Partial least squares and machine learning for the prediction of intramuscular fat content of lamb loin. (July 2021)

• Record Type: Journal Article
• Authors: Fowler, Stephanie M.; Wheeler, David; Morris, Stephen; Mortimer, Suzanne I.; Hopkins, David L.
• Is Part Of: Meat science. Volume 177(2021)
• Publication Date: 2021-07
• Subjects: Sheep meat -- Meat processing -- Carcases assessment -- Near infra-red spectroscopy -- Chemometrics; 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.2021.108505
• Languages: English
• ISSNs: 0309-1740

Abstract

Abstract: Given the paucity of lamb carcase grading tools, there is a distinct need for the development of rapid, non-destructive grading tools for Australian lamb carcases, particularly fat content given its importance to meat and eating quality. The aim of the current study was to determine the potential for Near Infrared (NIR) spectroscopy to predict IMF using Partial Least Squares (PLS) and machine learning analysis methods. As such, 299 lamb loins were measured using a NIR fibre optic device, a sample was excised for Soxhlet determination of IMF content and prediction models were created using either PLS or machine learning analyses methods. IMF prediction model outcomes were similar between analysis methods with an R 2 = 0.6 and RMSE = 0.84 and R 2 = 0.65 and RMSE = 0.72, respectively. This study highlighted that spectra from one slaughter varied greatly from the two succeeding slaughters and wavelengths selected between studies are not consistent.