Using Machine Learning and Multi-Element Analysis to Evaluate the Authenticity of Organic and Conventional Vegetables
Author:
Funder
Fapesp
CNPq
Publisher
Springer Science and Business Media LLC
Subject
Safety Research,Safety, Risk, Reliability and Quality,Applied Microbiology and Biotechnology,Food Science,Analytical Chemistry
Link
http://link.springer.com/content/pdf/10.1007/s12161-019-01597-2.pdf
Reference66 articles.
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3. Barbosa RM, Batista BL, Barião CV, Varrique RM, Coelho VA, Campiglia AD, Barbosa F Jr (2015) 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. Food Chem 184:154–159. https://doi.org/10.1016/j.foodchem.2015.02.146
4. Barbosa RM, de Paula ES, Paulelli AC, Moore AF, Souza JMO, Batista BL, Campiglia AD, Barbosa F Jr (2016) Recognition of organic rice samples based on trace elements and support vector machines. J Food Compos Anal 45:95–100. https://doi.org/10.1016/j.jfca.2015.09.010
5. Bigot C, Meile J, Kapitan A, Montet D (2015) Discriminating organic and conventional foods by analysis of their microbial ecology: an application on fruits. Food Control 48:123–129. https://doi.org/10.1016/j.foodcont.2014.03.035
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