RT journal article
T1 Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data
A1 Pérez Calle, José Luis
A1 Ferreiro González, Marta
A1 Ruiz Rodríguez, Ana
A1 Fernández, Daniel
A1 Palma Lovillo, Miguel
A2 Química Analítica
K1 FT-IR
K1 fruit juices
K1 food control
K1 machine learning
K1 spectroscopy
K1 regression
K1 authentication
K1 classification
AB Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R-2 greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test set.
PB MDPI
SN 2073-4395
YR 2022
FD 2022
LK http://hdl.handle.net/10498/26804
UL http://hdl.handle.net/10498/26804
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026