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

Irene Punta-Sánchez; Tomasz Dymerski; José Luis P. Calle; Ana Ruiz-Rodríguez; Marta Ferreiro-González; Miguel Palma

doi:10.3390/s24237481

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

Abstrakt

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.

Cytowania

0

CrossRef
0

Web of Science
0

Scopus

Autorzy (6)

Irene Punta-Sánchez
- University of Cadiz
Tomasz Dymerski dr inż.
José Luis P. Calle
- University of Cadiz
Ana Ruiz-Rodríguez
- University of Cadiz
Marta Ferreiro-González
- University of Cadiz
Miguel Palma
- University of Cadiz

Cytuj jako

Pełna treść

pobierz publikację

pobrano 0 razy

Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s24237481
Licencja: otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach dostępnych w wersji elektronicznej [także online]

Opublikowano w:

SENSORS strony 1 - 14,
ISSN: 1424-8220

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

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: