Abstract
The aim of this study was to develop a gas chromatography-mass spectrometry (GC-MS)-based metabolomics method to distinguish different kinds of poisons in the blood. We examined the changes in blood metabolites using GC-MS following administration of four different poisons (paraquat, dichlorvos, aconitine, and sodium nitrite). The data were analyzed with orthogonal partial least squares. Then, total and single differential metabolite profiles were evaluated with support vector machine (SVM) models. The results showed that various metabolites (5-ketone proline, 1,5-anhydrohexitol, lactic acid, glycine 2,2-furoic acid, and 3-hydroxybutyric acid) were differential between the experimental groups and the control groups. The accuracy rates of the SVM models established using total and single differential metabolites were 80% and 100%, respectively. In conclusion, we successfully developed a poison screening method. The established SVM models can distinguish four kinds of poisons and could be used to establish a complete poison metabonomic information database. Furthermore, some of the metabolites could be biomarkers of these poisons. Finally, both the models and potential biomarkers may reduce the time required for poison detection and provide direction for solving cases and auxiliary diagnosis.
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