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

    Authors

    R. M. Barbosa; B. L. Batista; C. V. Bariao; R. M. Varrique; V. A. Coelho; A. D. Campiglia;F. Barbosa

    Comments

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    Abbreviated Journal Title

    Food Chem.

    Keywords

    Machine learning; Chemometric; Food samples; ICP-MS; Sugarcane; Trace; elements; SUPPORT VECTOR MACHINES; DATA MINING TECHNIQUES; RARE-EARTH-ELEMENTS; GEOGRAPHICAL ORIGIN; HEAVY-METALS; FERTILIZER; CHEMOMETRICS; MULTIELEMENT; QUALITY; MARKERS; Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics

    Abstract

    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.

    Journal Title

    Food Chemistry

    Volume

    184

    Publication Date

    1-1-2015

    Document Type

    Article

    Language

    English

    First Page

    154

    Last Page

    159

    WOS Identifier

    WOS:000353849200020

    ISSN

    0308-8146

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