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7289461 
Journal Article 
Recognition of organic rice samples based on trace elements and support vector machines 
Barbosa, RM; de Paula, ES; Paulelli, AnaC; Moore, AF; Oliveira Souza, JM; Batista, BL; Campiglia, AD; Barbosa, N, Jr 
2016 
Yes 
Journal of Food Composition and Analysis
ISSN: 0889-1575
EISSN: 1096-0481 
45 
95-100 
WOS:000366536200013 
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. (C) 2015 Elsevier Inc. All rights reserved. 
Rice; Trace elements; q-ICP-MS; Chemometrics; Support vector machine; Classification; Authenticity; Food analysis; Food composition