Identification of damaged wheat kernels and cracked-shell hazelnuts with impact acoustics time-frequency patterns

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage1469en_US
dc.citation.issueNumber4en_US
dc.citation.spage1461en_US
dc.citation.volumeNumber51en_US
dc.contributor.authorInce, N. F.en_US
dc.contributor.authorOnaran, I.en_US
dc.contributor.authorPearson, T.en_US
dc.contributor.authorTewfik, A. H.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorKalkan, H.en_US
dc.contributor.authorYardimci, Y.en_US
dc.date.accessioned2016-02-08T10:08:30Z
dc.date.available2016-02-08T10:08:30Z
dc.date.issued2008en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractA new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with a PC-based data acquisition system. These signals were segmented with a flexible local discriminant bases (F-LDB) procedure in the time-frequency plane to extract discriminative patterns between damaged and undamaged food kernels. The F-LDB procedure requires no prior knowledge of the relevant time or frequency indices of the impact acoustics signals for classification. The method automatically finds all crucial time-frequency indices from the training data by combining local cosine packet analysis and a frequency axis clustering approach, which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post-processed by principal component analysis, and fed to a linear discriminant classifier. Experimental results establish the superior performance of the proposed approach when compared to prior techniques reported in the literature or used in the field. The new approach separated damaged wheat kernels (IDK, pupal, and scab) from undamaged wheat kernels with 96%, 82%, and 94% accuracy, respectively. It also separated cracked-shell hazelnuts from those with undamaged shells with 97.1% accuracy. The adaptation capability of the algorithm to the time-frequency patterns of signals makes it a universal method for food kernel inspection that can resist the impact acoustic variability between different kernel and damage types.en_US
dc.identifier.doi10.13031/2013.25226en_US
dc.identifier.eissn2151-0040
dc.identifier.issn2151-0032
dc.identifier.urihttp://hdl.handle.net/11693/23068
dc.language.isoEnglishen_US
dc.publisherAmerican Society of Agricultural and Biological Engineersen_US
dc.relation.isversionofhttps://doi.org/10.13031/2013.25226en_US
dc.source.titleAmerican Society of Agricultural and Biological Engineers. Transactionsen_US
dc.subjectAdaptive time-frequency analysisen_US
dc.subjectFood kernel inspectionen_US
dc.subjectImpact acousticsen_US
dc.subjectKernel classificationen_US
dc.titleIdentification of damaged wheat kernels and cracked-shell hazelnuts with impact acoustics time-frequency patternsen_US
dc.typeArticleen_US
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