The present work reports the enzymatic valorisation of the protein fraction of scotta, a dairy by-product representing the exhausted liquid residue of ricotta production. Scotta was subjected to ultra-filtration with membrane cut-offs from 500 to 4 kDa and the obtained protein-enriched fractions were used for the optimization of enzyme-based digestions aimed at producing potentially bioactive peptides. Nine different commercial proteases were tested and the best digestion conditions were selected based on protein yield, fraction bioactivity and foreseen scale up processing costs. Scale up of the 3% Pancreatin or 5% Papain processes was performed up to 2 L (37°C or 60°C respectively, 1 h incubation), and the digestion efficiency increased with the reaction volume as well as antioxidant activity (up to 60 gBSA eq/L and to 1.7 gAA eq/L). Retentate 1 digested fractions also showed, for the first time in dairy-based peptides, anti-tyrosinase activity, up to 0.14 gKA eq/L. Digested proteins were sub-fractionated by means of physical membrane separations and 30–10 kDa fraction from Papain treatment showed the highest antioxidant and anti-tyrosinase activities. The peptide sequence of the most bioactive fractions was achieved.

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Milk filtration procedures are gaining relevance in the dairy industry because milk ultra- and nanofiltrates are used to increase milk processing efficiency, and as additives for products with improved nutraceutical properties. This study aimed to develop Fourier-transformed mid-infrared spectroscopy calibrations for ultra- and nanopermeate and retentate fractions of defatted and delactosated milk. A total of 154 samples from different milk fractions were collected and analyzed using reference methods to determine protein, solids-not-fat, glucose, and galactose content. The obtained values were matched with their respective Fourier-transformed mid-infrared spectroscopy spectra to develop new prediction models. Calibrations for each trait were built following 3 different approaches to get the best prediction models: (1) using the entire data set, (2) using 3 subsets based on component concentrations (level approach), and (3) using hierarchical clusters calculated with pairwise Mahalanobis distance among spectra (cluster approach). Calibrations were developed using partial least squares regression, after removing low signal-to-noise ratio wavelengths, and validated through a leave-one-out cross-validation procedure. In addition, the accuracy of the predicted values within each fraction was checked for each approach. Dividing the data set into subsets improved prediction models for each trait and for the samples in each milk fraction. Without considering milk fraction, the best improvement was observed for glucose and galactose. Glucose ratio performance deviation in cross-validation (RPD) increased from 7.42 to 11.31 and 11.06, for cluster and level approaches, respectively, whereas galactose RPD increased from 8.86 to 11.69 and 11.27 for cluster and level approaches, respectively. Considering milk fractions, the best improvement was observed for protein content, where RPD ranged from 0.08 to 6.06 for the whole data set calibration, whereas it ranged from 0.43 to 40.34 for the subset calibration approaches. Cluster and level approaches to build calibration models were comparable for samples from different fractions, suggesting that the 2 subsetting protocols should be both investigated to get the best prediction performances.

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