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Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning

Authors :
Irene Punta-Sánchez
Tomasz Dymerski
José Luis P. Calle
Ana Ruiz-Rodríguez
Marta Ferreiro-González
Miguel Palma
Source :
Sensors, Vol 24, Iss 23, p 7481 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R2 values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R2 values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
23
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.6d31eba4af5d4bd3ac5688b5c257827e
Document Type :
article
Full Text :
https://doi.org/10.3390/s24237481