork | Duck | Yellow croaker | |||||||||||||||
| 2.0 μg kg−1 | 4.0 μg kg−1 | 20 μg kg−1 | 2.0 μg kg−1 | 4.0 μg kg−1 | 20 μg kg−1 | 2.0 μg kg−1 | 4.0 μg kg−1 | 20 μg kg−1 | ||||||||||
| Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | Recovery (%) | RSD (%) | |
| DN | 87.5 | 11.7 | 77.2 | 3.47 | 79.9 | 3.49 | 83.5 | 6.83 | 75.9 | 8.66 | 80.6 | 3.89 | 78.4 | 8.22 | 81.2 | 5.95 | 84.3 | 6.99 |
| IM-1-4 | 95.2 | 5.47 | 88.2 | 8.17 | 90.0 | 3.44 | 96.6 | 8.01 | 88.2 | 8.54 | 93.4 | 6.71 | 93.0 | 8.18 | 88.1 | 9.96 | 95.0 | 5.53 |
| UF | 69.0 | 5.76 | 66.2 | 9.83 | 67.0 | 6.82 | 75.8 | 8.32 | 68.2 | 10.6 | 74.0 | 6.14 | 81.2 | 9.68 | 65.8 | 9.71 | 71.0 | 5.34 |
| FLO | 84.3 | 9.50 | 83.5 | 6.68 | 85.8 | 5.15 | 89.4 | 9.89 | 83.2 | 8.49 | 83.1 | 5.48 | 88.5 | 7.15 | 83.1 | 7.48 | 81.7 | 4.79 |
| DNT | 84.2 | 9.60 | 76.2 | 3.85 | 74.8 | 3.86 | 80.8 | 5.04 | 74.1 | 10.9 | 80.9 | 4.57 | 76.7 | 7.16 | 81.0 | 6.71 | 78.7 | 7.14 |
| NTP | 93.0 | 5.15 | 96.1 | 6.24 | 97.7 | 5.28 | 100.9 | 10.3 | 88.3 | 9.94 | 99.8 | 7.82 | 94.5 | 8.51 | 95.7 | 10.8 | 93.6 | 4.98 |
| 6-CHL | 82.3 | 6.21 | 74.6 | 10.7 | 80.3 | 7.05 | 87.0 | 10.9 | 75.5 | 7.92 | 81.6 | 3.80 | 80.8 | 8.57 | 81.8 | 7.53 | 81.4 | 5.39 |
| THM | 94.9 | 6.61 | 91.3 | 9.67 | 94.7 | 3.14 | 91.9 | 8.93 | 87.7 | 10.0 | 91.4 | 4.51 | 94.3 | 8.53 | 87.1 | 3.85 | 98.2 | 4.93 |
| 5-OH-IMI | 78.6 | 6.82 | 74.4 | 5.34 | 79.1 | 6.24 | 88.4 | 7.81 | 73.6 | 10.9 | 82.0 | 5.85 | 89.6 | 11.4 | 81.4 | 4.44 | 82.2 | 4.48 |
| THCP | 64.5 | 6.98 | 62.8 | 5.20 | 61.3 | 7.81 | 68.7 | 7.88 | 64.8 | 5.33 | 65.8 | 4.55 | 61.2 | 10.9 | 58.9 | 3.67 | 67.6 | 6.50 |
| DM-CLO | 80.3 | 11.4 | 65.4 | 6.08 | 73.8 | 3.06 | 78.6 | 6.63 | 68.6 | 8.96 | 68.5 | 7.99 | 75.6 | 7.50 | 66.7 | 6.66 | 72.2 | 4.17 |
| CYC | 81.8 | 8.02 | 81.1 | 8.93 | 80.4 | 4.61 | 80.5 | 9.37 | 77.7 | 9.17 | 82.3 | 4.26 | 81.0 | 10.0 | 79.3 | 8.27 | 79.4 | 3.17 |
| CLO | 106.4 | 6.16 | 108.5 | 10.1 | 108.9 | 5.32 | 113.1 | 8.78 | 98.8 | 3.30 | 108.5 | 7.20 | 106.1 | 6.54 | 103.9 | 10.0 | 109.0 | 4.44 |
| IMI | 80.7 | 10.8 | 76.4 | 8.06 | 83.9 | 4.68 | 90.1 | 8.92 | 79.4 | 8.80 | 84.7 | 6.34 | 89.8 | 8.71 | 76.7 | 8.09 | 85.6 | 6.47 |
| DM-ACE | 69.5 | 5.17 | 68.0 | 6.92 | 66.1 | 5.48 | 68.5 | 10.1 | 67.1 | 7.73 | 67.7 | 4.88 | 74.5 | 7.50 | 60.5 | 10.3 | 68.4 | 6.15 |
| IMIT | 91.2 | 11.7 | 88.0 | 5.76 | 87.8 | 5.37 | 98.9 | 11.8 | 89.7 | 4.46 | 95.9 | 7.05 | 91.4 | 9.27 | 84.4 | 4.11 | 92.2 | 6.81 |
| ACE | 77.8 | 9.26 | 63.6 | 8.31 | 69.0 | 6.30 | 65.2 | 8.51 | 65.5 | 4.73 | 65.4 | 7.40 | 69.4 | 11.3 | 66.1 | 10.6 | 70.0 | 5.96 |
| SUL | 94.1 | 9.71 | 78.0 | 4.40 | 82.7 | 7.07 | 83.2 | 7.79 | 84.7 | 6.98 | 87.5 | 4.33 | 83.5 | 8.10 | 87.4 | 5.66 | 90.1 | 4.07 |
