Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems pres...

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Bibliographic Details
Published in:Applied sciences 2024-09, Vol.14 (17), p.8074
Main Authors: Durán Acevedo, Cristhian Manuel, Cárdenas Niño, Dayan Diomedes, Carrillo Gómez, Jeniffer Katerine
Format: Article
Language:English
Subjects:
Agriculture
Chromatography
E-nose
E-tongue
Food chains
Food quality
Food security
Food supply
fruit pesticides
Fruits
Fungicides
Insecticides
machine learning
pattern recognition
Pesticides
Poisoning
Public health
Sensors
Strawberries
Toxicity
Vegetables
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Summary:In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14178074