Direct analysis of vegetable oils by atmospheric pressure laser plasma ionization combined with machine learning methods

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The atmospteric pressure laser plasma ionization (APLPI) method in combination with machine learning methods is investigated to solve the problem of classification of vegetable oils. Samples of olive oil, rapeseed oil, sunflower oil and linseed oil were studied. The samples were classified on the basis of mass spectrometric profiles of volatile organic compounds emitted by the oils. It was shown that when hierarchical cluster analysis (HCA) with pre-selection of features by analysis of variance (ANOVA) and reduction of the dimensionality of the response matrix by t-distributed stochastic neighbor embedding (t-SNE), each type of oil forms a distinct cluster. Using the example of olive and rapeseed oil blends analysis, it was demonstrated that the combination of the APLPI method with the multiple linear regression (MLR) method allows to quantify the share of oils in the studied blends. The developed approach allows for rapid, direct nondestructive analysis of vegetable oils without sample preparation and can be used for detection of adulterated products.

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作者简介

K. Kravets

V.I. Vernadsky Institute of Geochemistry and Analytical Chemistry, Russian Academy of Sciences

Email: grechnikov@geokhi.ru
俄罗斯联邦, Kosygina St., 19, Moscow119991

S. Timakova

V.I. Vernadsky Institute of Geochemistry and Analytical Chemistry, Russian Academy of Sciences

Email: grechnikov@geokhi.ru
俄罗斯联邦, Kosygina St., 19, Moscow119991

A. Grechnikov

V.I. Vernadsky Institute of Geochemistry and Analytical Chemistry, Russian Academy of Sciences

编辑信件的主要联系方式.
Email: grechnikov@geokhi.ru
俄罗斯联邦, Kosygina St., 19, Moscow119991

S. Nikiforov

A.M. Prokhorov Institute of General Physics of the Russian Academy of Sciences

Email: grechnikov@geokhi.ru
俄罗斯联邦, Vavilova St., 38, Moscow 119991

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2. Fig. 1. Schematic diagram of the APLPI ion source with a sample inlet unit: 1 – chamber; 2 – mass spectrometer inlet; 3 – metal target; 4 – electric motor; 5 – vial with analyzed sample.

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3. Fig. 2. (a) Change in total ion current in sequential procedures of analysis of three different aliquots of a commercial olive oil sample, 1 – installation of a vial with analyzed sample into the sample inlet unit, 2 – removal of a vial from the sample inlet unit; (b) mass spectrum recorded during the analysis of olive oil.

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4. Fig. 3. Representation of mass spectrometric data in space: (a) PCA without feature selection, (b) t-SNE without feature selection, (c) t-SNE with feature selection.

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5. Fig. 4. Hierarchical representation of mass spectra of the studied types of vegetable oils. The dendrogram is constructed in the t-SNE space based on the selected 50 features.

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6. Fig. 5. Olive oil proportion values predicted by multiple linear regression based on the results of analysis of test samples of mixtures with a known ratio of olive and rapeseed oils.

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