Application of IR and UV–VIS spectroscopies and multivariate analysis for the classification of waste vegetable oils

Abstract

Due to the ever-increasing worldwide interest in the exploitation of waste vegetable oils, the development of analytical tools able to detect their adulteration with edible oils, is considered a priority for the scientific and industrial community. In this work, edible and waste vegetable oils have been analysed by Fourier Transform-InfraRed (FT-IR) and Ultraviolet-Visible (UV–VIS) spectroscopies and the corresponding spectral data subjected to statistical multivariate analysis for classification purposes. In particular, Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were performed in order to develop an analytical tool which is able to distinguish between edible and waste vegetable oil. Qualitative analysis of the spectra suggested FT-IR and UV–VIS as the more suitable techniques to distinguish between wastes and edible samples. Also, statistical multivariate analysis revealed that FT-IR-based methodology is more adequate for the target, even if the elevated sensibility of the method produces an undesired distinction between edible oils of the same type. Finally, further attempts on UV–VIS data obtained in reflection mode allowed to produce a good dataset which after statistical treatment gave a clear differentiation between edible and waste oil samples.