Multivariate adulteration detection for sesame oil

- Multivariate adulteration detection for sesame oil by one-class support vector machine.
- Lowest adulteration levels of the OC-SVM model were calculated.
- One-class model is promising tool to identify authenticity of edible oil and food.

Multivariate and untargeted adulterations are real cases of oil adulteration in practice. In this study, one-class support vector machine (OC-SVM) was used to build the model for detecting multivariate and untargeted adulterations of sesame oil. The predictive model was subsequently validated by an independent test set. The results indicated that the OC-SVM model could completely detect the adulterated oils. Moreover, oils adulterated with different levels of mixed edible oils were simulated by Monte Carlo method and employed to determine the lowest adulteration level of the predictive model. Compared with earlier studies, the OC-SVM model proposed for sesame oil in this study is more robust to detect untargeted and multivariate adulteration.

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