卷:184 | |
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 | |
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.
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