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dc.creatorLeón Ecay, Saraes_ES
dc.creatorLópez Maestresalas, Ainaraes_ES
dc.creatorMurillo Arbizu, María Teresaes_ES
dc.creatorBeriain Apesteguía, María Josées_ES
dc.creatorMendizábal Aizpuru, José Antonioes_ES
dc.creatorArazuri Garín, Silviaes_ES
dc.creatorJarén Ceballos, Carmenes_ES
dc.creatorBass, Phillip D.es_ES
dc.creatorColle, Michael J.es_ES
dc.creatorGarcía, Davides_ES
dc.creatorRomano Moreno, Migueles_ES
dc.creatorInsausti Barrenetxea, Kizkitzaes_ES
dc.date.accessioned2022-10-11T14:23:59Z
dc.date.available2022-10-11T14:23:59Z
dc.date.issued2022
dc.identifier.citationLeón-Ecay, S., López-Maestresalas, A., Murillo-Arbizu, M. T., Beriain, M. J., Mendizabal, J. A., Arazuri, S., Jarén, C., Bass, P. D., Colle, M. J., García, D., Romano-Moreno, M., & Insausti, K. (2022). Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics. Foods, 11(19), 3105.en
dc.identifier.issn2304-8158
dc.identifier.urihttps://hdl.handle.net/2454/44194
dc.description.abstractNowadays, the meat industry requires non-destructive, sustainable, and rapid methods that can provide objective and accurate quality assessment with little human intervention. Therefore, the present research aimed to create a model that can classify beef samples from longissimus thoracis muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to obtain different textures, two main strategies were used: (a) aging type (wet and dry aging with or without starters) and (b) aging times (0, 7, 13, 21, and 27 days). Categorization into two groups was carried out for further chemometric analysis, encompassing group 1 (ngroup1 = 30) with samples with WBSF < 53 N whereas group 2 (ngroup2 = 28) comprised samples with WBSF values 53 N. Then, classification models were created by applying the partial least squares discriminant analysis (PLS-DA) method. The best results were achieved by combining the following pre-processing algorithms: 1st derivative + mean center, reaching 70.83% of correctly classified (CC) samples and 67.14% for cross validation (CV) and prediction, respectively. In general, it can be concluded that HSI technology combined with chemometrics has the potential to differentiate and classify meat samples according to their textural characteristics.en
dc.description.sponsorshipThis research was funded by Fondo Europeo de Desarrollo Regional, program FEDER 2014-2020 of Navarra, and by the Government of Navarra (project 0011-1365-2019-000091). Open Access funding was provided by Universidad Pública de Navarra. This work was also supported by the Spanish Ministry MCIN/AEI/10.13039/501100011033/FEDER, via Project No. RTI2018-094475-B-I00.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPIen
dc.relation.ispartofFoods 2022, 11, 3105en
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMeat qualityen
dc.subjectTextureen
dc.subjectHSIen
dc.subjectPLS-DAen
dc.subjectChemometricsen
dc.titleClassification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometricsen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.date.updated2022-10-10T07:22:11Z
dc.contributor.departmentAgronomía, Biotecnología y Alimentaciónes_ES
dc.contributor.departmentAgronomia, Bioteknologia eta Elikaduraeu
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.contributor.departmentInstitute on Innovation and Sustainable Development in Food Chain - ISFOODes_ES
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.3390/foods11193105
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094475-B-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.3390/foods11193105
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.contributor.funderGobierno de Navarra / Nafarroako Gobernua Universidad Pública de Navarra / Nafarroako Unibertsitatees


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© 2022 by the authors. 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
Except where otherwise noted, this item's license is described as © 2022 by the authors. 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

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