RT journal article
T1 Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning
A1 Punta Sánchez, Irene
A1 Dymerski, Tomasz
A1 Pérez Calle, José Luis
A1 Ruiz Rodríguez, Ana
A1 Ferreiro González, Marta
A1 Palma Lovillo, Miguel
A2 Química Analítica
K1 adulteration
K1 classification
K1 food control
K1 honey
K1 machine learning
K1 regression
K1 ultra-fast gas chromatography
K1 volatile compounds
AB 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.
PB Multidisciplinary Digital Publishing Institute (MDPI)
SN 1424-8220
YR 2024
FD 2024
LK http://hdl.handle.net/10498/36036
UL http://hdl.handle.net/10498/36036
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026