Repozytorium publikacji - Politechnika Gdańska

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.

Autorzy

• Irene Punta-Sánchez,
• dr inż. Tomasz Dymerski link otwiera się w nowej karcie ,
• José Luis P. Calle,
• Ana Ruiz-Rodríguez,
• Marta Ferreiro-González,
• Miguel Palma