Requirements for validation parameters of analytical method for validated residue detection matrices in the USA, EU, and China
| Country | Content of target compound (ug kg−1) | Recovery (%) | Precision | Linearity | Ref. |
| China | <0.1 | 60–120 | Repeatabilities (r) and Reproducibility (R) ≤15%, when target compound is less than 100 ug kg−1 | ≥0.99 | [30] |
| 0.1–1 | 80–110 | ||||
| 1–100 | 90–110 | ||||
| >100 | 95–105 | ||||
| EU | / | 60–140 | Repeatabilities (r) ≤20% Reproducibility (R) ≤20% | Deviation of back-calculated concentration from true concentration ≤± 20% | [31] |
| AOAC (USA) | / | 70–120 | Repeatabilities (r) <15% Reproducibility (R) <25% | >0.995 | [27] |
| EU | ≤0.01 | 60–120 | RSD≤30% | The analytical calibration must cover at least the range which is suitable for the determination of the levels required and should range from 30% of the LOQ to 20% above the highest level | [32] |
| >0.01–≤0.1 | 70–120 | RSD≤20% | |||
| >0.01–≤1.0 | 70–110 | RSD≤15% | |||
| >1 | 70–110 | RSD≤10% |
Chromatograms of 18 kinds of neonicotinoid pesticides and its metabolites in pork (2.0 μg kg−1) (1-18 (DN, IM-1-4, UF, FLO, DNT, NTP, 6-CHL, THM, 5-OH-IMI, THCP, DM-CLO, CYC, CLO, IMI, DM-ACE, IMIT, ACE, SUL))
Citation: Acta Chromatographica 2024; 10.1556/1326.2023.01170
Real sample analysis and comparison with other methods
Thirty-seven samples, consisting of pork belly (10 slices), pork rear leg (10 slices), yellow croaker (9 slices), and duck breast (8 slices), were obtained from a local supermarket and subjected to the established method. The DNT was detected in one yellow croaker sample at a concentration of 14.5 μg kg−1. However, according to the Chinese GB2763-2021 “National Food Safety Standard Maximum Residue Limits of Pesticides in Food” [33], DNT in the tested sample did not exceed the allowable limit. The established method was also applied to compare with 2 Chinese standard methods [34, 35] and 2 published reports [36, 37], as shown in Table 6. It was found that the SPE methods reported in these literatures had low throughput and were incapable of adsorbing and enriching all 18 neonicotinoid insecticides investigated in this study. For instance, CYC, DM-CLO, and SUL were not retained on the HLB column. Among the methods reported, most were designed to detect only one or two targets. Liu et al [35]. extended this to eight neonicotinoids, but it is still insufficient to achieve the comprehensive range of neonicotinoid insecticides examined in this study. The results obtained using the method developed by our team showed no significant deviation from the actual values reported by the established method. This indicates that our method exhibits good sensitivity and stability, making it suitable for routine analysis requirements.
