Automatic Milking Systems in the Production of Parmigiano Reggiano Cheese: Effects on the Milk Quality and on Cheese Characteristics

The adoption of innovative processes, such as the automatic milking system (AMS), in the production of typical PDO cheeses, such as Parmigiano Reggiano, needs to be evaluated and tested, to verify its influence on milk quality and the typicality of the product. The present research was aimed to study the effect of the introduction of the AMS on the cheesemaking characteristics of the milk and the ripening process and the sensory properties of the Parmigiano Reggiano PDO cheese. Six cheesemaking trials were performed and, in each trial, two different separated pools of milk were submitted to the cheesemaking process in parallel, one from AMS and the other from a traditional milking parlor (TMP). AMS milk, in comparison with TMP one, showed higher content of lactose and calcium and lower contents of somatic cells, thermophilic lactic acid bacteria and chloride. Nevertheless, these changes were too small to influence the efficiency of the cheesemaking process and the cheese composition and its sensory profile. Moreover, cheeses made with AMS milk fully complied with the sensory characteristics of Parmigiano Reggiano cheese PDO, as defined by the PDO rules

Influence of feeding flavouring-appetizing substances on activity of cows in an automatic milking system

Aim of this research was to study the effect of somatic cell count on mineral content and salt equilibria at the level of quarter milk samples. Ten Italian Friesian cows, in which two homologous quarters (front quarters in 1 cow, rear quarters in 6 cows and both rear and front quarters in 3 cows) were characterised by a milk SCC400,000 cells/mL (HC-milk), respectively, were selected. Cows were milked at quarter level during the morning milking and a single sample was collected from each selected quarter, thus, 26 quarter milk samples were collected. Compared to LC-milk, HC-milk was characterised by a lower content of phosphorus and potassium and by a higher content of both sodium and chloride. The equilibrium of calcium, phosphorus and magnesium between the colloidal and soluble phase of milk and the mineralisation degree of the casein micelles, were not different between HC and LC milk

Fusing Climate Data Products using a Spatially Varying Autoencoder

Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The proposed autoencoder utilizes a Bayesian statistical framework, allowing for probabilistic interpretations while also varying spatially to capture useful spatial patterns across the various data products. Constraints are placed on the autoencoder as it learns patterns in the data, creating an interpretable consensus that includes the important features from each input. We demonstrate the utility of the autoencoder by combining information from multiple precipitation products in High Mountain Asia.Comment: 13 pages, 7 figure

Developing Lockdown Policies

Hardly a week passes without reading or hearing about a school being locked down because of concerns for the safety of students, teachers, and staff. The increasing number of events that prompt lockdowns presents a sad commentary about today’s world. Aware of the very real possibility of threats of violence on campus, district leaders must ensure that they have current policies that cover the safety and risk management issues associated with imposed lockdowns