2020
DOI: 10.1007/s12161-020-01731-5
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Analysis of Vegetable Oil from Different Suppliers by Chemometric Techniques to Ensure Correct Classification of Oil Sources to Deal with Counterfeiting

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Cited by 12 publications
(3 citation statements)
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“…The chemometric strategies and methods for spectroscopy-based food authentication have been reviewed [17,18]. The common chemometric qualitative and classification non-supervised approaches include principal component analysis (PCA), cluster analysis (CA), or soft independent modeling of class analogy (SIMCA), while the supervised methods include partial least squares (PLS) with discriminant Analysis (PLSDA), linear discriminating analysis (LDA) and principal component regression (PCR) [19][20][21]. The chemometric algorithm used in PCA and PLS has been extensively used to obtain different quality parameters of edible oils [16,22,23] and applied to the evaluation of fatty acids composition and other quality parameters of virgin olive oil [4,24].…”
Section: Introductionmentioning
confidence: 99%
“…The chemometric strategies and methods for spectroscopy-based food authentication have been reviewed [17,18]. The common chemometric qualitative and classification non-supervised approaches include principal component analysis (PCA), cluster analysis (CA), or soft independent modeling of class analogy (SIMCA), while the supervised methods include partial least squares (PLS) with discriminant Analysis (PLSDA), linear discriminating analysis (LDA) and principal component regression (PCR) [19][20][21]. The chemometric algorithm used in PCA and PLS has been extensively used to obtain different quality parameters of edible oils [16,22,23] and applied to the evaluation of fatty acids composition and other quality parameters of virgin olive oil [4,24].…”
Section: Introductionmentioning
confidence: 99%
“…The color in VORW is usually related to the pigments in the oil sources. These vegetable pigments mainly comprise carotene molecules (Godoy et al 2020). Chemical otation was tested to evaluate its effectiveness in removing color from VORW.…”
Section: Color Removalmentioning
confidence: 99%
“…Different instrumental analytical techniques provide a large number of data variables, which not only offer the opportunity to mine useful chemical information from the original datasets, but also mean that it is difficult to interpret useful chemical information through univariate analysis [ 19 ]. Particularly for complex classification tasks such as the differentiation between edible oils from diverse biological or geographical sources, the analysis needs to be comprehensively examined using multivariate analysis to improve the accuracy of the classification [ 20 ].…”
Section: Introductionmentioning
confidence: 99%