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
Copyright Status
Unknown
Socpus ID
84926188310 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84926188310
STARS Citation
Barbosa, Rommel M.; Batista, Bruno L.; Barião, Camila V.; Varrique, Renan M.; and Coelho, Vinicius A., "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" (2015). Scopus Export 2015-2019. 380.
https://stars.library.ucf.edu/scopus2015/380