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

Keywords

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

Abstract

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.

Publication Date

10-1-2015

Publication Title

Food Chemistry

Volume

184

Number of Pages

154-159

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.foodchem.2015.02.146

Socpus ID

84926188310 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84926188310

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