Dataset from Zenodo · Noin de Oliveira, Arthur
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
- License
- CC BY 4.0commercial use allowedLicense text
- This license allows commercial use. Usually you must credit the author; read the license for the exact terms.
- Formats
- CSV
- Size
- 223 MB
- Updated
- Jul 22, 2022
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.
Description from Zenodo, shortened.
Files (1)
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