PDF(Pubmed)
Abstract：
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 t-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.
摘要：
由于农药和兽药（P＆VD）共存，植物衍生食品的污染风险尚未得到充分理解。随着越来越多的非法添加到食品中的意外P&VD，对于P&VDs的全面风险评估，必须开发一种非针对性的筛查方法。在这项研究中,基于高效液相色谱-串联质谱的结果,通过筛选合格变量来代表玉米中124个多类P&VDs的标记化合物,开发了一种改进的支持向量机(SVM)辅助代谢组学方法.主成分分析和正交偏最小二乘判别分析表明存在明显的组间差异,通过S-plot图进一步调查，置换测试,和变量在预测中的重要性，以获得合格的变量。同时,采用径向基函数下的SVM递归特征消除来获得从大到小的所有变量的权重平方值，以筛选合格的变量。进一步采用成对t检验和浓度的倍数变化来确认这些合格的变量以代表标记化合物。结果表明，通过SVM辅助代谢组学方法可以鉴定出124个P&VD中的120个，虽然仅通过代谢组学方法可以找到109个P&VD，这意味着支持向量机可以提高代谢组学方法的筛选准确性。此外,通过真实的污染玉米样品验证了该方法的实用性，在污染物的非靶向筛选方面具有广阔的应用前景。经计算，玉米样品中120个P&VDs的检出限为0.3~1.5μg/kg。