Comparation with other methods
| Instrument | Pre-treatment methods | Compound | Matrices | Recovery rate (%) | LOQ (μg·kg−1) | Real sample (μg·kg−1) | Ref. |
| HPLC–MS/MS | C18+GCB | THM, CLO | chicken liver, pork | 70.2–107.6 | 10, 10 | / | [34] |
| HPLC–MS/MS | SPE Envi-Carb/LC-NH2 | DNT | pork, Chum salmon | 70.8–87.9 | 10 | 14.2 | [35] |
| HPLC-MS/MS | SPE HLB cartridge | NTP, THM, THCP, DNT, ACE, CLO, IMI | chicken, pork | 59.2–134 | 0.23–2.4 | 14.5 | [36] |
| HPLC–MS/MS | Z-sep+ | DNT, DN, UF | pork | 75–91 | 1, 10, 1 | 14.3 | [37] |
| HPLC–MS/MS | QuEChERS | 11 neonicotinoids and 7 metabolites | pork, duck, yellow croaker | 63.4–114.2 | 0.10–2.0 | 14.5 | This work |
Conclusion
This study aims to establish a highly precise and reproducible method for the quantification of 18 neonicotinoid pesticides and their metabolites in meat products. To improve the cleanup process for multiresidue analysis of neonicotinoid pesticides in fatty matrices using HPLC-MS/MS, we evaluated five different clean-up materials across three meat matrices: pork, duck, and yellow croaker. Comparative analysis of recovery data for the 18 neonicotinoids and their metabolites revealed that the Shimadzu SHIMSEN QuEChERS slightly outperformed the other four purification materials. After optimizing the mass spectrometry parameters, extraction solvents, and extraction methods, the developed method demonstrated satisfactory recovery and precision. When applied to real sample analysis, the method detected the presence of certain neonicotinoid pesticides in fish, underscoring the importance of rigorous safety testing for meat products.
Author contributions
Kai Li and Liqiang Guo contributed equally to this work. Project management: Kai Li; experiment and analysis: Liqiang Guo, Guoning Tian, Xiaojie Xu, Yajing Li; writing manuscript: Kai Li; revising: Kai Li; final approval: all authors.
Funding
This work was financially supported by General Administration of Customs P. R. China, grant number (2022HK030, 2021HK200), Weifang Science and Technology Development Foundation, grant number 2020ZJ1323.
Data availability
All data generated or analyzed during this study are included in this published article.
Ethical approval
This article does not contain any studies with human participants or animals performed by the authors.
Informed consent
Informed consent is not applicable.
Conflicts of interest
The authors declare no competing interests.
Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1556/1326.2023.01170.
References
- 1.↑
Craddock, H. A.; Huang, D.; Turner, P. C.; Quirós-Alcalá, L.; Payne-Sturges, D. C. Trends in neonicotinoid pesticide residues in food and water in the United States, 1999–2015. Environ. Health 2019, 18(1), 1.
- 2.↑
Xu, X.; Wang, X.; Yang, Y.; Ares, I.; Martínez, M.; Lopez-Torres, B.; Martinez-Larranaga, M.-R.; Wang, X.; Anadón, A.; Martinez, M.-A. Neonicotinoids: mechanisms of systemic toxicity based on oxidative stress-mitochondrial damage. Arch. Toxicol. 2022, 96(6), 1493.
- 3.↑
Laubscher, B.; Diezi, M.; Renella, R.; Mitchell, E. A.; Aebi, A.; Mulot, M.; Glauser, G. Multiple neonicotinoids in children’s cerebro-spinal fluid, plasma, and urine. Environ. Health 2022, 21(1), 1.
- 4.↑
Mendy, A.; Pinney, S. M. Exposure to neonicotinoids and serum testosterone in men, women, and children. Environ. Toxicol. 2022, 37(6), 1521.
- 5.↑
Vuong, A. M.; Zhang, C.; Chen, A. Associations of neonicotinoids with insulin and glucose homeostasis parameters in US adults: NHANES 2015–2016. Chemosphere 2022, 286, 131642.
- 6.↑
Mahai, G.; Wan, Y.; Xia, W.; Wang, A.; Qian, X.; Li, Y.; He, Z.; Li, Y.; Xu, S. Exposure assessment of neonicotinoid insecticides and their metabolites in Chinese women during pregnancy: a longitudinal study. Sci. Total Environ. 2022, 818, 151806.
- 7.↑
Yuan, T. H.; Yu, M. T.; Ikenaka, Y.; Chen, Y. H.; Nakayama, S. F.; Chan, C. C. Characteristics of neonicotinoid and metabolite residues in Taiwanese tea leaves. J. Sci. Food Agric. 2022, 102(1), 341.
