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The objective of this study was to investigate Raman spectroscopy as a tool for the prediction of sensory quality in beef. Raman spectra were collected from M. longissimus thoracis et lumborum (LTL) muscle on a thawed steak frozen 48 h post-mortem. Another steak was removed from the muscle and aged for 14 days before being assessed for 12 sensory traits by a trained panel. The most accurate coefficients of determination of cross validation (R2CV) calibrated within the current study were for the trained sensory panel textural scores; particularly tenderness (0.46), chewiness (0.43), stringiness (0.35) and difficulty to swallow (0.33), with practical predictions also achieved for metallic flavour (0.52), fatty after-effect (0.44) and juiciness (0.36). In general, the application of mathematical spectral pre-treatments to Raman spectra improved the predictive accuracy of chemometric models developed. This study provides calibrations for valuable quality traits derived from a trained sensory panel in a non-destructive manner, using Raman spectra collected at a time-point compatible with meat management systems. [ABSTRACT FROM AUTHOR]

The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R2C) and cross-validation (R2CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R2CV: 0.91 (quartering, 24 h) and R2CV: 0.96 (LTL muscle, 48 h)) and drip loss (R2CV: 0.82 (quartering, 24 h) and R2CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine whether sire breed and/or castration had an effect on meat quality of M. longissimus thoracis et lumborum (LTL) muscle from crossbred bulls and steers and to investigate the relationship amongst the traits examined. Warner–Bratzler shear force (WBSF), intramuscular fat (IMF)%, cook-loss%, drip-loss%, colour (L*, a*, b*) and ultimate pH (upH) were determined in the LTL muscle from eight beef sire breeds representative of the Irish herd (Aberdeen Angus, Belgian Blue, Charolais, Hereford, Limousin, Parthenaise, Salers and Simmental). The results indicate that IMF%, cook-loss% and drip-loss% were associated with breed (p < 0.05); while WBSF, IMF% and cook-loss% differ between genders (p < 0.05). Steer LTL had a greater IMF% and exhibited reduced WBSF and cook-loss% in comparison to the bull LTL (p < 0.05). This study provides greater insight into how quality traits in beef are influenced by breed and gender and will support the industry to produce beef with consistent eating quality. [ABSTRACT FROM AUTHOR]

Rapid prediction of beef quality remains a challenge for meat processors. This study evaluated the potential of Raman spectroscopy followed by chemometrics for prediction of Warner-Bratzler shear force (WBSF), intramuscular fat (IMF), ultimate pH, drip-loss and cook-loss. PLS regression models were developed based on spectra recorded on frozen-thawed day 2 longissimus thoracis et lumborum muscle and validated using test sets randomly selected 3 times. With the exception of ultimate pH, models presented notable performance in calibration (R2 ranging from 0.5 to 0.9; low RMSEC) and, despite variability in the results, promising predictive ability: WBSF (RMSEP ranging from 4.6 to 9 N), IMF (RMSEP ranging from 0.9 to 1.1%), drip-loss (RMSEP ranging from 1 to 1.3%) and cook-loss (RMSEP ranging from 1.5 to 2.9%). Furthermore, the loading values indicated that the physicochemical variation of the meat influenced the models. Overall, results indicated that Raman spectroscopy is a promising technique for routine quality assessments of IMF and drip-loss, which, with further development and improvement of its accuracy could become a reliable tool for the beef industry. • Raman spectroscopy was evaluated for prediction of WBSF, IMF, drip-loss and cook-loss. • Models were developed using PLS regression and validated using independent data sets. • IMF, drip-loss and cook-loss calibrations exhibited notable fit; WBSF moderate fit. • Despite variability, in average all models showed promising predictive ability. • The loading values indicated that variation in the physicochemical composition of the meat influenced the models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]