Quality classification of corn tortillas using computer vision

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dc.contributor.authorMery, Domingo
dc.contributor.authorChanona Perez, Jorge J.
dc.contributor.authorSoto, Alvaro
dc.contributor.authorMiguel Aguilera, Jose
dc.contributor.authorCipriano, Aldo
dc.contributor.authorVelez Rivera, Nayeli
dc.contributor.authorArzate Vazquez, Israel
dc.contributor.authorGutierrez Lopez, Gustavo F.
dc.date.accessioned2024-01-10T13:16:51Z
dc.date.available2024-01-10T13:16:51Z
dc.date.issued2010
dc.description.abstractComputer vision is playing an increasingly important role in automated visual food inspection. However, quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. Cross-validation yielded a performance of 95% in the classification of the five hedonic sub-classes. Additionally, using only 10 of the selected features and a simple statistical classifier, it was possible to determine the origin of the tortillas with a performance of 96%. We believe that the proposed framework opens up new possibilities in the field of automated visual inspection of tortillas. (c) 2010 Elsevier Ltd. All rights reserved.
dc.description.funderLACCIR Virtual Institute
dc.description.funderInstituto Politecnico Nacional (IPN, Mexico)
dc.description.funderCONACYT-Mexico
dc.fechaingreso.objetodigital27-03-2024
dc.format.extent8 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jfoodeng.2010.07.018
dc.identifier.issn0260-8774
dc.identifier.urihttps://doi.org/10.1016/j.jfoodeng.2010.07.018
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78618
dc.identifier.wosidWOS:000282491300003
dc.information.autorucIngeniería;Aguilera JM;S/I;99054
dc.information.autorucIngeniería;Cipriano A;S/I;99102
dc.information.autorucIngeniería;Mery D;S/I;102382
dc.information.autorucIngeniería;Soto A;S/I;73678
dc.issue.numero4
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final364
dc.pagina.inicio357
dc.publisherELSEVIER SCI LTD
dc.revistaJOURNAL OF FOOD ENGINEERING
dc.rightsacceso restringido
dc.subjectCorn tortillas
dc.subjectComputer vision
dc.subjectAutomated visual inspection
dc.subjectSensorial panel
dc.subjectPATTERN-RECOGNITION
dc.subjectMOMENT INVARIANTS
dc.subjectCOMPONENTS
dc.subjectSELECTION
dc.subjectTEXTURE
dc.subjectIMAGES
dc.subjectFLOUR
dc.subjectFOOD
dc.subject.ods02 Zero Hunger
dc.subject.odspa02 Hambre cero
dc.titleQuality classification of corn tortillas using computer vision
dc.typeartículo
dc.volumen101
sipa.codpersvinculados99054
sipa.codpersvinculados99102
sipa.codpersvinculados102382
sipa.codpersvinculados73678
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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