Publication: Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics
dc.contributor.author | León Ecay, Sara | |
dc.contributor.author | López Maestresalas, Ainara | |
dc.contributor.author | Murillo Arbizu, María Teresa | |
dc.contributor.author | Beriain Apesteguía, María José | |
dc.contributor.author | Mendizábal Aizpuru, José Antonio | |
dc.contributor.author | Arazuri Garín, Silvia | |
dc.contributor.author | Jarén Ceballos, Carmen | |
dc.contributor.author | Bass, Phillip D. | |
dc.contributor.author | Colle, Michael J. | |
dc.contributor.author | García, David | |
dc.contributor.author | Romano Moreno, Miguel | |
dc.contributor.author | Insausti Barrenetxea, Kizkitza | |
dc.contributor.department | Agronomia, Bioteknologia eta Elikadura | eu |
dc.contributor.department | Ingeniaritza | eu |
dc.contributor.department | Institute on Innovation and Sustainable Development in Food Chain - ISFOOD | en |
dc.contributor.department | Agronomía, Biotecnología y Alimentación | es_ES |
dc.contributor.department | Ingeniería | es_ES |
dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua Universidad Pública de Navarra / Nafarroako Unibertsitate | es |
dc.date.accessioned | 2022-10-11T14:23:59Z | |
dc.date.available | 2022-10-11T14:23:59Z | |
dc.date.issued | 2022 | |
dc.date.updated | 2022-10-10T07:22:11Z | |
dc.description.abstract | Nowadays, 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.sponsorship | This 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.mimetype | application/pdf | en |
dc.identifier.citation | León-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.doi | 10.3390/foods11193105 | |
dc.identifier.issn | 2304-8158 | |
dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/44194 | |
dc.language.iso | eng | en |
dc.publisher | MDPI | en |
dc.relation.ispartof | Foods 2022, 11, 3105 | en |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094475-B-I00/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.3390/foods11193105 | |
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) license | en |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | Meat quality | en |
dc.subject | Texture | en |
dc.subject | HSI | en |
dc.subject | PLS-DA | en |
dc.subject | Chemometrics | en |
dc.title | Classification of beef longissimus thoracis muscle tenderness using hyperspectral imaging and chemometrics | en |
dc.type | Artículo / Artikulua | es |
dc.type | info:eu-repo/semantics/article | en |
dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | en |
dspace.entity.type | Publication | |
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