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

• Published in: Food chemistry 2015-10, Vol.184, p.154-159
• Main Authors: Barbosa, Rommel M., Batista, Bruno L., Barião, Camila V., Varrique, Renan M., Coelho, Vinicius A., Campiglia, Andres D., Barbosa, Fernando
• Format: Article
• Language: English
• Subjects: Algorithms; Bayes Theorem; Chemometric; Food samples; ICP-MS; Machine learning; Mass Spectrometry - methods; Reference Values; Saccharum - chemistry; Spectrophotometry, Atomic; Sugarcane; Trace elements; Trace Elements - analysis
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2015.02.146

•32 elements were determined in sugarcane samples by ICP-MS.•Authenticity of organic sugarcane is possible by using chemometric.•Naive Bayes or Random Forest algorithms and 8 minerals provide 95% of accuracy. 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.