Advances in dairy Research

10.35248/adr ◽  
2019 ◽  
Keyword(s):  
Dairy Research

Susceptibility of plasmin and chymosin in Cheddar cheese to inactivation by high pressure

2004 ◽  
Vol 71 (4) ◽  
pp. 496-499 ◽  
Author(s):  
Thom Huppertz ◽  
Patrick F Fox ◽  
Alan L Kelly
Keyword(s):  
High Pressure ◽  
Functional Properties ◽  
Cheddar Cheese ◽  
Mozzarella Cheese ◽  
Organoleptic Properties ◽  
Gouda Cheese ◽  
Dairy Research

High pressure (HP) processing has attracted considerable interest in dairy research in recent years; its effects on cheese have been reviewed by O'Reilly et al. (2001). HP treatment of cheese may influence several of its properties, e.g., for hard and semi-hard cheeses, such as Cheddar and Gouda, the most notable effects are on the texture and rate of ripening. HP treatment increased elasticity and improved organoleptic properties of Gouda cheese (Kolakowski et al. 1998). Messens et al. (2000) reported that, immediately after treatment at 400 MPa, Gouda cheese was less rigid and solid-like and more viscoelastic than untreated cheese. HP treatment also enhances the functional properties of Mozzarella cheese (Johnston & Darcy, 2000; O'Reilly et al. 2002).

scholarly journals Effect of teat skin disinfection on the rate of infection and interval to infection in cows exposed to high levels of Staphylococcus aureus: Erratum

1981 ◽  
Vol 48 (3) ◽  
pp. i-i
Keyword(s):  
Staphylococcus Aureus ◽  
Skin Disinfection ◽  
Dairy Research

Effect of teat skin disinfection on the rate of infection and interval to infection in cows exposed to high levels of Staphylococcus aureusRichard F. Sheldrake and Roderic J. T. HoareJournal of Dairy Research (1981), 48, 1–5

scholarly journals Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1690 ◽  
Author(s):  
Marianne Cockburn
Keyword(s):  
Machine Learning ◽  
Dairy Farm ◽  
Poor Performance ◽  
Dairy Farming ◽  
Training Data ◽  
Data Sources ◽  
Full Potential ◽  
Promising Tool ◽  
Public Data ◽  
Dairy Research

Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

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