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dc.contributor.authorHidayat, S. N.por
dc.contributor.authorRusman, A.por
dc.contributor.authorJulian, T.por
dc.contributor.authorTriyana, K.por
dc.contributor.authorVeloso, Ana C. A.por
dc.contributor.authorPeres, A. M.por
dc.identifier.citationHidayat, S. N.; Rusman, A.; Julian, T.; Triyana, K.; Veloso, Ana C. A.; Peres, A. M., Electronic nose coupled with linear and nonlinear supervised learning methods for rapid discriminating quality grades of superior java cocoa beans. International Journal of Intelligent Engineering and Systems, 12(6), 167-176, 2019por
dc.description.abstractAn electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors and a moisture-temperature sensor, was used for classifying three quality grades of superior java cocoa beans, namely fine cocoa dark bean < 20%, fine cocoa dark bean > 60%, and bulk cocoa bean that is a harder task compared to the discrimination of high versus low-quality cocoa beans. The E-nose signals were pre-processed using the maximum value method. The capability for discriminating the quality grade of the cocoa beans was checked by applying multivariate statistical tools, namely, linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural networks (ANN). For this, the experimental dataset was split into two subsets, one for training (i.e., establishing the classification models) and the other for external-validation purposes. Furthermore, hyperparameter optimization and K-fold cross-validation variant were implemented during the model training procedure to select the best classification models and to avoid over-fitting issues. The best predictive classification performance was obtained with the E-nose-MLP-ANN procedure, which allowed 99% of correct classifications (overall accuracy) for the training dataset and 95% of correct classifications (overall accuracy) for the external-validation dataset. The satisfactory results clearly demonstrated that the E-nose could be applied as a quality control tool in the cocoa industry, requiring minimum and simple sample preparation. © Intelligent Network and Systems Society.por
dc.description.sponsorshipThe authors thank the Directorate of Research and Community Service, Ministry of Research, Technology and Higher Education, the Republic of Indonesia for providing research grants of PTUPT 2019 (Contract No. 2688/UN1.DITLIT/DITLIT/LT/2019). The authors also like to acknowledge the financial support given by Associate Laboratory LSRE-LCM-UID/EQU/50020/2019, strategic funding UID/BIO/04469/2019-CEB, BioTecNorte operation (NORTE-01-0145-FEDER-000004) and strategic project PEst-OE/AGR/UI0690/2014 – CIMO, all funded by national funds through FCT/MCTES (PIDDAC).por
dc.publisherIntelligent Networks and Systems Societypor
dc.subjectArtificial neural networkspor
dc.subjectCocoa bean qualitypor
dc.subjectElectronic nosepor
dc.subjectLinear discriminant analysispor
dc.subjectSupport vector machinespor
dc.titleElectronic nose coupled with linear and nonlinear supervised learning methods for rapid discriminating quality grades of superior java cocoa beanspor
sdum.journalInternational Journal of Intelligent Engineering and Systemspor
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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