Prevention of biofilm formation in milking equipment is important to ensure good hygiene quality of raw milk. Key factors to achieving good results are a successful cleaning procedure and a method to check the cleanliness of milking equipment surfaces. Adenosine triphosphate bioluminescence is a fast and easy method for investigating bacterial contamination of surfaces. However, previous studies on the potential of ATP bioluminescence to assess the hygiene status of milking equipment have been hampered by lack of a validated test procedure. The aim of this work was therefore to establish a test procedure for assessing the cleanliness of milking equipment using ATP bioluminescence, and apply the method on-farm to study the hygiene status of aging rubber material in milking equipment. In developing the test procedure, the effects of sampling location in tubes and liners, sampling of dry versus wet barrels, milking point in the parlor, and acid or alkali detergent on ATP values were investigated. The results showed that, to obtain reproducible results, replicate sampling from the same milking points in the parlor is important. For milk tubes, samples should preferably be taken from the milk meter side, for liners on the inside of the barrel. For best results, sampling should be performed after use of alkali detergent. No beneficial effect was observed of sampling dry liner barrels, so sampling in the standardized test procedure is performed directly after cleaning. The standardized test procedure was used on 3 different commercial farms and sampling was initiated after replacement of old rubber parts. On one of the farms, additional sampling was performed to evaluate total bacteria count and determine the association with ATP level. The results suggest that, provided an efficient cleaning procedure is used, the hygiene quality of milking equipment can be maintained during the recommended lifetime of the rubberware. However, due to occasional variation in cleaning efficiency between milking points and liner barrels, random sampling on single occasions can lead to incorrect conclusions. Replicate sampling over time is therefore important for correct interpretation of ATP bioluminescence data. If ATP levels are very high, complementary sampling for total bacteria count should be used to verify that the level is due to bacterial contamination, and not other organic ATP-contributing material (e.g., milk residues).

The composition of raw milk is important for the stability of dairy products with a long shelf-life. Based on known historical changes in raw milk composition, the aim of this study was to get a better understanding of how possible future variations in milk composition may affect the stability of dairy products. The effects of elevated calcium, citrate, and urea levels on the stability of ultra-high temperature (UHT) treated milk stored for 52 weeks at 4, 20, 30, and 37 °C were investigated by a two-level full factorial designed study with fat separation, fat adhesion, sedimentation, color, pH, ethanol stability, and heat coagulation time as response variables. The results showed that elevated level of calcium lowered the pH, resulting in sedimentation and significantly decreased stability. Elevated level of citrate was associated with color, but the stability was not improved compared to the reference UHT milk. Elevated levels of urea or interaction terms had little effect on the stability of UHT milk. Storage conditions significantly affected the stability. In conclusion, to continue produce dairy products with high stability, the dairy industry should make sure the calcium content of raw milk is not too high and that storage of the final product is appropriate.

The maturation of a traditional Swedish long-ripened cheese has shown increasing variation in recent years and the ripening time is now generally longer than in the past. While the cheese is reliant on non-starter lactic acid bacteria for the development of its characteristic flavour, we hypothesised that the observed changes could be due to variations in the microbiota composition and number of bacteria in the raw milk used for production of the cheese. To evaluate associations between microbiota in the raw milk and the resulting cheese, three clusters of commercial farms were created to increase variation in the microbiota of dairy silo milk used for cheese production. Cheese production was performed in three periods over one year. Within each period, milk from the three farm clusters was collected separately and transported to the cheese production facility. Following pasteurisation, the milk was processed into the granular-eyed cheese and matured at a dedicated cheese-ripening facility. For each cheese batch, farm bulk and dairy silo milk samples, a starter culture, early process samples and cheese samples from different stages of maturation (7–20 months) were collected and their microbiota characterised using 16S rRNA amplicon sequencing. The microbiota in the farm bulk milk differed significantly between periods and clusters. Differences in microbiota in dairy silo milk were observed between periods, but not between farm clusters, while the cheese microbiota differed between periods and clusters. The top 13 amplicon sequence variants were dominant in early process samples and the resulting cheese, making up at least 93.3% of the relative abundance (RA). Lactococcus was the dominant genus in the early process samples and, together with Leuconostoc, also dominated in the cheese samples. Contradicting expectations, the RA of the aroma-producing genus Lactobacillus was low in cheese during ripening and there was an unexpected dominance of starter lactic acid bacteria even at the later stages of cheese ripening. To identify factors behind the recent variations in ripening time of this cheese, future studies should address the effects of process variables and the dairy environment.

The monthly variation in raw dairy silo milk was investigated and related to the ripening time of the resulting cheese during an industrial cheese-making trial. Milk composition varied with month, fat and protein content being lowest in August (4.19 and 3.44 g/100 g, respectively). Casein micelle size was largest (192–200 nm) in December–February and smallest (80 nm) in August. In addition, SCC, total bacteria count, proteolytic activities, gel strength, and milk fatty acid composition were significantly varied with month. Overall sensory and texture scores of resulting cheese were mainly influenced by plasmin and plasminogen activity, indicating the importance of native proteolytic systems. Recently, concepts based on the differentiated use of milk in dairy products have been suggested. For the investigated cheese type, there might be little to gain from such an approach. The variation in the investigated quality characteristics of the dairy milk used for cheese making had little effect on cheese ripening in our study. In contrast to our hypothesis, we conclude that as long as the quality of the milk meets certain minimum criteria, there are only weak associations between cheese milk characteristics and the time required for the development of aroma and texture in the cheese. To find answers behind the observed variation in cheese ripening time, studies on the effects of process parameters are needed.