Article: Tomato classification using mass spectrometry-machine learning technique: A food safety-enhancing platform
Food chemistry. 2023 Jan. 01, v. 398
2023
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 algorithms to analyze Mass Spectrometry data for ... ...
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 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. |
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Keywords | algorithms ; biomarkers ; decision support systems ; electrospray ionization mass spectrometry ; food chemistry ; food safety ; fruits ; models ; silica gel ; tomatoes |
Language | English |
Dates of publication | 2023-0101 |
Publishing place | Elsevier Ltd |
Document type | Article |
ZDB-ID | 243123-3 |
ISSN | 1873-7072 ; 0308-8146 |
ISSN (online) | 1873-7072 |
ISSN | 0308-8146 |
DOI | 10.1016/j.foodchem.2022.133870 |
Database | NAL-Catalogue (AGRICOLA) |
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