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A method was developed to analyze residual pesticides in various vegetables using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS). A method can analyze over 200 individual compounds, selectively separate peaks within 30 min, and meet various criteria such as those by the Codex Alimentarius Commission (CODEX). Most compounds showed recovery rates between 70 and 120%, and a relative standard deviation was within 20%. Measurement uncertainty, considering various influencing factors such as instrument precision, method sensitivity, experimental conditions, sample handling, and analytical procedures, was meticulously calculated. The expanded uncertainties of cross-checking 33 available pesticides ranged from 10.1 to 26.2 μg/kg at a confidence level of approximately 95%. Risk assessment of detected pesticides in agricultural products indicated a safety range of 0.00003-2.87240%. The developed method effectively analyzes diverse compounds simultaneously, contributing to agricultural product safety.

Geographical origin is an important determinant of agricultural product quality and safety. Herein, inductively coupled plasma (ICP) analysis was applied to determine the inorganic elemental content of onions and identify their geographical origin (Korean or Chinese). Chemometric, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to the ICP results. OPLS-DA distinguished each group, and 17 elements with variable importance in projection (VIP) values of ≥ 1 were selected. The receiver operating characteristic (ROC) curve had an area under the curve (AUC) of 1, indicating excellent discriminatory power. Differences in elemental content between groups were visually observed in a heatmap, and the country of origin was determined with 100% accuracy using canonical discriminant analysis (CDA). This method accurately distinguishes between Korean and Chinese onions and is expected to be beneficial for identifying agricultural products., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Extraction using acetonitrile and water and quadrupole time-of-flight mass spectrometry (LC and GC-QTOF/MS) techniques were used to screen for potential hazardous substances in livestock and pet hair. In addition, LC-MS/MS and GC-MS/MS techniques were used for verification of the analytical method and quantitative analysis of pesticides, veterinary drugs, mycotoxins and antioxidants in hair. Optimized sample preparation involves extracting 0.05 g of sample with 0.6 mL of ACN and 0.4 mL of distilled water. In addition, the two layers were separated by adding 0.1 g of NaCl. Then, both the ACN and water layers were analyzed by LC-TOF/MS, and the ACN layer was analyzed by GC-TOF/MS. Most of the matrix effects of livestock and pet hair were less than 50%, but some matrices and components showed high results, so matrix matching correction was applied for more precise quantification. Method validation was performed for 394 constituents (293 pesticides, 93 veterinary drugs, 6 mycotoxins and 2 preservatives) in dog, cat, cow and pig hair and chicken and duck feathers. All components showed good linearity (r2 ≥0.98) in the developed assay. The quantification limit of all compounds was set at 0.02 mg/kg, which is the lowest level that satisfies the recovery rate standard. The recovery experiment was repeated 8 times at 3 concentrations. Most of the components were extracted with the ACN layer, and the recovery rate was 63.35-119.98%. In order to confirm the efficiency of extracting harmful substances from actual samples, 30 hairs of livestock and pets were screened., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Modified QuEChERS and triple quadrupole mass spectrometry (LC and GC-MS/MS) technology were used to sequentially analyze pesticides, veterinary drugs, and mycotoxins in feed. In order to analyze the harmful substances that may remain or occur in the feed, we performed optimization experiments for sample preparation and LC-MS/MS and GC-MS/MS conditions. Optimized sample preparation involves extracting 5 g of sample with 15 mL of 0.25 M EDTA and 10 mL of acetonitrile. And some extracts were diluted 10-fold with 100 mM ammonium formate aqueous solution and analyzed by LC-MS/MS, and some extracts were purified through 25 mg PSA and analyzed by GC-MS/MS by adding an analyte protectant. We confirmed the matrix effect of feed ingredients and compound feeds, and added a dilution process after extraction to increase on-site efficiency. Matrix-matched calibration was applied for quantification. Method validation was performed for 197 pesticides, 56 components for veterinary drugs, and 5 components for toxins. All the components showed good linearity (r2 ≥ 0.98) in the developed analytical method. For most compounds, the limit of quantitation was 0.05 mg/kg. The recovery rate experiment was repeated three times at three concentrations including LOQ in feed ingredient, compound feed for livestock, and compound feed for pets. The recovery rate was 70.09-119.76% and relative standard deviations were ≤ 18.91%. And the accuracy and precision were further verified through cross-validation between laboratories. The developed analytical method was used to monitor 414 domestically distributed and imported feeds., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)