This study (purchase required) reports a flow injection mass spectrometric (FIMS) fingerprinting approach, measuring a pattern of triglycerides and fatty acids, to discriminate olive oil adulterated with cheaper vegetable oils. The authors proposed SVM and PLS classification and regression models for the identification and quantitative analysis of olive oil adulteration. They reported that SVM outperformed PLS-DA, achieving higher values for accuracy, sensitivity, and specificity, as well as positive predictive and negative predictive values in identifying adulterated olive oil samples. Compared with a PLSR model, the SVR model demonstrated superior performance in determining the content of adulterated olive oil, with a higher coefficient of determination and lower Root Mean Square Error. They conclude that FIMS fingerprinting technology in combination with SVM can be effectively implemented for rapid, reliable, and accurate identification and quantification of olive oil adulteration.
