Effect of subclinical intramammary infection on milk quality in dairy sheep: II. Matured-pressed cheese (Manchego) produced from milk of uninfected and infected glands and from their blends

Sensory Attributes and Instrumental Chemical Parameters of Commercial Spanish Cured Ewes' Milk Cheeses: Insights into Cheese Quality Figures

The external appearance of some of the Protected Designation of Origin (PDO) cured cheeses is similar to other cheese samples made in Spain: 1 kg and 2.5-3 kg formats, cylindrical, and with or without a pleita mark on the surface. In this work, commercial cured ewe's milk cheese samples with a similar external appearance were analyzed, including five PDO and five non-PDO samples. The parameters analyzed were color, texture, pH, humidity, water activity, and the volatile profile. Additionally, a descriptive and consumer-sensory analysis of the cheese samples was carried out. Statistical analysis of the results showed that luminosity, color coordinates a* and b*, percentage of deformation, humidity, water activity, and acid contents were significantly higher in non-PDO cheese samples. The breaking force, maximum force, and the content of esters were significantly higher in those cheese samples with PDO. In addition, PDO cheese samples showed higher scores for all attributes evaluated by consumers, except for color. These results suggest that PDO cheeses are placed on the market with a higher degree of ripening than non-PDO ones and that consequently they are more positively valued by consumers.

Correlation between Milk Bacteriology, Cytology and Mammary Tissue Histology in Cows: Cure from the Pathogen or Recovery from the Inflammation

The aim of the current study was to verify the existence of a significant correlation between bacterial isolation (or not) and mammary gland inflammation, using traditional bacterial culturing and PCR, milk leucocytes distributions, and tissue histology. Twenty-two cows were tested at the level of the individual gland for bacteriological culture and real-time PCR (RT-PCR), milk composition, somatic cells count (SCC), and cell differentiation. Post-slaughter samples of teat-ends and mammary tissues were tested for histology and bacteriology by RT-PCR. The 88 glands were assigned to either outcome: 1. Healthy—no inflammation and no bacterial finding (NBF) (n = 33); 2. Inflammation and NBF (n = 26); 3. Inflammation and intra-mammary infection (n = 22) with different bacteria. Bacteriology of milk samples and that of the RT-PCR showed 91.4% agreement. In the lobule’s tissues of healthy glands, ~50% were milk producers and the other glands had dry areas with increased fat globules with a low number of leukocytes. In contrast, ~75% of the infected glands were identified as inflamed, but with no isolation of bacteria. Infiltration of mononuclear cells and neutrophils into the connective tissue was observed but not in the lobule’s lumen. In summary, the study confirms that not every mastitis/inflammation is also an infection.

Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression

Simple Summary

Considered that all sheep milk in Italy is destined for cheese processing, traits describing rennet coagulation aptitude should be among the most important selection goals for dairy breeds. To reduce the costs and logistics related to the large-scale recording of these traits, mid-infrared (MIR) spectroscopy could be conveniently used to generate reliable predictions without any additional cost. The aims of this research were to predict the milk coagulation properties (MCP) and individual cheese yield (ILCY) in sheep by MIR spectrometry using partial least squares regression (PLS), and to compare different data pre-treatment procedures. The prediction results observed in the present study, although moderate, suggest the possibility of adding novel phenotypes (e.g., MCP and ILCY) in breeding schemes for dairy sheep breeds. Mid-infrared spectroscopy coupled with PLS regression could allow the prediction of phenotypes at the population level without additional costs.

Abstract

The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics.

Real-time evaluation of individual cow milk for higher cheese-milk quality with increased cheese yield

Cheese was produced in a series of experiments from milk separated in real time during milking by using the Afilab MCS milk classification service (Afikim, Israel), which is installed on the milk line in every stall and sorts milk in real time into 2 target tanks: the A tank for cheese production (CM) and the B tank for fluid milk products (FM). The cheese milk was prepared in varying ratios ranging from ~10:90 to ~90:10 CM:FM by using this system. Cheese was made with corrected protein-to-fat ratio and without it, as well as from milk stored at 4°C for 1, 2, 3, 4, and 8d before production. Cheese weight at 24h increased along the separation cutoff level with no difference in moisture, and dry matter increased. The data compiled allowed a theoretical calculation of cheese yield and comparing it to the original van Slyke equation. Whenever the value of Afi-Cf, which is the optical measure of curd firmness obtained by the Afilab instrument, was used, a better predicted level of cheese yield was obtained. In addition, 27 bulk milk tanks with milk separated at a 50:50 CM:FM ratio resulted in cheese with a significantly higher fat and protein, dry matter, and weight at 24h. Moreover, solids incorporated from the milk into the cheese were significantly higher in cheeses made of milk from A tanks. The influence of storage of milk up to 8d before cheese making was tested. Gross milk composition did not change and no differences were found in cheese moisture, but dry matter and protein incorporated in the cheese dropped significantly along the storage time. These findings confirm that milk stored for several days before processing is prone to physico-chemical deterioration processes, which result in loss of milk constituents to the whey and therefore reduced product yield. The study demonstrates that introducing the unknown parameters for calculating the predicted cheese yield, such as the empiric measured Afi-Cf properties, are more accurate and the increase in cheese yield is more than increasing just the protein level, the value that is being tested by the dairies, or even casein.