RT journal article
T1 Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning
A1 Pérez Calle, José Luis
A1 Punta Sánchez, Irene
A1 Velasco González de Peredo, Ana
A1 Ruíz Rodríguez, Ana
A1 Ferreiro González, Marta
A1 Palma Lovillo, Miguel
A2 Química Analítica
K1 honey
K1 adulteration
K1 machine learning
K1 visible near infrared spectroscopy
K1 support vector machine
K1 random forest
AB Honey is one of the most adulterated foods, usually through the addition of sweetenersor low-cost honeys. This study presents a method based on visible near infrared spectroscopy (VisNIRs), in combination with machine learning (ML) algorithms, for the correct identification andquantification of adulterants in honey. Honey samples from two botanical origins (orange blossomand sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%,10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed atendency to group the samples according to botanical origin, as well as the presence of adulteration.A supervised analysis was performed to detect the presence of adulterations. The best performancewith 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). Aregression study was also carried out to quantify the percentage of adulteration. The best result wasobtained by support vector regression (SVR) with a coefficient of determination (R2) of 0.991 and aroot mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining MLwith spectroscopic data as a method for the automated quality control of honey.
PB MDPI
SN 2304-8158
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31799
UL http://hdl.handle.net/10498/31799
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026