- -

Non-destructive control in cheese processing: Modelling texture evolution in the milk curdling phase by laser backscattering imaging

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Non-destructive control in cheese processing: Modelling texture evolution in the milk curdling phase by laser backscattering imaging

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Verdú Amat, Samuel es_ES
dc.contributor.author Pérez Jiménez, Alberto José es_ES
dc.contributor.author Barat Baviera, José Manuel es_ES
dc.contributor.author Grau Meló, Raúl es_ES
dc.date.accessioned 2022-06-15T18:04:40Z
dc.date.available 2022-06-15T18:04:40Z
dc.date.issued 2021-03 es_ES
dc.identifier.issn 0956-7135 es_ES
dc.identifier.uri http://hdl.handle.net/10251/183342
dc.description.abstract [EN] This study aim was to explore the laser backscattering imaging technique's capacity to model the curdling phase in cheese processing. To do so, three different formulas were studied by modifying solute concentration. Textural modifications to the matrix during curdling were characterised by viscosimetry and texture measurements depending on samples' liquid or solid state. This state changed by determining gelation to establish the limits for the liquid and solid phases. The process was also characterised by the imaging technique, which showed dependence on both solute concentration and enzymatic effect on both the previously observed phases. After following multivariate statistical procedures to reduce dimensionality, the imaging results revealed that solute concentration strongly influenced the variance that the imaging technique captured. It reduced the visibility of the phase change in the image parameters. After eliminating this influence, the evolution of the matrix across the liquid and solid phases was modelled. Data were divided into phases and used to successfully predict the matrix status in each phase by multivariate non-linear regression procedures. It was concluded that the laser backscattering imaging technique presented suitable properties to be used for non-destructive continuous curdling process monitoring during the cheese-making process. es_ES
dc.description.sponsorship The authors gratefully acknowledge the financial support from the University Polytechnic of Valencia for Programme "Ayudas para la Contratacion de Doctores para el Acceso al Sistema Espanol de Ciencia, Tecnologia e Innovacion, en Estructuras de Investigacion de la UPV (PAID-10-17)" es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Food Control es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Cheese production es_ES
dc.subject Laser backscattering imaging es_ES
dc.subject Non-destructive control es_ES
dc.subject Curdling process es_ES
dc.subject Monitoring es_ES
dc.subject Prediction es_ES
dc.subject Phase change es_ES
dc.subject.classification TECNOLOGIA DE ALIMENTOS es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Non-destructive control in cheese processing: Modelling texture evolution in the milk curdling phase by laser backscattering imaging es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.foodcont.2020.107638 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-10-17/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Verdú Amat, S.; Pérez Jiménez, AJ.; Barat Baviera, JM.; Grau Meló, R. (2021). Non-destructive control in cheese processing: Modelling texture evolution in the milk curdling phase by laser backscattering imaging. Food Control. 121:1-9. https://doi.org/10.1016/j.foodcont.2020.107638 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.foodcont.2020.107638 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 9 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 121 es_ES
dc.relation.pasarela S\418200 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem