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Enzymatic protein hydrolysis can be used industrially for refining peptides from poultry by-products. The aim is to introduce these peptides for human consumption, but today the majority is used for feed or pet food. One main barrier for entering the human market is unknown and unstable peptide product qualities. Reliable analytical methods for characterising the peptide fraction is therefore vital for optimizing and controlling the product quality. In this work, we investigate the potential of using a large database of dry-film Fourier-transform Infrared (FTIR) fingerprints to gain new insight in quality variations of peptide products. The database cover >1300 samples from laboratory experiments performed over several years. We found that the FTIR fingerprints contain patterns that are stable across experiments and time, and that variations in the fingerprint can be related to raw material composition and processing factors. We also show that the database can be used for evaluating industrial samples. By comparing the FTIR fingerprints of industrial samples with those in the database, we could see systematic changes in product quality over time and make hypotheses about the underlying causes for change. These results lay the foundation for using dry-film FTIR spectroscopy as an industrial tool for product and process optimisation and control. • FTIR fingerprints of protein hydrolysates contain reproducible patterns. • The fingerprints can be related to raw material composition and process parameters. • A database of FTIR fingerprints can be used to benchmark industry samples. • Dry-film FTIR is a promising technology for optimizing industrial hydrolysis processes. [ABSTRACT FROM AUTHOR]

Raman, near-infrared and fluorescence spectroscopy were evaluated for determination of collagen content in ground meat. Two sample sets were used (i.e. ground beef and ground poultry by-products), and collagen concentrations (measured as hydroxyproline) varied in the ranges 0.1–3.3% in the beef samples and 0.4–1.5% in the poultry samples. Similar validation results for hydroxyproline were obtained for NIRS (R2 = 0.82 and RMSECV = 0.11%) and Raman (R2 = 0.81 and RMSECV = 0.11%) for the poultry samples. For the beef samples, NIRS obtained slightly less accurate results (R2 = 0.89, RMSECV = 0.25%) compared to Raman (R2 = 0.94, RMSECV = 0.19%), most likely due to less representative sampling. Fluorescence spectroscopy gave higher prediction errors (RMSECV = 0.50% and 0.13% for beef and poultry, respectively). This shows that Raman spectroscopy employing a scanning approach for representative sampling is a potential tool for on-line determination of collagen in meat. • Non-destructive determination of collagen in meat products by Raman spectroscopy. • Regression models were compared for different spectroscopic techniques. • Specific models for collagen and proteins are possible with Raman spectroscopy. [ABSTRACT FROM AUTHOR]