Skup podataka sa Zenodo · Noin de Oliveira, Arthur

Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform

Licenca
CC BY 4.0dozvoljena komercijalna upotrebaTekst licence
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Formati
CSV
Veličina
223 MB
Ažurirano
22. 7. 2022.

Opis

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.

Opis sa Zenodo, skraćen.

Fajlovi (1)

  • CSVCherryTomato_Markers_Matrix.csv223 MB

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