Development of VISNIR predictive regression models for ultimate pH, meat tenderness (shear force) and intramuscular fat content of Australian lamb. (September 2019)

• Record Type: Journal Article
• Authors: Knight, Matthew I.; Linden, Nick; Ponnampalam, Eric N.; Kerr, Matthew G.; Brown, Wayne G.; Hopkins, David L.; Baud, Stuart; Ball, Alex J.; Borggaard, Claus; Wesley, Ian
• Is Part Of: Meat science. Volume 155(2019)
• Page Start: 102
• Page End: 108
• Publication Date: 2019-09
• Subjects: Meat quality -- Lamb grading -- MSA sheep meat -- Objective measurement; Meat -- Periodicals; Meat industry and trade -- Periodicals; Viande -- Périodiques; Viande -- Industrie -- Périodiques; Meat; Meat industry and trade; Periodicals; 641.36
• DOI: 10.1016/j.meatsci.2019.05.009
• Languages: English
• ISSNs: 0309-1740

Abstract

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.