期刊
FOOD CHEMISTRY
卷 309, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2019.125669
关键词
Extra virgin olive oil; Thermal oxidation; LIF spectroscopy; KPCA-LDA; Dimensionality reduction
资金
- National Natural Science Foundation of China [61505009]
The fluorescence spectra of oil samples were obtained by laser-induced fluorescence spectroscopy and thermal oxidation stoichiometry at room temperature and 80 degrees C respectively. The Support Vector Machine, combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), could distinguish pure extra virgin olive oils (EVOO) from oils adulterated with 2% soybean oil, with a recognition rate of 100%. Besides, as the intensity of the fluorescence spectra and concentration of the adulterants showed a non-linear relationship, linear dimension reduction methods may lead to overlapping of the different adulterated concentrations features, resulting in large errors in quantifying adulteration. In this paper, Kernel Principal Component AnalysisLinear Discriminant Analysis (KPCA-LDA) was applied instead of PCA-LDA to extract fluorescence spectra features, and a Partial Least Squares Regression model was established, which could quantify adulterants such as low percentages of soybean oil in EVOO. The coefficient of determination and root mean squared error were 0.92 and 2.72%, respectively.
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