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dc.contributor.authorPrieto, Nuria-
dc.contributor.authorRoss, D. W.-
dc.contributor.authorNavajas, E. A.-
dc.contributor.authorNute, G. R.-
dc.contributor.authorRichardson, R. I.-
dc.contributor.authorHyslop, J. J.-
dc.contributor.authorSimm, G.-
dc.contributor.authorRoehe, R.-
dc.date.accessioned2010-03-11T12:28:53Z-
dc.date.available2010-03-11T12:28:53Z-
dc.date.issued2009-
dc.identifier.citationMeat Science, 83: 96-103 (2009)en_US
dc.identifier.issn0309-1740-
dc.identifier.urihttp://hdl.handle.net/10261/22262-
dc.description8 pages, 3 figures, 3 tables.en_US
dc.description.abstractThe aim of this study was to assess the on-line implementation of visible and near infrared reflectance (Vis-NIR) spectroscopy as an early predictor of beef quality traits, by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir. Samples from M. longissimus thoracis from 194 heifers and steers were scanned at quartering 48 h postmortem over the Vis-NIR spectral range from 350 to 1800 nm. Thereafter, samples from M. longissimus thoracis et lumborum were analysed for colour (L*, a*, b*; 48 h postmortem), cooking loss (14 days postmortem), instrumental texture (Volodkevitch, 10 days aged meat; slice shear force, 3 and 14 days aged meat) and sensory characteristics. Vis-NIR calibrations, tested by cross-validation, showed high predictability for L*, a* and b* (R-2 = 0.86, 0.86 and 0.91; SECV = 0.96, 0.95 and 0.69, respectively). The accuracy of Vis-NIR to estimate cooking loss and instrumental texture ranged from R-2 = 0.31 to 0.54, suggesting relatively low prediction ability. Sensory characteristics assessed on 14 days aged meat samples showed R 2 in the range from 0.21 (juiciness) to 0.59 (flavour). Considering the subjective assessment of sensory characteristics the correlations of Vis-NIR measurements and several meat quality traits in the range from 0.46 to 0.95 support the use of on-line Vis-NIR in the abattoir. Improvement of predictability was achieved if only extreme classes of meat characteristics have to be predicted by Vis-NIR spectroscopy.en_US
dc.description.sponsorshipWe are grateful to the Scottish Government for funding the research and Scotbeef, Q.M.S., B.C.F. and Signet for their substantial support. Also, we thank SAC colleagues Kirsty McLean, Laura Nicoll, Claire Anderson, Mhairi Jack, Ann McLaren, Ruth Turl, Elizabeth Goodenough, John Gordon, Lesley Deans, Alex Moir and Cameron Craigie their help in the experimental work and University of Bristol technical staff Duncan Marriott, Anne Baker and Sue Hughes, for texture and sensory analysis. N. Prieto is grateful to the Ministry of Science and Innovation (MICINN), Spain, for financial assistance via a post-doctoral grant.en_US
dc.format.extent22195 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsclosedAccessen_US
dc.subjectNear infrared reflectance spectroscopyen_US
dc.subjectBeefen_US
dc.subjectMeat qualityen_US
dc.subjectChemical-physical parametersen_US
dc.subjectSensory characteristicsen_US
dc.titleOn-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef qualityen_US
dc.typeartículoen_US
dc.identifier.doi10.1016/j.meatsci.2009.04.005-
dc.description.peerreviewedPeer revieweden_US
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.meatsci.2009.04.005en_US
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