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

Autor(es)

Oliveira, Arthur Noin de ; Bolognini, Sophia Regina Frazatto ; Navarro, Luiz Claudio ; Delafiori, Jeany ; Sales, Geovana Manzan ; Oliveira, Diogo Noin de ; Catharino, Rodrigo Ramos

• Tipo de documento: Artigo Científico
• Fonte: Food Chemistry; v. 398, p. 5-pg., 2023-01-01.

Resumo

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 algo-rithms 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. (AU)

• Processo FAPESP: 19/05718-3 - Determinação das alterações metabólicas e do potencial terapêutico do Zika Vírus em células tumorais por espectrometria de massas e inteligência artificial
• Beneficiário: Jeany Delafiori
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto