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

Author(s)

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

• Document type: Journal article
• Source: Food Chemistry; v. 398, p. 5-pg., 2023-01-01.

Abstract

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)

• FAPESP's process: 19/05718-3 - Determination of metabolic alterations and therapeutic potential of Zika Virus in cancer cells by mass spectrometry and artificial intelligence
• Grantee: Jeany Delafiori
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)