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A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
Published on: January 3, 2014
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A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and
Weifeng Xue1, Fang Li1, Xuemei Li1
1Technology Centre of Dalian Customs, Dalian 116000, China.
Molecules (Basel, Switzerland)
|July 13, 2024
Summary
A new method using support vector machine (SVM)-assisted metabolomics improves the detection of pesticides and veterinary drugs (P&VDs) in maize. This approach enhances screening accuracy for food safety risk assessment.
Area of Science:
- Food Science
- Analytical Chemistry
- Toxicology
Background:
- Pesticide and veterinary drug (P&VD) contamination in plant-derived foods poses risks that are not fully understood.
- The illegal addition of diverse P&VDs necessitates advanced non-targeted screening methods for comprehensive risk assessment.
Purpose of the Study:
- To develop and validate a modified support vector machine (SVM)-assisted metabolomics approach for non-targeted screening of 124 multi-class P&VDs in maize.
- To enhance the screening accuracy and efficiency of metabolomics methods for contaminant detection.
Main Methods:
- High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was employed for data acquisition.
- Support vector machine (SVM) recursive feature elimination and statistical analyses (PCA, OPLS-DA, t-tests) were used to screen eligible marker compounds.
- The SVM-assisted metabolomics approach was validated using real contaminated maize samples.
Main Results:
- The SVM-assisted metabolomics method successfully identified 120 out of 124 P&VDs, significantly improving upon the 109 P&VDs detected by metabolomics alone.
- The method demonstrated improved screening accuracy, with limits of detection ranging from 0.3 to 1.5 µg/kg for the identified P&VDs in maize.
- Practical validation using contaminated maize samples confirmed the method's applicability.
Conclusions:
- The developed SVM-assisted metabolomics approach effectively enhances the accuracy of non-targeted P&VD screening in maize.
- This method offers a promising tool for comprehensive risk assessment and ensuring food safety.
- The study highlights the value of integrating machine learning with metabolomics for contaminant analysis.
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