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dc.contributor.advisorArana Momoitio, Gorka
dc.contributor.advisorOlabarrieta Paul, Idoia
dc.contributor.authorNieto Ortega, Sonia
dc.date.accessioned2023-06-13T06:44:14Z
dc.date.available2023-06-13T06:44:14Z
dc.date.issued2023-03-07
dc.date.submitted2023-03-07
dc.identifier.urihttp://hdl.handle.net/10810/61362
dc.description244 p.
dc.description.abstractThe quality and authenticity assurance of food products has become an essential issue for the food industry. Even though it has been traditionally controlled using analytical destructive methods, food industry would benefit from a change that can overcome their drawbacks. The digital transformation of the industry, also known as Industry 4.0, has emerged recently as a change of paradigm in the current processes. Smart sensors have arisen as powerful tools in this digital transformation. They are a good option to perform the control of authenticity and quality in the food industry and to monitor processes. However, in order to make these sensors smart, there is the necessity of using chemometrics.Even though in other sectors this digitalization is assumed to be necessary and has already started, food industry transformation is slowed down due to its specific casuistry. In big companies, this digitalization is already a fact, but SMEs, which in Europe account for more than the 90% of the food companies, need the development of applications ad-hoc adapted for their needs, using sensors with a lower cost but still rigorous, since they have limited resources and/or digital capabilities. Therefore, more investigation is needed for the digitalization of this sector, especially regarding these enterprises.With that aim, this doctoral thesis has used three different non-destructive and portable sensors, based in different principles (near infrared spectroscopy or NIRS, bioelectrical impedance analysis or BIA and time domain reflectometry or TDR), coupled with the use of different chemometric methods, for developing five different new applications regarding the quality and authenticity control of four different food matrices: tuna, fish oil coming from by-products, avocado and béchamel sauce.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectdata analysises_ES
dc.subjectmultivariate analysises_ES
dc.subjectanálisis de datoses_ES
dc.subjectanálisis multivariantees_ES
dc.titleDevelopment of new applications for smart sensors coupled with chemometrics for ensuring the quality and authenticity of food products within the framework of Industry 4.0es_ES
dc.typeinfo:eu-repo/semantics/doctoralThesises_ES
dc.rights.holder(c) 2023 SONIA NIETO ORTEGA
dc.identifier.studentID901670es_ES
dc.identifier.projectID22967es_ES
dc.departamentoesQuímica analíticaes_ES
dc.departamentoeuKimika analitikoaes_ES


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