RT journal article
T1 Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data
A1 Pérez Calle, José Luis
A1 Barea Sepúlveda, Marta
A1 Ruiz Rodríguez, Ana
A1 Álvarez Saura, José Ángel
A1 Palma Lovillo, Miguel
A2 Química Analítica
A2 Química Física
K1 near-infrared spectroscopy
K1 adulteration
K1 fruits juices
K1 machine learning
K1 regression
K1 classification
AB Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.
PB MDPI
SN 1424-8220
YR 2022
FD 2022-05
LK http://hdl.handle.net/10498/27242
UL http://hdl.handle.net/10498/27242
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026