- 8.↑
Malev, O.; Klobučar, R. S.; Fabbretti, E.; Trebše, P. Comparative toxicity of imidacloprid and its transformation product 6-chloronicotinic acid to non-target aquatic organisms: Microalgae Desmodesmus subspicatus and amphipod Gammarus fossarum. Pestic. Biochem. Physiol. 2012, 104(3), 178.
- 9.↑
Thompson, D. A.; Lehmler, H.-J.; Kolpin, D. W.; Hladik, M. L.; Vargo, J. D.; Schilling, K. E.; LeFevre, G. H.; Peeples, T. L.; Poch, M. C.; LaDuca, L. E. A critical review on the potential impacts of neonicotinoid insecticide use: current knowledge of environmental fate, toxicity, and implications for human health. Enviro Sci. Process. Impacts 2020, 22(6), 1315.
- 10.↑
Ma Jie, H. Y.; Guo, L. Q.; Li, K.; Zhang, J.-L.; Xu, W. J.; Gong, X. M. Research progress on pollution and control of neonicotinoid insecticides in food. J. Food Saf. Qual. 2022, 13(1), 278.
- 11.↑
Ko, A.-Y.; Rahman, M. M.; Abd El-Aty, A.; Jang, J.; Park, J.-H.; Cho, S.-K.; Shim, J.-H. Development of a simple extraction and oxidation procedure for the residue analysis of imidacloprid and its metabolites in lettuce using gas chromatography. Food Chem. 2014, 148, 402.
- 12.↑
Mahdavi, V.; Garshasbi, Z.; Farimani, M. M.; Farhadpour, M.; Aboul‐Enein, H. Y. Health risk assessment of neonicotinoid insecticide residues in pistachio using a QuEChERS‐based method in combination with HPLC‐UV. Biomed. Chromatogr. 2020, 34(3), e4747.
- 13.↑
Rawat, K.; Srivastava, A.; Tandon, S.; Singh, G. P. Method validation for simultaneous determination of four neonicotinoids in vegetables by liquid chromatography. Anal. Sci. 2023, 39(4), 431.
- 14.↑
Guo, L.; Li, K.; Zhang, J.; Tian, G.; Wang, K.; Li, Y. Determination of 20 neonicotinoid insecticides and their metabolites in infant foods by a modified QuEChERS method combined with HPLC-MS/MS. J. Chem., 2022, 2022.
- 15.↑
Muhammad, N.; Wang, F.; Subhani, Q.; Zhao, Q.; Qadir, M. A.; Cui, H.; Zhu, Y. Comprehensive two-dimensional ion chromatography (2D-IC) coupled to a post-column photochemical fluorescence detection system for determination of neonicotinoids (imidacloprid and clothianidin) in food samples. RSC Adv. 2018, 8(17), 9277.
- 16.↑
Carbonell-Rozas, L.; Lara, F. J.; García-Campaña, A. M. Analytical methods based on liquid chromatography and capillary electrophoresis to determine neonicotinoid residues in complex matrices. A comprehensive review. Crit. Rev. Anal. Chem. 2023, 1.
- 17.↑
Li, H.; Wang, Z.; Kong, L.; Huang, B.; Xu, Y.; Hou, R. AuNPs-based lateral flow immunoassay for point-of-needs analysis of four neonicotinoids in tea samples: effects of grinding degrees, solvent types and contents on extraction efficiency. Food Chem. 2022, 397, 133790.
- 18.↑
Banerjee, K.; Oulkar, D. P.; Dasgupta, S.; Patil, S. B.; Patil, S. H.; Savant, R.; Adsule, P. G. Validation and uncertainty analysis of a multi-residue method for pesticides in grapes using ethyl acetate extraction and liquid chromatography–tandem mass spectrometry. J. Chromatogr. A. 2007, 1173(1–2), 98.
- 19.↑
Di Muccio, A.; Fidente, P.; Barbini, D. A.; Dommarco, R.; Seccia, S.; Morrica, P. Application of solid-phase extraction and liquid chromatography–mass spectrometry to the determination of neonicotinoid pesticide residues in fruit and vegetables. J. Chromatogr. A. 2006, 1108(1), 1.
- 20.
Seccia, S.; Fidente, P.; Montesano, D.; Morrica, P. Determination of neonicotinoid insecticides residues in bovine milk samples by solid-phase extraction clean-up and liquid chromatography with diode-array detection. J. Chromatogr. A. 2008, 1214(1–2), 115.
- 21.
Watanabe, E.; Baba, K.; Eun, H. Simultaneous determination of neonicotinoid insecticides in agricultural samples by solid-phase extraction cleanup and liquid chromatography equipped with diode-array detection. J. Agric. Food Chem. 2007, 55(10), 3798.
