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Multielement determination in orange juice by ICP-MS associated with data mining for the classification of organic samples

作     者:Christian Turra Marcio Dias de Lima Elisabete A.De Nadai Fernandes Marcio Arruda Bacchi Fernando Barbosa Jr. Rommel Barbosa 

作者机构:Centro de Energia Nuclear na AgriculturaUniversidade de Sao PauloPiracicabaBrazil USP-Ribeirao PretoBrazil Instituto de InformaticaUniversidade Federal de GoiasGoianiaGOBrazil Instituto Federal de Educacao Ciencia e Tecnologia de GoiasGoianiaGOBrazil 

出 版 物:《Information Processing in Agriculture》 (农业信息处理(英文))

年 卷 期:2017年第4卷第3期

页      面:199-205页

核心收录:

学科分类:08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

主  题:Data mining Support vector machines Multilayer perceptron Orange juice Trace elements Inductively coupled plasma mass spectrometry(ICP-MS) 

摘      要:The aim of this study was to discriminate organic from conventional orange juice based on chemical elements and data mining applications.A comprehensive sampling of organic and conventional oranges was carried out in Borborema,state of Sao Paulo,*** fruits of the variety Valencia(Citrus sinensis(L.)Osbeck)budded on Rangpur lime(Citrus limonia Osbeck)were *** chemical elements were determined in 57 orange samples grown in organic and conventional *** order to classify these samples,data mining techniques(Support Vector Machine(SVM)and Multilayer Perceptron(MLP))were combined with feature selection(F-score and chi-squared).SVM with chi-squared had a better performance compared with the other techniques because it reached 93.00% accuracy using only seven chemical components(Cu,Cs,Zn,Al,Mn,Rb and Sr),and correctly classified 96.73% of the samples grown in an organic system.

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