A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize

• Published in: Molecules (Basel, Switzerland) Vol. 29; no. 13; p. 3026
• Main Authors: Xue, Weifeng, Li, Fang, Li, Xuemei, Liu, Ying
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.06.2024
• Subjects: Chromatography, High Pressure Liquid - methods; Cluster analysis; Defective products; Drugs; Food contamination & poisoning; Food Contamination - analysis; Food safety; maize; marker compounds; metabolomics; Metabolomics - methods; non-targeted screening; Pesticides; Pesticides - analysis; pesticides and veterinary drugs; Principal Component Analysis; Support Vector Machine; Support vector machines; Tandem Mass Spectrometry - methods; Veterinary Drugs - analysis; Zea mays - chemistry; support vector machine; metabolomics; maize; pesticides and veterinary drugs; marker compounds; non-targeted screening
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules29133026

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise -tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.