Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA)

Eva Borràs, Joan Ferré, Ricard Boqué, Montserrat Mestres, Laura Aceña, Angels Calvo, Olga Busto

iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.

Abstract

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV–visible spectrophotometry (UV–vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV–vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils.

Keywords: Virgin olive oil; Headspace-mass spectrometry (HS-MS); Mid infrared spectroscopy (MIR); UV–vis spectrophotometry; Data fusion; Multivariate analysis; Partial least squares-discriminant analysis (PLS-DA); Classification; Sensory analysis.