Water-holding capacity (WHC) is one of the most important quality traits in meat, and the main aim of this thesis was to examine the potential for rapid spectroscopic techniques to predict WHC in meat. A secondary aim was to examine the potential for spectroscopic techniques to analyze pH and proteolysis; mechanisms known to affect WHC of meat.
A model system consisting of isolated myofibrils from pork was used to investigate if spectroscopic techniques have potential to identify changes in samples with different pH or degree of protein degradation. Raman, Fourier transform-infrared (FT-IR), near-infrared (NIR) and fluorescence spectroscopy were used for analyses. Raman and FT-IR spectroscopy performed very well in the pH- and proteolysis experiment. Changes in protein secondary structure and protonation of carboxylic acid side chains of amino acids were affected by changes in pH. Degree of protein degradation affected spectral regions related to breakage of peptide bonds, such as CN-vibrations and carboxylic acid vibration caused by C-terminal formation, as well as changes in protein secondary structure. NIR performed poorly in the pH experiment, but performed reasonably well
for dried samples in the proteolysis experiment, attributing this to an increased ability to form protein gels at low degrees of protein degradation. Fluorescence spectroscopy performed worse in the proteolysis experiment than in the pH experiment, attributing the performance in the pH experiment to a pH-related shift caused by changes in the microenvironment of tryptophan.
A study analyzing 122 samples from longissimus lumborum of Norwegian landrace boars was conducted to investigate if spectroscopic techniques have the potential to predict WHC and estimate ultimate pH in pork. WHC was measured as EZ-DripLoss and drip loss formed during eightdays of vacuum storage. Assessment of results from partial least squares regression (PLSR) analyses from spectroscopy and reference measurements showed that Raman spectroscopy performed the best, followed by NIR and at last, fluorescence. PLSR models from Raman spectroscopy gave coefficient of correlation from cross validation (Rcv2) of 0.51, 0.41 and 0.49 and root mean square error of cross validation (RMSECV) of 1.2, 0.82 and 0.06 for EZ-DripLoss, vacuum drip loss and pH,
respectively. In comparison, NIR yielded PLSR models with Rcv2 of 0.27, 0.16 and 0.29 and RMSECV of 1.5, 0.97 and 0.07 for EZ-DripLoss, vacuum drip loss and pH, respectively. Regarding pH in meat, changes in Raman spectra related to protein secondary structure were similar in the model system and in meat. Changes in carboxylic acid protonation were not detected in meat, but signals from molecules related to metabolism were identified.
In conclusion, this highly encourages more research using Raman spectroscopy for analysis of meat quality
