Journal
JOURNAL OF CHROMATOGRAPHY A
Volume 1188, Issue 2, Pages 308-313Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chroma.2008.02.046
Keywords
virgin olive oil; sensory attributes; instrumental classification and quantification; headspace-mass spectrometry; decision tree; chemometric tools
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A methodology for obtaining reliable qualitative and quantitative information about negative (fusty, muddy sediment, musty, rancid and vinegary) and positive (fruity) sensory attributes of virgin olive oils (lampante and extra) has been developed. The procedure implies the joint use of a headspace autosampler, a mass spectrometer and an adequate chemometric data treatment. For this purpose, soft independent modelling of class analogy (SIMCA) and partial least squares (PLS) regression approaches were used for attribute identification and quantification, respectively. InStep application was employed to generate a decision tree by the combination of both models in order to provide the joint prediction of the sensory attributes of a given virgin olive oil and their respective intensities by means of a single output result. The good prediction results obtained when the decision tree generated were applied to a new set of virgin olive oil samples (viz, a specificity of 100%, an average sensitivity of 86% and a RMSEP < 0.8% in the quantification task) reveals its potential applicability in routine analysis. (C) 2008 Elsevier B.V. All rights reserved.
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