Detection and quantification of extra virgin olive oil adulteration with edible oils by FT-IR spectroscopy and chemometrics

Abstract

Detection of adulteration in extra virgin olive oil (EVOO) is one of the main aspects in the quality control. In this study, we sought to identify the adulterated oil from EVOO to discriminate the type of adulterants and to quantify the levels of adulteration using FT-IR spectroscopy coupled with chemometrics. Supervised locally linear embedding (SLLE) was employed to reduce the dimensionality of variables and then compared with principal component analysis and locally linear embedding. The results show that SLLE gave satisfactory results. Nearest centroid classification and PLS regression methods were applied to establish the classification and quantification models for EVOO adulteration using the compressed low dimensional FT-IR data. The results have shown that we can clearly identify which edible oils are adulterated and accurately quantify the percentage of adulteration in EVOO.