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http://hdl.handle.net/10662/22191
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Campo DC | Valor | idioma |
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dc.contributor.author | Martín Tornero, Elísabet | - |
dc.contributor.author | Durán Merás, Isabel | - |
dc.contributor.author | Alcaraz, Mirta R. | - |
dc.contributor.author | Muñoz de la Peña, Arsenio | - |
dc.contributor.author | Galeano Díaz, María Teresa | - |
dc.contributor.author | Goicoechea, Héctor Casimiro | - |
dc.date.accessioned | 2024-09-03T10:51:09Z | - |
dc.date.available | 2024-09-03T10:51:09Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 0026-265X | - |
dc.identifier.uri | http://hdl.handle.net/10662/22191 | - |
dc.description.abstract | In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, “Torta del Casar” and “Queso de la Serena”. Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least-squares (MCR-ALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline. | es_ES |
dc.description.sponsorship | Financial support was provided by the Ministerio de Ciencia e Innovaci´on of Spain (Project PID2020-112996 GB-I00 financed by MCIN/AEI/10.13039/501100011033) and Junta de Extremadura (Project IB20016) co-financed by Fondos Europeos de Desarrollo Regional. H.C.G. and M.R.A thank to program María Zambrano (Ministerio de Universidades of Spain) | es_ES |
dc.format.extent | 9 p. | es_ES |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Torta del casar | es_ES |
dc.subject | Queso de la Serena | es_ES |
dc.subject | Infrarrojo cercano | es_ES |
dc.subject | Near infrared | es_ES |
dc.subject | Multivariate curve resolution | es_ES |
dc.subject | Lineal discriminant analysis | es_ES |
dc.subject | Quadratic discriminant analysis | es_ES |
dc.subject | Artificial neural networks | es_ES |
dc.subject | Redes neuronales artificiales | es_ES |
dc.subject | Análisis discriminante cuadrático | es_ES |
dc.subject | Resolución de curvas multivariadas | es_ES |
dc.title | Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening | es_ES |
dc.type | article | es_ES |
dc.description.version | peerReviewed | es_ES |
europeana.type | TEXT | en_US |
dc.rights.accessRights | openAccess | es_ES |
dc.subject.unesco | 3309 Tecnología de Los Alimentos | es_ES |
dc.subject.unesco | 3309.09 Productos lácteos | es_ES |
dc.subject.unesco | 2301 Química Analítica | es_ES |
europeana.dataProvider | Universidad de Extremadura. España | es_ES |
dc.identifier.bibliographicCitation | lísabet Martín-Tornero, Isabel Durán-Merás, Mirta R. Alcaraz, Arsenio Muñoz de la Peña, Teresa Galeano-Díaz, Héctor C. Goicoechea, Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening, Microchemical Journal, Volume 204, 2024, 111039, ISSN 0026-265X, https://doi.org/10.1016/j.microc.2024.111039 | es_ES |
dc.type.version | publishedVersion | es_ES |
dc.contributor.affiliation | Universidad de Extremadura. Departamento de Química Analítica | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0026265X24011512?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.microc.2024.111039 | - |
dc.identifier.publicationtitle | Microchemical Journal | es_ES |
dc.identifier.publicationissue | 204 | es_ES |
dc.identifier.publicationfirstpage | 111039-1 | es_ES |
dc.identifier.publicationlastpage | 111039-9 | es_ES |
dc.identifier.orcid | 0000-0002-8908-1104 | es_ES |
dc.identifier.orcid | 0000-0003-2360-7852 | es_ES |
dc.identifier.orcid | 0000-0001-9832-6866 | es_ES |
Colección: | DQUAN - Artículos |
Archivos
Archivo | Descripción | Tamaño | Formato | |
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j_microc_2024_111039.pdf | 1,95 MB | Adobe PDF | Descargar |
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