title: Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression.
abstract: Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors.
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: D016018:Least-Squares Analysis; D013058:Mass Spectrometry; D000069463:Olive Oil / Q000737:chemistry*; D013056:Spectrophotometry, Ultraviolet; D017550:Spectroscopy, Fourier Transform Infrared; D013649:Taste
chemical_list: D000069463:Olive Oil
keywords: Data fusion; Electronic panel; MIR spectroscopy; Mass spectrometry; Multivariate analysis; Olive oil; Partial least squares (PLS) Regression; Sensory evaluation; UV–vis spectrophotometry