A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry

卷:184

Barbosa, Rommel M. ; Batista, Bruno L. ; Bariao, Camila V. ; Varrique, Renan M. ; Coelho, Vinicius A. ; Campiglia, Andres D. ; Barbosa, Fernando, Jr.

关键词: Machine learning;    Chemometric;    Food samples;    ICP-MS;    Sugarcane;    Trace elements;   

DOI  :  10.1016/j.foodchem.2015.02.146

学科分类：食品科学和技术

【 摘 要 】

A practical and easy control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry is proposed. Reference ranges for 32 chemical elements in 22 samples of sugarcane (13 organic and 9 non organic) were established and then two algorithms, Naive Bayes (NB) and Random Forest (RF), were evaluated to classify the samples. Accurate results (>90%) were obtained when using all variables (i.e., 32 elements). However, accuracy was improved (95.4% for NB) when only eight minerals (Rb, U, Al, Sr, Dy, Nb, Ta, Mo), chosen by a feature selection algorithm, were employed. Thus, the use of a fingerprint based on trace element levels associated with classification machine learning algorithms may be used as a simple alternative for authenticity evaluation of organic sugarcane samples. (C) 2015 Elsevier Ltd. All rights reserved.