Logotipo del repositorio

Strategy for the prediction, control and optimizing of the functional properties of food proteins; using statistical and chemometric tools

cnea.tipodocumentoPARTE DE LIBRO
dc.contributor.authorBarberis, Sonia Esther
dc.contributor.authorSturniolo, Héctor Luis
dc.contributor.authorFolguera, Laura
dc.contributor.authorMagallanes, Jorge
dc.date.accessioned2025-12-11T23:32:36Z
dc.date.available2025-12-11T23:32:36Z
dc.date.issued2018
dc.description.abstractProteins have functional properties that govern their behavior in foods during processing, storage and consumption. Proteins can have high nutritional quality and not have functional properties suitable for incorporation into determined food systems. Furthermore, a desirable functional attribute for an additive may be undesirable for everyone else.This chapter describes the design of a new strategy to predict, control and optimize the functional parameters of food proteins hydrolyzed or not, using chemometrics tools. The starting material consists of proteins whose functional properties are desired to modify. Functional properties (e.g. emulsifying and foaming properties); can be simultaneously evaluated by an Experimental Statistical Design, Response Surface Graphics and Multiple Linear Regressions.This strategy expands the applications of food proteins and allows the following facilities: - assess interactions between variables of multivariate systems, - evaluate the dependence between functional parameters, - optimize the additive production with tailor-made functional properties for different food systems.
dc.description.institutionalaffiliationFil: Barberis, Sonia Esther. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Departamento de Farmacia. Laboratorio de Bromatología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich". Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich"; Argentina
dc.description.institutionalaffiliationFil: Sturniolo, Héctor Luis. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Departamento de Farmacia. Laboratorio de Bromatología; Argentina
dc.description.institutionalaffiliationFil: Folguera, Laura. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina
dc.description.institutionalaffiliationFil: Magallanes, Jorge. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentina
dc.identifier.urihttps://nuclea.cnea.gob.ar/handle/20.500.12553/8670
dc.publisherElsevier
dc.relationinfo:eu-repo/semantics/reference/hdl/11336/130414
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/B9780128114476000114
dc.rights.licenseinfo:eu-repo/semantics/restrictedAccess
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subjectPREDICTION, CONTROL, OPITMIZATION
dc.subjectFUNCTIONAL PROPERTIES
dc.subjectFOOD PROTEINS
dc.subjectSTATISTICAL AND CHEMOMETRIC TOOLS
dc.subjectBioprocesamiento Tecnológico, Biocatálisis, Fermentación
dc.subjectBiotecnología Industrial
dc.subjectINGENIERÍAS Y TECNOLOGÍAS
dc.titleStrategy for the prediction, control and optimizing of the functional properties of food proteins; using statistical and chemometric tools
dc.typeLIBROS
dc.type.versionVersión publicada

Archivos