The objective of this study was to determine whether processing could modify the resistance of casein (CN) to digestion in infants. A range of different dairy matrices was manufactured from raw milk in a pilot plant and subjected to in vitro digestion using an infant gut model. Digestion products were identified using MS and immunochemical techniques. Results obtained showed that CNs were able to resist digestion, particularly κ- and αs(2)-CN. Resistant areas were identified and corresponded to fragments hydrophobic at pH 3.0 (gastric conditions) and/or carrying post-translational modifications (phosphorylation and glycosylation). Milk processing led to differences in peptide patterns and heat treatment of milk tended to increase the number of peptides found in digested samples. This highlights the likely impact of milk processing on the allergenic potential of CNs.

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Dairy industries are interested to know the heat treatment undergone by milk for controlling the quality of drinking milks or to control their heating systems. The purpose of this work was to develop a specific and sensitive technique for classification of the heat treatment a milk has been submitted to, without disposing of the original raw milk. For this purpose, alpha-lactalbumin was chosen as a bioindicator of heat treatment, and monoclonal antibodies specific for its native or heat-denatured form were raised and used in two inhibition ELISAs. ELISA allowed differentiation among raw, pasteurized, ultrahigh-temperature-treated, and sterilized milks without even having to know the alpha-lactalbumin concentration of the original raw milk. However, this technique was more suitable for intense heat treatments such as UHT treatment and sterilization because of the heat stability of alpha-lactalbumin.

Assessing the cheese-making properties (CMP) of milks with a rapid and cost-effective method is of particular interest for the Protected Designation of Origin cheese sector. The aims of this study were to evaluate the potential of mid-infrared (MIR) spectra to estimate coagulation and acidification properties, as well as curd yield (CY) traits of Montbéliarde cow milk. Samples from 250 cows were collected in 216 commercial herds in Franche-Comté with the objectives to maximize the genetic diversity as well as the variation in milk composition. All coagulation and CY traits showed high variability (10 to 43%). Reference analyses performed for soft (SC) and pressed cooked (PCC) cheese technology were matched with MIR spectra. Prediction models were built on 446 informative wavelengths not tainted by the water absorbance, using different approaches such as partial least squares (PLS), uninformative variable elimination PLS, random forest PLS, Bayes A, Bayes B, Bayes C, and Bayes RR. We assessed equation performances for a set of 20 CMP traits (coagulation: 5 for SC and 4 for PCC; acidification: 5 for SC and 3 for PCC; laboratory CY: 3) by comparing prediction accuracies based on cross-validation. Overall, variable selection before PLS did not significantly improve the performances of the PLS regression, the prediction differences between Bayesian methods were negligible, and PLS models always outperformed Bayesian models. This was likely a result of the prior use of informative wavelengths of the MIR spectra. The best accuracies were obtained for curd yields expressed in dry matter (CY DM ) or fresh (CY FRESH ) and for coagulation traits (curd firmness for PCC and SC) using the PLS regression. Prediction models of other CMP traits were moderately to poorly accurate. Whatever the prediction methodology, the best results were always obtained for CY traits, probably because these traits are closely related to milk composition. The CY DM predictions showed coefficient of determination (R 2 ) values up to 0.92 and 0.87, and RS y,x values of 3 and 4% for PLS and Bayes regressions, respectively. Finally, we divided the data set into calibration (2/3) and validation (1/3) sets and developed prediction models in external validation using PLS regression only. In conclusion, we confirmed, in the validation set, an excellent prediction for CY DM [R 2 = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CY FRESH (R 2 = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R 2 ≥ 0.70, RPD ≥1.8), which enable quantitative prediction.

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