Published July 21, 2022
| Version V.1
Dataset
Open
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
Creators
- 1. Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, Brazil – Rua Cinco de Junho, 350 – 13083-970 – Cidade Universitária Zeferino Vaz, Campinas/SP - Brazil
Description
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.
Files
CherryTomato_Markers_Matrix.csv
Files
(233.9 MB)
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