Mid-infrared spectrometry prediction of the cheese-making properties of raw Montbéliarde milks from herds and cheese dairy vats used for the production of Protected Designation of Origin and Protected Geographical Indication cheeses in Franche-Comté.

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 (R2) 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 (R2 = 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 R2 > 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.

Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows.

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 (CYDM) or fresh (CYFRESH) 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 CYDM predictions showed coefficient of determination (R2) values up to 0.92 and 0.87, and RSy,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 CYDM [R2 = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CYFRESH (R2 = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R2 ≥ 0.70, RPD ≥1.8), which enable quantitative prediction.

Modelling the competitive growth between Listeria monocytogenes and biofilm microflora of smear cheese wooden shelves.

The aim of this study was to investigate the mechanism of the observed inhibition of Listeria monocytogenes by the natural biofilm microflora (BM) on wooden shelves used in the ripening of a soft and smear cheese. For this, BM was harvested and we conducted a series of experiments in which two strains of L. monocytogenes were co-cultured with BM on glass fiber filters deposited on model cheeses. Compared to monoculture, L. monocytogenes growth rate in co-culture was not reduced but the growth of the pathogen stopped as soon as BM entered the stationary phase. This reduction in maximum population density can be explained by nutrient consumption and exhaustion by BM as no production of inhibitors by BM has been detected. This mechanism of pathogen inhibition has been previously described as the "Jameson effect".

Biofilm ecology of wooden shelves used in ripening the French raw milk smear cheese Reblochon de Savoie.

Little work has been carried out on the microbiology of wooden shelves supporting cheese during ripening, and the safety of their use during cheese ripening has frequently been asked. Microbial characterization (enumerations on specific growth media) and description of their physicochemical conditions (pH, water activity, and salt concentration) were determined on 50 wooden shelves of 3 different ages at the end of the cheese-ripening process, using cheeses from 8 farm producers. The experiments were performed during 2 different seasons (summer and autumn). Micrococci-corynebacteria and yeasts and molds were found to be the dominant microflora on the shelves. Leuconostocs, facultative heterofermentative lactobacilli, enterococci, staphylococci, and pseudomonads were also found but at lower levels. There was no statistical difference in the major microflora between shelves of different ages. Moreover, the total counts and the predominant microflora showed a surprising homogeneity between origins of cheeses. For most of the microflora enumerated, no seasonal variation was observed. Regardless of the age of the shelves, the wood had high water activity values (0.94 to 0.97), neutral pH values (7.1 to 8.3), and low salt contents (0.11 to 0.17 mg/cm2). The origins of the cheese had a statistically significant impact on water activity, pH, and salt concentration, whereas the age of the shelves did not influence these parameters. This study demonstrated the stability of the technological biofilm present on wooden shelves and will serve to enlarge the debate on the use of wood in cheese ripening.