Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

Baruque, Bruno; Corchado Rodríguez, Emilio Santiago; Yin, Hujun; Rovira Carballido, Jordi; González, Javier

Título

dc.contributor.author	Baruque, Bruno
dc.contributor.author	Corchado Rodríguez, Emilio Santiago
dc.contributor.author	Yin, Hujun
dc.contributor.author	Rovira Carballido, Jordi
dc.contributor.author	González, Javier
dc.date.accessioned	2017-09-06T09:15:51Z
dc.date.available	2017-09-06T09:15:51Z
dc.date.issued	2007
dc.identifier.citation	Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4693, pp. 435-443.
dc.identifier.isbn	978-3-540-74826-7 (Print) / 978-3-540-74827-4 (Online)
dc.identifier.issn	0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.uri	http://hdl.handle.net/10366/135026
dc.description.abstract	This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Springer Science + Business Media
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess