RT journal article
T1 Machine learning-assisted spectroscopic methods for detecting adulteration in Barrantes wine from Folla Redonda grapes
A1 Aliaño González, María José
A1 Vázquez Espinosa, María de las Mercedes
A1 Velasco González de Peredo, Ana
A1 Chinchilla Salcedo, Nuria
A1 Fernández Barbero, Gerardo
A1 Palma Lovillo, Miguel
A1 Carrera Fernández, Ceferino Adrián
A2 Química Analítica
A2 Química Orgánica
K1 Folla Redonda
K1 Barrantes wine
K1 Adulteration
K1 Vis-NIRS
K1 FTIR
K1 Machine learning
K1 SVM
AB The present study explores the application of advanced machine learning algorithms combined with vis-NIRS and FTIR spectroscopy to detect and quantify adulteration in Barrantes wine, produced from the Folla Redonda grape, a variety exclusive to the Galicia region in Spain. Given the unique characteristics of Folla Redonda grapes, such as their distinctive phenolic profile and high antioxidant content, Barrantes wine is at risk of adulteration with lower-quality red wines. The study employs a variety of preprocessing techniques, including normalization and derivative transformations, and evaluates classification models such as Support Vector Machines (SVM), Partial Least Squares Discriminant Analysis (PLS-DA), and Random Forest (RF) for detecting adulteration at varying levels (10 % – 60 %). The findings show that SVM with SNV preprocessing provides the highest accuracy for vis-NIRS and FTIR data (100 %). Furthermore, regression models, particularly SVMRadial, were used to predict the level of adulteration with high precision (R2 > 0.95; RMSE <11 %). This research is pioneering in its field, as it is the first to optimize machine learning techniques for adulteration detection in wine. The findings provide a valuable tool for quality control in the agro-food sector, specifically for niche wines like Barrantes. The results of this study underscore the potential for rapid, precise, and non-destructive analysis through the integration of these approaches.
PB Elsevier
SN 1386-1425
YR 2026
FD 2026
LK http://hdl.handle.net/10498/39060
UL http://hdl.handle.net/10498/39060
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 04-nov-2026