Literature DB >> 20105515

Invited review: technical solutions for analysis of milk constituents and abnormal milk.

M Brandt1, A Haeussermann, E Hartung.   

Abstract

Information about constituents of milk and visual alterations can be used for management support in improving mastitis detection, monitoring fertility and reproduction, and adapting individual diets. Numerous sensors that gather this information are either currently available or in development. Nevertheless, there is still a need to adapt these sensors to special requirements of on-farm utilization such as robustness, calibration and maintenance, costs, operating cycle duration, and high sensitivity and specificity. This paper provides an overview of available sensors, ongoing research, and areas of application for analysis of milk constituents. Currently, the recognition of abnormal milk and the control of udder health is achieved mainly by recording electrical conductivity and changes in milk color. Further indicators of inflammation were recently investigated either to satisfy the high specificity necessary for automatic separation of milk or to create reliable alarm lists. Likewise, milk composition, especially fat:protein ratio, milk urea nitrogen content, and concentration of ketone bodies, provides suitable information about energy and protein supply, roughage fraction in the diet, and metabolic imbalances in dairy cows. In this regard, future prospects are to use frequent on-farm measurements of milk constituents for short-term automatic nutritional management. Finally, measuring progesterone concentration in milk helps farmers detect ovulation, pregnancy, and infertility. Monitoring systems for on-farm or on-line analysis of milk composition are mostly based on infrared spectroscopy, optical methods, biosensors, or sensor arrays. Their calibration and maintenance requirements have to be checked thoroughly before they can be regularly implemented on dairy farms. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20105515     DOI: 10.3168/jds.2009-2565

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  8 in total

1.  Productional data of primiparous dairy cows reared in different social environments during the first 8 weeks after birth.

Authors:  Barbora Valníčková; Radka Šárová; Ilona Stěhulová
Journal:  Data Brief       Date:  2022-05-16

Review 2.  Sensors and clinical mastitis--the quest for the perfect alert.

Authors:  Henk Hogeveen; Claudia Kamphuis; Wilma Steeneveld; Herman Mollenhorst
Journal:  Sensors (Basel)       Date:  2010-08-27       Impact factor: 3.576

3.  Calibration of an automated California mastitis test with focus on the device-dependent variation.

Authors:  Anne-Christin Neitzel; Eckhard Stamer; Wolfgang Junge; Georg Thaller
Journal:  Springerplus       Date:  2014-12-22

4.  Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films.

Authors:  Coral Salvo-Comino; Celia García-Hernández; Cristina García-Cabezón; Maria Luz Rodríguez-Méndez
Journal:  Sensors (Basel)       Date:  2018-08-18       Impact factor: 3.576

5.  The Effect of Lipopolysaccharide-Induced Experimental Bovine Mastitis on Clinical Parameters, Inflammatory Markers, and the Metabolome: A Kinetic Approach.

Authors:  Carl-Fredrik Johnzon; Josef Dahlberg; Ann-Marie Gustafson; Ida Waern; Ali A Moazzami; Karin Östensson; Gunnar Pejler
Journal:  Front Immunol       Date:  2018-06-25       Impact factor: 7.561

Review 6.  Escherichia coli Mastitis in Dairy Cattle: Etiology, Diagnosis, and Treatment Challenges.

Authors:  Débora Brito Goulart; Melha Mellata
Journal:  Front Microbiol       Date:  2022-07-07       Impact factor: 6.064

7.  Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds.

Authors:  Mathias Bausewein; Rolf Mansfeld; Marcus G Doherr; Jan Harms; Ulrike S Sorge
Journal:  Animals (Basel)       Date:  2022-08-19       Impact factor: 3.231

8.  Unraveling the Relationship between Milk Yield and Quality at the Test Day with Rumination Time Recorded by a PLF Technology.

Authors:  Rosanna Marino; Francesca Petrera; Marisanna Speroni; Teresa Rutigliano; Andrea Galli; Fabio Abeni
Journal:  Animals (Basel)       Date:  2021-05-28       Impact factor: 2.752

  8 in total

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