Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum

Publication date: Available online 16 August 2019Source: Meat ScienceAuthor(s): Jamie Cafferky, Torres Sweeney, Paul Allen, Amna Sahar, Gerard Downey, Andrew Cromie, Ruth M. HamillAbstractThe aim of this study was to calibrate chemometric models to predict beef M. longissimus thoracis et lumborum (LTL) sensory and textural values using visible-near infrared (VISNIR) spectroscopy. Spectra were collected on the cut surface of LTL steaks both on-line and off-line. Cooked LTL steaks were analysed by a trained beef sensory panel as well as undergoing WBSF analysis. The best coefficients of determination of cross validation (R2CV) in the current study were for textural traits (WBSF = 0.22; stringiness = 0.22; crumbly texture = 0.41: all 3 models calibrated using 48 h post-mortem spectra), and some sensory flavour traits (fatty mouthfeel = 0.23; fatty after-effect = 0.28: both calibrated using 49 h post-mortem spectra). The results of this experiment indicate that VISNIR spectroscopy has potential to predict a range of sensory traits (particularly textural traits) with an acceptable level of accuracy at specific post-mortem times.

Source: Meat Science - August 17, 2019 Category: Food Science Source Type: research