Comparison of four classification statistical methods for characterising virgin olive oil quality during storage up to 18 months

Abstract

This study examines the ability of fluorescence spectroscopy for monitoring the quality of 70 Moroccan virgin olive oils belonging to three varieties and originating from three regions of Morocco. By applying principal component analysis and factorial discriminant analysis to the emission spectra acquired after excitation wavelengths set at 270, 290, and 430 nm, a clear differentiation between samples according to their storage time was observed. The obtained results were confirmed following the application of four multivariate classification methods: partial least squares regression, principal component regression, support vector machine, and multiple linear regression on the emission spectra. The best prediction model of storage time was obtained by applying partial least squares regression since a coefficient of determination (R²) and a root mean square error of prediction (RMSEP) of 0.98 and 24.85 days were observed, respectively. The prediction of the chemical parameters allowed to obtain excellent validation models with R² ranging between 0.98 and 0.99 for free acidity, peroxide value, chlorophyll level, k₂₃₂, and k₂₇₀.

Keywords: Chemometric; Fluorescence spectroscopy; Freshness; Storage; Virgin olive oil.