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Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices

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 the use of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algori...

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Autores principales: Calle, José Luis P., Vázquez-Espinosa, Mercedes, Barea-Sepúlveda, Marta, Ruiz-Rodríguez, Ana, Ferreiro-González, Marta, Palma, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340320/
https://www.ncbi.nlm.nih.gov/pubmed/37444273
http://dx.doi.org/10.3390/foods12132536
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author Calle, José Luis P.
Vázquez-Espinosa, Mercedes
Barea-Sepúlveda, Marta
Ruiz-Rodríguez, Ana
Ferreiro-González, Marta
Palma, Miguel
author_facet Calle, José Luis P.
Vázquez-Espinosa, Mercedes
Barea-Sepúlveda, Marta
Ruiz-Rodríguez, Ana
Ferreiro-González, Marta
Palma, Miguel
author_sort Calle, José Luis P.
collection PubMed
description 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 the use of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combination with 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 using a Box–Behnken design coupled with a response surface methodology. The following factors were studied: 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 an excellent 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 effective alternative for characterizing and classifying the different types of juices.
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spelling pubmed-103403202023-07-14 Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices Calle, José Luis P. Vázquez-Espinosa, Mercedes Barea-Sepúlveda, Marta Ruiz-Rodríguez, Ana Ferreiro-González, Marta Palma, Miguel Foods Article 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 the use of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combination with 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 using a Box–Behnken design coupled with a response surface methodology. The following factors were studied: 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 an excellent 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 effective alternative for characterizing and classifying the different types of juices. MDPI 2023-06-29 /pmc/articles/PMC10340320/ /pubmed/37444273 http://dx.doi.org/10.3390/foods12132536 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Calle, José Luis P.
Vázquez-Espinosa, Mercedes
Barea-Sepúlveda, Marta
Ruiz-Rodríguez, Ana
Ferreiro-González, Marta
Palma, Miguel
Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
title Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
title_full Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
title_fullStr Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
title_full_unstemmed Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
title_short Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices
title_sort novel method based on ion mobility spectrometry combined with machine learning for the discrimination of fruit juices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340320/
https://www.ncbi.nlm.nih.gov/pubmed/37444273
http://dx.doi.org/10.3390/foods12132536
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