Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/65544

TitleElectronic nose coupled with linear and nonlinear supervised learning methods for rapid discriminating quality grades of superior java cocoa beans
Author(s)Hidayat, S. N.
Rusman, A.
Julian, T.
Triyana, K.
Veloso, Ana C. A.
Peres, A. M.
KeywordsArtificial neural networks
Cocoa bean quality
Electronic nose
Linear discriminant analysis
Support vector machines
Issue date2019
PublisherIntelligent Networks and Systems Society
JournalInternational Journal of Intelligent Engineering and Systems
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, 2019
Abstract(s)An 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.
TypeArticle
URIhttp://hdl.handle.net/1822/65544
DOI10.22266/ijies2019.1231.16
ISSN2185-310X
e-ISSN2185-3118
Publisher versionhttp://www.inass.org/publications.html
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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