- 22.↑
Barbieri, M. V.; Postigo, C.; Guillem-Argiles, N.; Monllor-Alcaraz, L. S.; Simionato, J. I.; Stella, E.; Barceló, D.; de Alda, M. L. Analysis of 52 pesticides in fresh fish muscle by QuEChERS extraction followed by LC-MS/MS determination. Sci. Total Environ. 2019, 653, 958.
- 23.
Yao, W.; Zhang, Z.; Song, S.; Hao, X.; Xu, Y.; Han, L. Multi-residue analysis of 34 pesticides in black pepper by QuEChERS with d-SPE vs. d-SLE cleanup. Food Anal Methods 2019, 12(1), 176.
- 24.
Moreno-González, D.; Alcántara-Durán, J.; Addona, S. M.; Beneito-Cambra, M. Multi-residue pesticide analysis in virgin olive oil by nanoflow liquid chromatography high resolution mass spectrometry. J. Chromatogr. A. 2018, 1562, 27.
- 25.↑
Rejczak, T.; Tuzimski, T. Method development for sulfonylurea herbicides analysis in rapeseed oil samples by HPLC–DAD: comparison of zirconium-based sorbents and EMR-lipid for clean-up of QuEChERS extract. Food Anal Methods 2017, 10, 3666.
- 26.↑
Sun, D.; Jin, Y.; Zhao, Q.; Tang, C.; Li, Y.; Wang, H.; Qin, Y.; Zhang, J. Modified EMR-lipid method combined with HPLC-MS/MS to determine folates in egg yolks from laying hens supplemented with different amounts of folic acid. Food Chem. 2021, 337, 127767.
- 27.↑
AOAC Official Method, Pesticide Residues in Foods by Acetonitrile Extraction and Partitioning with Magnesium Sulfate, 2007, 2007 (accessed 25.01.16). AOAC 2007.
- 28.↑
Fan, X.; Tang, T.; Du, S.; Sang, N.; Huang, H.; Zhang, C.; Zhao, X. Simultaneous determination of 108 pesticide residues in three traditional Chinese medicines using a modified QuEChERS mixed sample preparation method and HPLC-MS/MS. Molecules 2022, 27(21), 7636.
- 29.↑
Li, R.-X.; Li, M.-M.; Wang, T.; Wang, T.-L.; Chen, J.-Y.; Francis, F.; Fan, B.; Kong, Z.-Q.; Dai, X.-F. Screening of pesticide residues in Traditional Chinese Medicines using modified QuEChERS sample preparation procedure and LC-MS/MS analysis. J. Chromatogr. B. 2020, 1152, 122224.
- 30.↑
GB/T 27404-2008 Criterion on quality control of laboratories-chemical testing of food. China National Standard 2008.
- 31.↑
SANTE/11312/2021; Analytical quality control and method validation procedures for pesticide residues analysis in food and feed SANTE 11312/2021. (accessed on 3 November 2022).
- 32.↑
SANTE/2020/12830, rev1; Guidance Document on Pesticide Analytical Methods for Risk Assessment and Post-approval Control and Monitoring Purposes. (24 February 2021).
- 33.↑
GB2763-2021 National food safety standard-maximum residue limits for pesticides in food. China National Standard 2021.
- 34.↑
GB 23200.39—2016 National food safety standards Determination of thiamethoxam and its metabolite clothianidin residues in foods Liquid chromatography-mass spectrometry. China National Standard 2017.
- 35.↑
GB 23200.51-2016 National food safety standards—Determination of dinotefuran residue in foods Liquid chromatography-mass spectrometry. China National Standard 2017.
- 36.↑
Liu, S.; Zheng, Z.; Wei, F.; Ren, Y.; Gui, W.; Wu, H.; Zhu, G. Simultaneous determination of seven neonicotinoid pesticide residues in food by ultraperformance liquid chromatography tandem mass spectrometry. J. Agric. Food Chem. 2010, 58(6), 3271.
- 37.↑
Zhang, Y.; Wu, X.; Duan, T.; Xu, J.; Dong, F.; Liu, X.; Li, X.; Du, P.; Zheng, Y. Ultra high performance liquid chromatography with tandem mass spectrometry method for determining dinotefuran and its main metabolites in samples of plants, animal‐derived foods, soil, and water. J. Sep. Sci. 2018, 41(14), 2913.
