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dc.contributor.authorZhao, Ming
dc.contributor.authorNian, Yingqun
dc.contributor.authorAllen, Paul
dc.contributor.authorDowney, Gerard
dc.contributor.authorKerry, Joseph P.
dc.contributor.authorO’Donnell, Colm P.
dc.date.accessioned2020-08-04T11:40:24Z
dc.date.available2020-08-04T11:40:24Z
dc.date.issued2018-04-23
dc.identifier.citationZhao M, Nian Y, Allen P, Downey G, Kerry JP, O’Donnell CP. Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes. Data in Brief 2018;19:1355-1360; doi https://doi.org/10.1016/j.dib.2018.04.056en_US
dc.identifier.issn2352-3409
dc.identifier.urihttp://hdl.handle.net/11019/2229
dc.descriptionpeer-revieweden_US
dc.description.abstractThe data presented in this article are related to the research article entitled “Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef” [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250–3380 cm−1, 900–1800 cm−1 and 1300–2800 cm−1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300–2800 cm−1.en_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofseriesData in Brief;
dc.subjectSelected Raman shift rangesen_US
dc.subjectSensory attributesen_US
dc.subjectBull beefen_US
dc.subjectPartial least squares regression modelsen_US
dc.titlePerformances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.dib.2018.04.056
dc.contributor.sponsorTeagasc Walsh Fellowship Programmeen_US
dc.source.volume19
dc.source.beginpage1355-1360
refterms.dateFOA2019-02-05T00:00:00Z


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