Rico Scheier

Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss

A prototype handheld Raman system was used as a rapid non-invasive optical device to measure raw sheep meat to estimate cooked meat tenderness and cooking loss. Raman measurements were conducted on m. longissimus thoracis et lumborum samples from two sheep flocks from two different origins which had been aged for five days at 3-4°C before deep freezing and further analysis. The Raman data of 140 samples were correlated with shear force and cooking loss data using PLS regression. Both sample origins could be discriminated and separate correlation models yielded better correlations than the joint correlation model. For shear force, R(2)=0.79 and R(2)=0.86 were obtained for the two sites. Results for cooking loss were comparable: separate models yielded R(2)=0.79 and R(2)=0.83 for the two sites. The results show the potential usefulness of Raman spectra which can be recorded during meat processing for the prediction of quality traits such as tenderness and cooking loss.

Prediction of pork quality at the slaughter line using a portable Raman device

Raman spectroscopy is providing a fingerprint of the early postmortem metabolism in meat. In this study, for the first time, Raman spectroscopy is shown to measure and predict quality traits of intact muscles at the slaughtering process. Porcine semimembranosus muscles (N=151) were measured 30-60 min post mortem at the veterinarian line of a commercial abattoir using a prototype handheld Raman device. The Raman spectra were regressed against technologically important quality traits as measured with classic reference methods. Predicting pH35, pH24 and drip loss with PLSR yielded coefficients of determination of 0.75, 0.58 and 0.83 and root mean square errors of cross validation of 0.09, 0.05 and 0.6%, respectively. This is demonstrating the on-line potential of early postmortem Raman spectra to measure pH35 and to predict pH24 and drip loss.

Assessment of tenderness of aged bovine gluteus medius muscles using Raman spectroscopy.

A portable 671 nm Raman system was evaluated as a rapid and non-destructive device for the assessment of beef tenderness using 175 gluteus medius muscles (99 for calibration, 76 for validation) aged at -1 °C and 7 °C for fourteen days. Raman and shear force (SF) measurements were performed with the aged beef. The samples stored at -1 °C showed on average only slightly increased SF values. The correlation of Raman spectra with SF using partial least squares regression yielded cross-validated predictions of SF for both storage temperatures with coefficients of determination R(2)cv=0.33-0.79. Validation with independent samples resulted in predictions with R(2)val=0.33. Using thresholds between 30 and 49N, tough and tender samples could be discriminated with partial least squares discriminant analysis with 70-88% and 59-80% accuracy during cross-validation and validation, respectively. These results demonstrate the principle feasibility to predict the SF and thus toughness of raw, aged gluteus beef cuts with a portable Raman device showing potential for grading beef cuts.

Meat quality prediction using Raman spectroscopy and chemometrics.

The feasibility of using Raman spectroscopy as a fast and non-invasive method to monitor the quality parameters in pork meat has been investigated. For this application an online prediction methodology has not been established yet. Based on raw Raman spectra of 10 pork semimembranosus muscles a range of data pre-processing and multivariate calibration methodology have been used to develop online predictive models for the meat quality parameters: the lactate and pH. The linear and nonlinear algorithms studied were comparatively analysed for speed, robustness and accuracy. Identifying the best “efficiency” evaluation procedure represented the final milestone of the present study. Thus with a cross-validated r2 value for both pH and lactate of 0.97, a RMSECV of 4.5 mmol/l for the lactate prediction and 0.06 units for the pH prediction, locally weighted regression provided the most accurate and robust model. This prove the feasibility of using Raman spectroscopy for online meat quality control applications.