Tomato classification using mass spectrometry-machine learning technique: A food safety-enhancing platform.

Autor: de Oliveira AN; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil., Bolognini SRF; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil., Navarro LC; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil., Delafiori J; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil., Sales GM; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil., de Oliveira DN; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil., Catharino RR; Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Rua Cinco de Junho, 350 - 13083-970 - Cidade Universitária Zeferino Vaz, Campinas/SP, Brazil. Electronic address: rrc@unicamp.br.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2023 Jan 01; Vol. 398, pp. 133870. Date of Electronic Publication: 2022 Aug 08.
DOI: 10.1016/j.foodchem.2022.133870
Abstrakt: Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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