title: 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.
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.
authors: Instituto de Informática, Universidade Federal de Goiás, Goiânia-Go, Brazil.
authors: Rommel M
authors: RM
authors: Barbosa
authors: Laboratório de Toxicologia e Essencialidade de Metais, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café s/n, Monte Alegre, 1404903 Ribeirão Preto, SP, Brazil.
authors: Bruno L
authors: BL
authors: Batista
authors: Laboratório de Toxicologia e Essencialidade de Metais, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café s/n, Monte Alegre, 1404903 Ribeirão Preto, SP, Brazil.
authors: Camila V
authors: CV
authors: Barião
authors: Laboratório de Toxicologia e Essencialidade de Metais, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café s/n, Monte Alegre, 1404903 Ribeirão Preto, SP, Brazil.
authors: Renan M
authors: RM
authors: Varrique
authors: Laboratório de Toxicologia e Essencialidade de Metais, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café s/n, Monte Alegre, 1404903 Ribeirão Preto, SP, Brazil.
authors: Vinicius A
authors: VA
authors: Coelho
authors: Department of Chemistry, University of Central Florida, Orlando, USA.
authors: Andres D
authors: AD
authors: Campiglia
authors: Laboratório de Toxicologia e Essencialidade de Metais, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café s/n, Monte Alegre, 1404903 Ribeirão Preto, SP, Brazil. Electronic address: fbarbosa@fcfrp.usp.br.
authors: Fernando
authors: F
authors: Barbosa
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D013058:Mass Spectrometry / Q000379:methods*; D012016:Reference Values; D031786:Saccharum / Q000737:chemistry*; D013054:Spectrophotometry, Atomic; D014131:Trace Elements / Q000032:analysis*
chemical_list: D014131:Trace Elements
keywords: Chemometric; Food samples; ICP-MS; Machine learning; Sugarcane; Trace elements