Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging

Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their qua...

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Autores:
Hernandez Fernandez, Joaquin
Martinez-Trespalacios, Jose
Marquez, Edgar
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12670
Acceso en línea:
https://hdl.handle.net/20.500.12585/12670
Palabra clave:
Sorbitol
Nucleating agent
Infrared spectroscopy machine learning
RMSE
SVR
LEMB
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openAccess
License
http://creativecommons.org/publicdomain/zero/1.0/
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dc.title.spa.fl_str_mv Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
title Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
spellingShingle Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
Sorbitol
Nucleating agent
Infrared spectroscopy machine learning
RMSE
SVR
LEMB
title_short Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
title_full Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
title_fullStr Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
title_full_unstemmed Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
title_sort Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging
dc.creator.fl_str_mv Hernandez Fernandez, Joaquin
Martinez-Trespalacios, Jose
Marquez, Edgar
dc.contributor.author.none.fl_str_mv Hernandez Fernandez, Joaquin
Martinez-Trespalacios, Jose
Marquez, Edgar
dc.subject.keywords.spa.fl_str_mv Sorbitol
Nucleating agent
Infrared spectroscopy machine learning
RMSE
SVR
topic Sorbitol
Nucleating agent
Infrared spectroscopy machine learning
RMSE
SVR
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C–O–C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2 , and RMSECV. Hyperparameter optimiza tion was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN)
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-05-17T12:27:30Z
dc.date.available.none.fl_str_mv 2024-05-17T12:27:30Z
dc.date.issued.none.fl_str_mv 2024-04-15
dc.date.submitted.none.fl_str_mv 2024-05-16
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dc.identifier.citation.spa.fl_str_mv Hernández-Fernández, J.; Martinez-Trespalacios, J.; Marquez, E. Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging. Foods 2024, 13, 1200. https://doi.org/10.3390/foods13081200
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12670
dc.identifier.doi.none.fl_str_mv 10.3390/foods13081200
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Hernández-Fernández, J.; Martinez-Trespalacios, J.; Marquez, E. Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging. Foods 2024, 13, 1200. https://doi.org/10.3390/foods13081200
10.3390/foods13081200
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12670
dc.language.iso.spa.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 18 páginas
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dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.publisher.sede.spa.fl_str_mv Campus Tecnológico
dc.source.spa.fl_str_mv Foods
institution Universidad Tecnológica de Bolívar
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spelling Hernandez Fernandez, Joaquine572e424-8c7f-4b36-ade7-2c94d29e3c79Martinez-Trespalacios, Jose35a0bdfa-63e6-466c-a948-93a61eefcb38Marquez, Edgar89b04eaf-a0a4-4a6c-95fe-30b68a6ed20d2024-05-17T12:27:30Z2024-05-17T12:27:30Z2024-04-152024-05-16Hernández-Fernández, J.; Martinez-Trespalacios, J.; Marquez, E. Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging. Foods 2024, 13, 1200. https://doi.org/10.3390/foods13081200https://hdl.handle.net/20.500.12585/1267010.3390/foods13081200Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C–O–C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2 , and RMSECV. Hyperparameter optimiza tion was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN)18 páginasapplication/pdfenghttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccessCC0 1.0 Universalhttp://purl.org/coar/access_right/c_abf2FoodsDevelopment of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaginginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85SorbitolNucleating agentInfrared spectroscopy machine learningRMSESVRLEMBCartagena de IndiasCampus TecnológicoInvestigadoresPlastics-the Facts 2019 An Analysis of European Plastics Production, Demand and Waste Data. Available online: https:// plasticseurope.org/wp-content/uploads/2021/10/2019-Plastics-the-facts.pdf (accessed on 24 November 2023).Hernández-Fernández, J.; Lopez-Martinez, J.; Barceló, D. Development and validation of a methodology for quantifying parts per-billion levels of arsine and phosphine in nitrogen, hydrogen and liquefied petroleum gas using a variable pressure sampler coupled to gas chromatography-mass spectrometry. J. Chromatogr. A 2021, 1637, 461833. [CrossRef]Hernández-Fernández, J.; Castro-Suarez, J.R.; Toloza, C.A.T. Iron Oxide Powder as Responsible for the Generation of Industrial Polypropylene Waste and as a Co-Catalyst for the Pyrolysis of Non-Additive Resins. Int. J. Mol. Sci. 2022, 23, 11708. [CrossRef]Kabir, E.; Kaur, R.; Lee, J.; Kim, K.H.; Kwon, E.E. Prospects of biopolymer technology as an alternative option for non-degradable plastics and sustainable management of plastic wastes. J. Clean. Prod. 2020, 258, 120536. [CrossRef]Rendón-Villalobos, R.; Ortíz-Sánchez, A.; Tovar-Sánchez, E.; Flores-Huicochea, E. The role of biopolymers in obtaining environ mentally friendly materials. Compos. Renew. Sustain. Mater. 2016, 151.Chacon, H.; Cano, H.; Fernández, J.H.; Guerra, Y.; Puello-Polo, E.; Ríos-Rojas, J.F.; Ruiz, Y. Effect of Addition of Polyurea as an Aggregate in Mortars: Analysis of Microstructure and Strength. Polymers 2022, 14, 1753. [CrossRef] [PubMed]Davis, G.; Song, J.H. Biodegradable packaging based on raw materials from crops and their impact on waste management. Ind. Crops Prod. 2006, 23, 147–161. [CrossRef]Raheem, D. Application of plastics and paper as food packaging materials—An overview. Emir. J. Food Agric. 2013, 25, 177–188. [CrossRef]Shaaban, H.A.; Farouk, A. Preservation and Packaging Food Recent Methods and Techniques: A Review. Biomed. J. Sci. Tech. Res. 2022, 46, 37680–37695. [CrossRef]Pavon, C.; Aldas, M.; Hernández-Fernández, J.; López-Martínez, J. 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