Abstract
This article introduces a novel approach to detecting honey adulteration by combining ultrafast 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.
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s24237481
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- Category:
- Articles
- Type:
- artykuły w czasopismach dostępnych w wersji elektronicznej [także online]
- Published in:
-
SENSORS
pages 1 - 14,
ISSN: 1424-8220 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Punta-Sánchez I., Dymerski T., Calle J. L. P., Ruiz-Rodríguez A., Ferreiro-González M., Palma M., Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning, SENSORS, 2024,10.3390/s24237481
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s24237481
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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