Qualitative and quantitative studies of plasticizers in extra virgin olive oil by surface-enhanced Raman spectroscopy combined with chemometrics

Abstract

Plasticizers are widely used as significant adjuvants in the plastics processing industry and have received widespread attention from the international community because of their reproductive toxicity. This study used a combination of surface-enhanced Raman spectroscopy (SERS) technology and chemometrics to conduct qualitative and quantitative analyses of plasticizers in extra virgin olive oil (EVOO). Among three established classification models, namely, partial least squares discriminant analysis (PLS-DA), k-nearest neighbours (KNN), and support vector machine (SVM), SVM was found to be the best classification model, with a correct classification rate (CCR) of 100 %. For the plasticizer concentration prediction model established by partial least squares (PLS), the absolute coefficient Rp2 was higher than 0.97, the root mean square error of prediction (RMSEP) was less than 3.632, and the ratio of performance to deviation (RPD) values were all greater than 5. Furthermore, the proposed method was validated by a standard gas chromatography-mass spectrometry (GC-MS) method using t-test with no significant difference. These results indicate that SERS combined with chemometrics can achieve the identification and quantification of plasticizers in EVOO and has application value for the analysis of plasticizers in edible oils.