Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics
作者： Amna Sahar , Paul Allen , Torres Sweeney , Jamie Cafferky , Gerard Downey
DOI: 10.3390/FOODS8110525
关键词: Near-infrared spectroscopy 、 Mathematics 、 Musculus longissimus thoracis 、 Visible near infrared 、 Chemical quality 、 Spectroscopy 、 Calibration 、 Analytical chemistry 、 Chemometrics 、 Coefficient of determination
摘要: The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe applied on the exposed surface carcass for collection spectra, including neck and rump (1 h 2 post-mortem after quartering, i.e., 24 25 post-mortem) boned-out LTL muscle (48 49 post-mortem). In parallel, reference analysis parameters beef ultimate pH, colour (L, a*, b*), cook loss drip conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used correlate spectral information with muscle. Different mathematical pre-treatments their combinations improve model accuracy, which evaluated basis coefficient determination calibration (R2C) cross-validation (R2CV) root-mean-square error (RMSEC) (RMSECV). Reliable achieved pH (R2CV: 0.91 (quartering, h) R2CV: 0.96 (LTL muscle, 48 h)) 0.82 0.99 lower RMSECV values. results show Vis–NIR online prediction certain over different time periods.
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