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

      Cafferky, Jamie; Sweeney, Torres; Allen, Paul; Sahar, Amna; Downey, Gerard; Cromie, A. R.; Hamill, Ruth; Department of Agriculture, Food and the Marine; 11/SF/311 (Elsevier, 2019-08-16)
      The 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.