Development of VISNIR predictive regression models for ultimate pH, meat tenderness (shear force) and intramuscular fat content of Australian lamb

Matthew I. Knight, Nick Linden, Eric N. Ponnampalam, Matthew G. Kerr, Wayne G. Brown, David L. Hopkins, Stuart Baud, Alex J. Ball, Claus Borggaard, Ian Wesley

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Abstract

This study investigated the effectiveness of visible-near-infrared (VISNIR) spectroscopy at classifying Australian lamb for: a) ultimate pH (pH 24), b) meat tenderness (i.e. shear force at day 5 of ageing, SF5) and c) intramuscular fat (IMF) content at 24 h post-slaughter using a custom-made handheld probe coupled with the ASD Labspec Pro instrument. VISNIR predictive regression models were developed. In the loin muscle (M. longissimus thoracis et lumborum), the models classified the predicted pH 24, SF5 and IMF content at above or below a threshold value with 94%, 98% and 88% accuracy, respectively. The observed difference between the actual and predicted value (i.e. the standard error of cross validation, SECV) for ultimate pH and IMF content are approaching accuracies required to attain highly reliable Meat Standards Australia grading standards. However, further development is required to improve the SECV for SF5.
Original languageEnglish
Pages (from-to)102-108
Number of pages7
JournalMeat Science
Volume155
Early online date11 May 2019
DOIs
Publication statusPublished - Sep 2019

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    Knight, M. I., Linden, N., Ponnampalam, E. N., Kerr, M. G., Brown, W. G., Hopkins, D. L., Baud, S., Ball, A. J., Borggaard, C., & Wesley, I. (2019). Development of VISNIR predictive regression models for ultimate pH, meat tenderness (shear force) and intramuscular fat content of Australian lamb. Meat Science, 155, 102-108. https://doi.org/10.1016/j.meatsci.2019.05.009