Intramammary infection (IMI), comprises a group of costly diseases affecting dairy animals worldwide. Many dairy parlours are equipped with on-line computerised data acquisition systems designed to detect IMI. However, the data collected is related to the cow level, therefore the contribution of infected glands to the recorded parameters may be over estimated. The present study aimed at evaluating the influence of single gland IMI by different bacteria species on the cow's overall milk quality. A total of 130 cows were tested 239 times; 79 cows were tested once and the others were examined 2-8 times. All of the analysed data refer to the number of tests performed, taking into account the repeated testing of the same cows. Of the cows tested ~50% were free of infection in all 4 glands and the others were infected in one gland with different coagulase negative staphylococci (CNS), Streptococcus dysgalactiae, or were post infected with Escherichia coli (PIEc), i.e., free of bacterial infection at the time of sampling but 1-2 months after clinical infection by E. coli. Overall, infection with bacteria had significant effects on somatic cell count (SCC) and lactose concentration. Examining each bacterium reveals that the major influence on those parameters was the sharp decrease in lactose in the PIEc and curd firmness in PIEc and Strep. Individual gland milk production decreased ~20% in Strep. dysgalactiae- and ~50% in PIEc-infected glands with respect to glands with no bacterial findings. Significant differences were found in lactose, SCC, rennet clotting time and curd firmness in the milk of infected glands and among those, these parameters were significantly higher in Strep. dysgalactiae and PIEc than in CNS infected cows. The current results using quarter-milking reinforces the importance of accurate IMI detection in relation to economic and welfare factors, and moreover, emphasises the need for technical sensing and constant reporting to the farmer about changes in the milk quality of every animal.

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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.

The economical profitability of the dairy industry is based on the quality of the bulk milk collected in the farms, therefore it was based on the herd level rather than on the individual animals at real time. Udder infection and stage of lactation are directly related to the quality of milk produced on the herd level. However, improvement of milk quality requires testing each animal's milk separately and continuously. Recently, it was postulated that online equipment can estimate milk quality according to its clotting parameters, and thus result in better economical return for cheese making. This study further investigated the potential application of the AfiLab TM equipment to provide real-time analysis of milk-clotting parameters for cheese manufacture and cheese yield on quarter (1018) and individual cow (277) levels. Days in milk, lactose, log SCC and udder infection were found to have a significant effect on curd firmness and cheese properties and yield. The results clearly indicate that: (a) the parameter Afi-CF determined with the AfiLab TM is suitable for assessing milk quality for its clotting parameters, a value which is not provided by merely measuring fat and protein content on the gland and the cow levels; (b) bacterial type is the single major cause of reduced milk quality, with variations depending on the bacterial species; and (c) early and late lactation also had negative effects on milk-clotting parameters. Cheese made from the various milk samples that were determined by the Afilab TM to be of higher quality for cheese making resulted in higher yield and better texture, which were related mainly to the bacterial species and stage of lactation.

Real-time analysis of milk coagulation properties as performed by the AfiLab TM milk spectrometer introduces new opportunities for the dairy industry. The study evaluated the performance of the AfiLab TM in a milking parlor of a commercial farm to provide real-time analysis of milk-clotting parameters -Afi-CF for cheese manufacture and determine its repeatability in time for individual cows. The AfiLab TM in a parlor, equipped with two parallel milk lines, enables to divert the milk on-line into two bulk milk tanks (A and B). Three commercial dairy herds of 220 to 320 Israeli Holstein cows producing ,11 500 l during 305 days were selected for the study. The Afi-CF repeatability during time was found significant ( P , 0.001) for cows. The statistic model succeeded in explaining 83.5% of the variance between Afi-CF and cows, and no significant variance was found between the mean weekly repeated recordings. Days in milk and log somatic cell count (SCC) had no significant effect. Fat, protein and lactose significantly affected Afi-CF and the empirical van Slyke equation. Real-time simulations were performed for different cutoff levels of coagulation properties where the milk of high Afi-CF cutoff value was channeled to tank A and the lower into tank B. The simulations showed that milk coagulation properties of an individual cow are not uniform, as most cows contributed milk to both tanks. Proportions of the individual cow's milk in each tank depended on the selected Afi-CF cutoff. The assessment of the major causative factors of a cow producing low-quality milk for cheese production was evaluated for the group that produced the low 10% quality milk. The largest number of cows in those groups at the three farms was found to be cows with post-intramammary infection with Escherichia coli and subclinical infections with streptococci or coagulase-negative staphylococci (,30%), although the SCC of these cows was not significantly different. Early time in lactation together with high milk yield .50 l/day, and late in lactation together with low milk yield , 15 l/day and estrous (0 to 5 days) were also important influencing factors for low-quality milk. However, ,50% of the tested variables did not explain any of the factors responsible for the cow producing milk in the low -10% Afi-CF.Keywords: milk clotting parameters, real-time evaluation, cheese yield ImplicationsThe present work demonstrates the potential of the AfiLab TM to assess on-line milk-clotting parameters and to divert the milk into two bulk milk tanks A and B through two parallel milk lines. Simulated segregation of milk according to clotting parameters revealed that the cow's milk was diverted according to varying quality parameters for cheese production on a daily basis, as well as during the milking session. The major factors responsible for low-quality milk for cheese making were post-udder infection by various bacteria and the combination of the cow's status of days in milk and level of milk production.