2026-09-16T08:14:44Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1350262025-04-30T20:38:20Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811

Baruque, Bruno Corchado Rodríguez, Emilio Santiago Yin, Hujun Rovira Carballido, Jordi González, Javier 2017-09-06T09:15:51Z 2017-09-06T09:15:51Z 2007 Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 4693, pp. 435-443. 978-3-540-74826-7 (Print) / 978-3-540-74827-4 (Online) 0302-9743 (Print) / 1611-3349 (Online) http://hdl.handle.net/10366/135026 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. en https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 3.0 Unported Computer Science Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models info:eu-repo/semantics/conferenceObject