Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics

This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed under the same frying conditions were analysed. Parti...

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Autores: Peraza Alemán, Carlos Miguel, López Maestresalas, Ainara, Jarén Ceballos, Carmen, Ruiz de Galarreta, José Ignacio, Barandalla, Leire, Arazuri Garín, Silvia
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/53805
Acceso en línea:https://hdl.handle.net/2454/53805
Access Level:acceso abierto
Palabra clave:Spatial distribution
Machine learning
NIR-HSI
Solanum tuberosum L.
PLSR
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spelling Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometricsPeraza Alemán, Carlos MiguelLópez Maestresalas, AinaraJarén Ceballos, CarmenRuiz de Galarreta, José IgnacioBarandalla, LeireArazuri Garín, SilviaSpatial distributionMachine learningNIR-HSISolanum tuberosum L.PLSRThis study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed under the same frying conditions were analysed. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR), combined with a logarithmic transformation of the acrylamide levels, were applied to develop predictive models. The most optimal outcomes for PLSR yielded R2 p: 0.85, RMSEP: 201 μg/kg and RPD: 2.53, while for SVMR yielded R2 p: 0.80, RMSEP: 229 μg/kg and RPD: 2.22. Furthermore, the selection of significant wavelengths enabled an 87.95 % reduction in variables without affecting the model’s accuracy. Finally, spatial mapping of acrylamide content was conducted on all chips in the external validation set. This method provides both quantification and visualization capabilities, thus enhancing quality control for acrylamide identification in processed potatoes.This work was supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU/AEI /10.13039/501100011033), Spain, project: PID2019-109790RR-C22 and the predoctoral grant (PRE2020-094533) associated to it.ElsevierIngenieríaIngeniaritzaInstitute on Innovation and Sustainable Development in Food Chain - ISFOOD2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/53805reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109790RR-C22© 2025 The Authors. This is an open access article under the CC BY-NC license.https://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/538052026-06-17T12:41:47Z
dc.title.none.fl_str_mv Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
title Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
spellingShingle Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
Peraza Alemán, Carlos Miguel
Spatial distribution
Machine learning
NIR-HSI
Solanum tuberosum L.
PLSR
title_short Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
title_full Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
title_fullStr Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
title_full_unstemmed Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
title_sort Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics
dc.creator.none.fl_str_mv Peraza Alemán, Carlos Miguel
López Maestresalas, Ainara
Jarén Ceballos, Carmen
Ruiz de Galarreta, José Ignacio
Barandalla, Leire
Arazuri Garín, Silvia
author Peraza Alemán, Carlos Miguel
author_facet Peraza Alemán, Carlos Miguel
López Maestresalas, Ainara
Jarén Ceballos, Carmen
Ruiz de Galarreta, José Ignacio
Barandalla, Leire
Arazuri Garín, Silvia
author_role author
author2 López Maestresalas, Ainara
Jarén Ceballos, Carmen
Ruiz de Galarreta, José Ignacio
Barandalla, Leire
Arazuri Garín, Silvia
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería
Ingeniaritza
Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
dc.subject.none.fl_str_mv Spatial distribution
Machine learning
NIR-HSI
Solanum tuberosum L.
PLSR
topic Spatial distribution
Machine learning
NIR-HSI
Solanum tuberosum L.
PLSR
description This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed under the same frying conditions were analysed. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR), combined with a logarithmic transformation of the acrylamide levels, were applied to develop predictive models. The most optimal outcomes for PLSR yielded R2 p: 0.85, RMSEP: 201 μg/kg and RPD: 2.53, while for SVMR yielded R2 p: 0.80, RMSEP: 229 μg/kg and RPD: 2.22. Furthermore, the selection of significant wavelengths enabled an 87.95 % reduction in variables without affecting the model’s accuracy. Finally, spatial mapping of acrylamide content was conducted on all chips in the external validation set. This method provides both quantification and visualization capabilities, thus enhancing quality control for acrylamide identification in processed potatoes.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/53805
url https://hdl.handle.net/2454/53805
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109790RR-C22
dc.rights.none.fl_str_mv © 2025 The Authors. This is an open access article under the CC BY-NC license.
https://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2025 The Authors. This is an open access article under the CC BY-NC license.
https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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