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dc.contributor.authorInce, N. F.en_US
dc.contributor.authorGoksu, F.en_US
dc.contributor.authorTewfik, A. H.en_US
dc.contributor.authorOnaran, I.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorPearson, T. C.en_US
dc.date.accessioned2019-02-05T06:38:45Z
dc.date.available2019-02-05T06:38:45Zen_US
dc.date.issued2008en_US
dc.identifier.issn2162-643X
dc.identifier.urihttp://hdl.handle.net/11693/48837
dc.description.abstractDue to low consumer acceptance and the possibility of immature kernels, closed-shell pistachio nuts should be separated from open-shell nuts before reaching the consumer. A system using impact acoustics as a means of classifying closed-shell nuts from open-shell nuts has already been shown to be feasible and have better discrimination performance than a mechanical system. The accuracy of an impact acoustics based system is determined by the signal processing and feature extraction procedures. In this article, a new time-frequency plain feature extraction and classification algorithm was developed to discriminate between open- and closed-shell pistachio nuts produced in the Gaziantep region of Turkey. The proposed approach relies on the analysis of the impact acoustics signal of pistachio nuts, which are emitted from their impact with a steel plate after dropping from a certain height. Features are extracted by decomposing the acoustic signals into time and frequency components, using double-tree undecimated wavelet packet transform. The most discriminative features from the dual tree nodes are selected by a wrapper strategy that includes the structural pruning of the double-tree feature dictionary. The proposed approach requires no prior knowledge of the relevant time or frequency content of the acoustic signals. The algorithm used a small number of features and achieved a classification accuracy of 91.7% on the validation data set, while separating the closed shells from the open ones. A previously implemented algorithm, which uses maximum signal amplitude, absolute integration, and gradient features, achieved 82% classification accuracy on the same dataset. The results show that the time-frequency features extracted from impact acoustics can be used successfully for classification of open- and closed-shell Turkish pistachios.en_US
dc.language.isoEnglishen_US
dc.source.titleBiological Engineering Transactionsen_US
dc.relation.isversionofhttp://doi.org/10.13031/2013.24476en_US
dc.subjectClassificationen_US
dc.subjectImpact acousticen_US
dc.subjectTurkish pistachiosen_US
dc.subjectUndecimated wavelet packet transformen_US
dc.titleDiscrimination between closed-and open-shell (Turkish) pistachio nuts using undecimated wavelet packet transformen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage159en_US
dc.citation.epage172en_US
dc.citation.volumeNumber1en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.13031/2013.24476en_US
dc.publisherAmerican Society of Agricultural and Biological Engineersen_US
dc.contributor.bilkentauthorÇetin, A. Enis
dc.identifier.eissn2330-0337
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958en_US


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