Literature DB >> 17369216

Screening for subclinical ketosis in dairy cattle by Fourier transform infrared spectrometry.

A P W de Roos1, H J C M van den Bijgaart, J Hørlyk, G de Jong.   

Abstract

Subclinical ketosis is a metabolic disorder in high-producing dairy cattle that can be detected by ketone bodies in milk: acetone (Ac), acetoacetate (AcAc), and beta-hydroxybutyrate (BHBA). Fourier transform infrared (FTIR) spectrometry is to a growing extent used for determination of milk constituents in milk recording, but as yet there is no calibration for ketone bodies available. The objective of this study was therefore to build a calibration for the MilkoScan FT6000 (FOSS Analytical A/S, Hillerød, Denmark) for Ac, AcAc, and BHBA and to evaluate the FTIR predictions for detection of subclinical ketosis. From 217 herds, 1,080 milk samples were taken from fresh multiparous dairy cows. The Ac, AcAc, and BHBA concentrations were determined by chemical methods using segmented flow analysis. Because of its low concentration, AcAc seemed to be hardly detectable and was therefore not considered further. The correlation between the chemical method results of Ac and BHBA was 0.82, indicating that both ketone bodies were elevated in milk during subclinical ketosis. In wk 1 postpartum, however, most samples with a high Ac concentration did not have a high BHBA concentration, whereas after wk 5 postpartum most samples with a high BHBA concentration did not have a high Ac concentration. For Ac and BHBA, the correlation coefficients between the FTIR predictions and the chemical results were around 0.80 with standard error of cross validation values of 0.184 and 0.064 mM for Ac and BHBA, respectively. Using thresholds of 0.15 mM for Ac and 0.10 mM for BHBA, high values for Ac or BHBA were detected with a sensitivity of 69 to 70%, a specificity of 95%, with 25 to 27% false positives and 6 to 7% false negatives. It is argued that FTIR predictions for Ac and BHBA are valuable for screening cows on subclinical ketosis, especially when used in combination with other indicators, and can serve in the evaluation of the herd health status with respect to subclinical ketosis.

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Year:  2007        PMID: 17369216     DOI: 10.3168/jds.2006-203

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


  8 in total

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Journal:  Vet Res Commun       Date:  2016-01-04       Impact factor: 2.459

2.  Protein deprivation attenuates Hsp expression in fat tissue.

Authors:  Harel Eitam; Rotem Agmon; Aviv Asher; Arieh Brosh; Alla Orlov; Ido Izhaki; Ariel Shabtay
Journal:  Cell Stress Chaperones       Date:  2011-11-12       Impact factor: 3.667

3.  Subclinical Ketosis in Dairy Herds: Impact of Early Diagnosis and Treatment.

Authors:  Giuseppe Cascone; Francesca Licitra; Alessandro Stamilla; Simona Amore; Mario Dipasquale; Rosario Salonia; Francesco Antoci; Alfonso Zecconi
Journal:  Front Vet Sci       Date:  2022-06-27

4.  Caloric stress alters fat characteristics and Hsp70 expression in milk somatic cells of lactating beef cows.

Authors:  Harel Eitam; Arieh Brosh; Alla Orlov; Ido Izhaki; Ariel Shabtay
Journal:  Cell Stress Chaperones       Date:  2008-08-13       Impact factor: 3.667

5.  Hyperketonemia Predictions Provide an On-Farm Management Tool with Epidemiological Insights.

Authors:  Ryan S Pralle; Joel D Amdall; Robert H Fourdraine; Garrett R Oetzel; Heather M White
Journal:  Animals (Basel)       Date:  2021-04-30       Impact factor: 2.752

6.  Genome-wide association study on Fourier transform infrared milk spectra for two Danish dairy cattle breeds.

Authors:  R M Zaalberg; L Janss; A J Buitenhuis
Journal:  BMC Genet       Date:  2020-01-31       Impact factor: 2.797

7.  Study on the Discrimination of Possible Error Sources That Might Affect the Quality of Volatile Organic Compounds Signature in Dairy Cattle Using an Electronic Nose.

Authors:  Asmaa S Ali; Joana G P Jacinto; Wolf Mϋnchemyer; Andreas Walte; Björn Kuhla; Arcangelo Gentile; Mohamed S Abdu; Mervat M Kamel; Abdelrauf Morsy Ghallab
Journal:  Vet Sci       Date:  2022-08-29

Review 8.  Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems.

Authors:  Tiago Bresolin; João R R Dórea
Journal:  Front Genet       Date:  2020-08-20       Impact factor: 4.599

  8 in total

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