Discrimination of geographical origin of extra virgin olive oils using terahertz spectroscopy combined with chemometrics

Abstract

Discrimination of geographical origin of extra-virgin olive oils (EVOOs) is of great importance for legislation and consumers worldwide. The feasibility of a rapid discrimination of four different geographical origins of EVOOs with terahertz spectroscopy system was examined. Different chemometrics including least squares-support vector machines (LS-SVM), back propagation neural network (BPNN) and random forest (RF) combined with principal component analysis (PCA), genetic algorithm (GA) were compared to obtain the best discrimination model. The results demonstrated that there were apparent differences among the four different geographical origins of EVOOs in fatty acid compositions and the absorbance spectra, and an excellent classification (accuracy was 96.25% in prediction set) could be achieved using the LS-SVM method combine with GA. It can be concluded that THz spectroscopy together with chemometrics would be a promising technique to rapid discriminate the geographical origin of EVOOs with high efficiency.