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.

In a previous study, we identified candidate causative variants located in 24 functional candidate genes for milk protein and fatty acid composition in Montbéliarde, Normande, and Holstein cows. We designed these variants on the custom part of the EuroG10K BeadChip (Illumina Inc., San Diego, CA), which is routinely used for genomic selection analyses in French dairy cattle. To validate the effects of these candidate variants on milk composition and to estimate their effects on cheesemaking properties, a genome-wide association study was performed on milk protein, fatty acid and mineral composition, as well as on 9 cheesemaking traits (3 laboratory cheese yields, 5 coagulation traits, and milk pH). All the traits were predicted from midinfrared spectra in the Montbéliarde cow population of the Franche-Comté region. A total of 194 candidate variants located in 24 genes and 17 genomic regions were imputed on 19,862 cows with phenotypes and genotyped with either the BovineSNP50 (Illumina Inc.) or the EuroG10K BeadChip. We then tested the effect of each SNP in a mixed linear model including random polygenic effects estimated with a genomic relationship matrix. We confirm here the effects of candidate causative variants located in 17 functional candidate genes on both cheesemaking properties and milk composition traits. In each candidate gene, we identified the most plausible causative variant: 4 are missense in the ALPL, SLC26A4, CSN3, and SCD genes, 7 are located in 5'UTR (AGPAT6), 3' untranslated region (GPT), or upstream (CSN1S1, CSN1S2, PAEP, DGAT1, and PICALM) regions, and 6 are located in introns of the SLC37A1, MGST1, CSN2, BRI3BP, FASN, and ANKH genes.

Franche-Comté is the primary producing region of Protected Designation of Origin cheeses in France. Normally, mid-infrared (MIR) prediction models for cheese-making property (CMP) traits are developed using individual bovine milks. However, considering the requests of all actors in the dairy sector, the present study aimed to assess the feasibility of MIR spectroscopy to develop CMP equations of Montbéliarde herd and dairy vat milks. For this purpose, 22 CMP traits were analyzed on samples collected in 2016 (half in February-March and half in May-June) from 100 commercial herds and 70 dairy vats (55 cheese dairies) located in Franche-Comté. These characteristics included 11 rennet coagulation traits and 8 lactic acidification traits measured in either soft cheese or pressed cooked cheese conditions and 3 laboratory curd yields. Models of MIR prediction for each of the 22 CMP traits were built using partial least squares regression with external validation by dividing the data set into calibration (70%) and validation (30%) sets. We confirmed that the variability of milk traits depends largely on the production scale and is higher for individual milk than for herd milk and even higher for vat milk. The best prediction models were obtained in herd milk samples for curd yields expressed in dry matter or fresh, with a coefficient of determination (R 2 ) in external validation of 0.78 and 0.77, respectively. As with individual milk, these traits are closely related to the gross composition of the milk and therefore easier to predict by MIR spectroscopy. However, these curd yield traits were poorly predicted (R 2 = 0.58) in vat milk samples due to their lower variability. In herd milk samples, prediction models of other CMP traits were poorly accurate except for the ratio of the time to obtain a standard firmness to the rennet coagulation time in soft cheese or pressed cooked cheese conditions, which showed R 2 > 0.66 in external validation. Such trait is important in qualifying the behavior of milk during cheese production. Prediction models of other CMP traits for either herd or vat milk samples had poor accuracy, and further work is needed to improve their performance.

La standardisation des teneurs en enzymes actives (chymosine et pepsine bovine) des agents coagulants permet de mettre en évidence les effets liés à la nature et à la composition des liquides de dissolution de ces enzymes. Dans cette étude ont été arbitrairement distingués après séparation, par ultrafiltration sur membrane 10 000, les enzymes actives (rétentat) et l'ultrafiltrat que l'on peut qualifier du terme « support ».Un examen systématique de différents agents coagulants actuellement proposés en fromagerie montre une très grande variation de la composition des « supports » notamment en ce qui concerne leurs teneurs en sel et en composés azotés. Cette variation a un effet significatif sur le comportement rhéologique des caillés, la cinétique de la synérèse et finalement sur le rendement fromager et la qualité des produits finaux. A titre indicatif, les essais réalisés en fromagerie de pâte molle (carrés de l'Est et pâte solubilisée) montrent que le coagulant Mucagel (enzyme de Mucor miehei et "support» de l'extrait de présure) conduit à une amélioration significative des paramètres fromagers par rapport au coagulant composé de l'enzyme de Mucor miehei et de son « support» habituel.A partir de l'ensemble des résultats obtenus, il est proposé d'adapter le couple enzyme + « support» au type de fabrication fromagère envisagée.Mots clés: Présure -Coagulant -Composition biochimique -Fabrication fromagère. Summary Technological ability of different milk coagulating agents. Influence of the diluting liquid compositionEffect of nature of coagulating agent on cheesemaking parameters and on final charateristics of end products was studied. From a systematical survey of commercial coagulating agents containing rennet and fungal (Mucor rniehei and Endothia parasitica) enzymes diluted in different solutions, it appears that the composition of the diluting enzyme Iiquid strongly varies in terms of salts and nitrogenous component contents. Such a variation has significant effect on the rheological behaviour of milk curds and their kinetics of syneresis. For example, during cheesemaking of soft cheeses, uses of a diluting liquid obtained by ultrafiltrating rennet extract can significantly improve cheese yielding capacity of the milk by Mucor miehei enzyme. Such results are correlated to the contents in minerai salts (NaCl and phosphate) and in organic nitrogen of this UF extract compared to those of usual diluting liquid of commercial Mucor rniehei preparation.

-The influence of the temperature at the rennet addition (31.2, 32.0 and 32.8 °C) and of the starter type on the soft cheese drainage process was studied. Off-line measurements of pH, calcium and phosphorous concentrations, as well as dry matter of the curd were performed in parallel with on-line measurements of the weight and pH of the whey. Only the starter type was found to have a significant influence on the curd and whey pH, on the calcium concentration in the curd and on the calcium/dry matter ratio. Correlations were established between weight of the whey and dry matter of the curd, as well as between pH of the whey and physico-chemical measurements in the curd. Linear regressions gave satisfactory results only when performed separately for each type of starter. Artificial neural networks allowed the building of common models for both starters and predicting curd pH, calcium concentration and the calcium/dry matter ratio using the pH of the whey at one hour after moulding.Drainage / curd characteristics / pH / starter / on-line measurement / modelling Résumé -Modélisation du pH, de l'extrait sec et de la minéralisation du caillé pendant l'égouttage d'un fromage à pâte molle. Au cours de l'égouttage en moule de fromages à pâte molle, des mesures de pH, de concentrations en calcium et phosphore ainsi que de l'extrait sec du caillé ont été effectuées parallèlement aux mesures en ligne de la masse et du pH du sérum. Les effets de la température d'emprésurage (31,2 ; 32,0 et 32,8 °C) et de la nature des levains ont été évalués sur l'évolution de ces grandeurs. Seule la nature des levains présente un effet significatif sur les évolutions du pH du caillé et du sérum, sur la concentration en calcium du caillé et sur le rapport Ca ++ /extrait sec. Des corrélations sont établies entre la masse de sérum égoutté et l'extrait sec du caillé ainsi qu'entre le pH du sérum et les grandeurs physico-chimiques mesurées dans le caillé. Dans ces relations, les régressions linéaires multiples n'apportent des résultats corrects que lorsqu'elles sont établies séparément, en fonction du levain utilisé. La mise en oeuvre de réseaux de neurones artificiels permet d'établir un modèle unique pour l'ensemble des variables recherchées.