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Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach

Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-...

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Detalles Bibliográficos
Autores principales: Barea-Sepúlveda, Marta, Calle, José Luis P., 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/PMC10528079/
https://www.ncbi.nlm.nih.gov/pubmed/37761070
http://dx.doi.org/10.3390/foods12183362
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author Barea-Sepúlveda, Marta
Calle, José Luis P.
Ferreiro-González, Marta
Palma, Miguel
author_facet Barea-Sepúlveda, Marta
Calle, José Luis P.
Ferreiro-González, Marta
Palma, Miguel
author_sort Barea-Sepúlveda, Marta
collection PubMed
description Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications.
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spelling pubmed-105280792023-09-28 Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach Barea-Sepúlveda, Marta Calle, José Luis P. Ferreiro-González, Marta Palma, Miguel Foods Article Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications. MDPI 2023-09-07 /pmc/articles/PMC10528079/ /pubmed/37761070 http://dx.doi.org/10.3390/foods12183362 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
Barea-Sepúlveda, Marta
Calle, José Luis P.
Ferreiro-González, Marta
Palma, Miguel
Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
title Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
title_full Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
title_fullStr Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
title_full_unstemmed Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
title_short Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
title_sort rapid classification of petroleum waxes: a vis-nir spectroscopy and machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528079/
https://www.ncbi.nlm.nih.gov/pubmed/37761070
http://dx.doi.org/10.3390/foods12183362
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