RT journal article
T1 Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
A1 Pérez Calle, José Luis
A1 Vázquez Espinosa, María de las Mercedes
A1 Barea Sepúlveda, Marta
A1 Ruiz Rodríguez, Ana
A1 Ferreiro González, Marta
A1 Palma Lovillo, Miguel
A2 Química Analítica
K1 fruit juices
K1 ion mobility spectrometry
K1 optimization
K1 Box–Behnken
K1 support vector machine
K1 random forest
K1 machine learning
AB Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on theuse of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combinationwith machine-learning algorithms for the characterization juices of different raw material (orange,pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used.First, an optimization of the most important conditions in generating the HS was carried out usinga Box–Behnken design coupled with a response surface methodology. The following factors werestudied: temperature, time, and sample volume. The optimum values were 46.3 ◦C, 5 min, and 750 µL,respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization.The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA)revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent,the commercial brand. The combination of IMSS with supervised classification techniques reported anexcellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest(RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effectivealternative for characterizing and classifying the different types of juices.
PB MDPI
SN 2304-8158
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31818
UL http://hdl.handle.net/10498/31818
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026