title: Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).
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.
authors.affiliation: iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
authors.forename: Eva
authors.initials: E
authors.lastname: Borràs
authors.affiliation: Chemometrics, Qualimetrics and Nanosensors Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
authors.forename: Joan
authors.initials: J
authors.lastname: Ferré
authors.affiliation: Chemometrics, Qualimetrics and Nanosensors Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
authors.forename: Ricard
authors.initials: R
authors.lastname: Boqué
authors.affiliation: iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
authors.forename: Montserrat
authors.initials: M
authors.lastname: Mestres
authors.affiliation: iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain.
authors.forename: Laura
authors.initials: L
authors.lastname: Aceña
authors.affiliation: Official Taste Panel of Virgin Olive Oil in Catalonia, Reus, Spain.
authors.forename: Angels
authors.initials: A
authors.lastname: Calvo
authors.affiliation: iSens Group, Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain. Electronic address: olga.busto@urv.cat.
authors.forename: Olga
authors.initials: O
authors.lastname: Busto
mesh_terms: D016002:Discriminant Analysis; D005504:Food Analysis / Q000295:instrumentation* / Q000379:methods*; D063427:Food Quality; D016018:Least-Squares Analysis; D013058:Mass Spectrometry / Q000379:methods; D015999:Multivariate Analysis; D009812:Odorants / Q000032:analysis*; D000069463:Olive Oil / Q000145:classification* / Q000592:standards; D013055:Spectrophotometry, Infrared / Q000379:methods; D013056:Spectrophotometry, Ultraviolet / Q000379:methods
chemical_list: D000069463:Olive Oil
keywords: Classification; Data fusion; Headspace-mass spectrometry (HS-MS); Mid infrared spectroscopy (MIR); Multivariate analysis; Partial least squares-discriminant analysis (PLS-DA); Sensory analysis; UV–vis spectrophotometry; Virgin olive oil