Recognition of organic rice samples based on trace elements and support vector machines |
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| Affiliation: | 1. Laboratory of Molecular Diagnostics, Embrapa Food Technology, Rio de Janeiro, Brazil;2. Núcleo de Pesquisa em Café Prof. Luiz Carlos Trugo, Laboratório de Química e Bioatividade de Alimentos, Instituto de Nutrição, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil;3. Cidade Universitária, Rio de Janeiro, Brazil;4. Laboratory of Mycology, Embrapa Food Technology, Rio de Janeiro, Brazil;5. Laboratory High Performance Liquid Chromatography, Embrapa Food Technology, Rio de Janeiro, Brazil;1. Institute of the Earth''s Crust, Siberian Branch of Russian Academy of Sciences, Lermontov St., 128, Irkutsk 664033, Russian Federation;2. Vinogradov Institute of Geochemistry, Siberian Branch of Russian Academy of Sciences, Favorsky St., 1A, Irkutsk 664033, Russian Federation;3. Irkutsk State University, 1 K. Marx Str., Irkutsk 664003, Russian Federation;4. Irkutsk Scientific Center, Siberian Branch, Russian Academy of Sciences, Lermontov St., 134, Irkutsk 664033, Russian Federation |
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| Abstract: | A simple approach is proposed for the authentication of organic rice samples. The strategy combines levels of concentration of trace elements and a data mining technique known as support vector machine (SVM). Nineteen elements (As, B, Ba, Ca, Cd, Ce, Cr, Co, Cu, Fe, La, Mg, Mn, Mo, P, Pb, Rb, Se and Zn) were determined in organic (n = 17) and conventional (n = 33) rice samples by quadrupole inductively coupled plasma mass spectrometry (q-ICP-MS) and the variations found in their elemental composition resulted in profiles with useful information for classification purposes. With the proposed methodology, it was possible to predict the authenticity of organic rice samples with an accuracy of 98% when using the 19 original elements. An accuracy of 96% was found using only the elements Ca and Cd. |
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| Keywords: | Rice Trace elements q-ICP-MS Chemometrics Support vector machine Classification Authenticity Food analysis Food composition |
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