期刊
TALANTA
卷 155, 期 -, 页码 116-123出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.talanta.2016.04.040
关键词
Olive oil; Electronic panel; Mass spectrometry; MIR spectroscopy; UV-vis spectrophotometry; Data fusion; Sensory evaluation; Partial least squares (PLS) Regression; Multivariate analysis
资金
- Spanish Ministry of Science and Technology [AGL2011-26456]
- Universitat Rovira i Virgili
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 (PIS) 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. (C) 2016 Elsevier B.V. All rights reserved.
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