Literature DB >> 24440251

Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits.

M De Marchi1, V Toffanin2, M Cassandro2, M Penasa2.   

Abstract

Interest in methods that routinely and accurately measure and predict animal characteristics is growing in importance, both for quality characterization of livestock products and for genetic purposes. Mid-infrared spectroscopy (MIRS) is a rapid and cost-effective tool for recording phenotypes at the population level. Mid-infrared spectroscopy is based on crossing matter by electromagnetic radiation and on the subsequent measure of energy absorption, and it is commonly used to determine traditional milk quality traits in official milk laboratories. The aim of this review was to focus on the use of MIRS to predict new milk phenotypes of economic relevance such as fatty acid and protein composition, coagulation properties, acidity, mineral composition, ketone bodies, body energy status, and methane emissions. Analysis of the literature demonstrated the feasibility of MIRS to predict these traits, with different accuracies and with margins of improvement of prediction equations. In general, the reviewed papers underlined the influence of data variability, reference method, and unit of measurement on the development of robust models. A crucial point in favor of the application of MIRS is to stimulate the exchange of data among countries to develop equations that take into account the biological variability of the studied traits under different conditions. Due to the large variability of reference methods used for MIRS calibration, it is essential to standardize the methods used within and across countries.
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  dairy cattle; mid-infrared spectroscopy; phenotyping; quality trait

Mesh:

Year:  2014        PMID: 24440251     DOI: 10.3168/jds.2013-6799

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


  22 in total

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Authors:  Edmond J Breen; Iona M MacLeod; Phuong N Ho; Mekonnen Haile-Mariam; Jennie E Pryce; Carl D Thomas; Hans D Daetwyler; Michael E Goddard
Journal:  Commun Biol       Date:  2022-07-05

2.  Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.

Authors:  A Ferragina; G de los Campos; A I Vazquez; A Cecchinato; G Bittante
Journal:  J Dairy Sci       Date:  2015-09-18       Impact factor: 4.034

3.  Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows.

Authors:  Chao Du; Liangkang Nan; Chunfang Li; Ahmed Sabek; Haitong Wang; Xuelu Luo; Jundong Su; Guohua Hua; Yabing Ma; Shujun Zhang
Journal:  Animals (Basel)       Date:  2021-04-22       Impact factor: 2.752

4.  Causal relationships between milk quality and coagulation properties in Italian Holstein-Friesian dairy cattle.

Authors:  Francesco Tiezzi; Bruno D Valente; Martino Cassandro; Christian Maltecca
Journal:  Genet Sel Evol       Date:  2015-05-13       Impact factor: 4.297

Review 5.  Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits.

Authors:  C Egger-Danner; J B Cole; J E Pryce; N Gengler; B Heringstad; A Bradley; K F Stock
Journal:  Animal       Date:  2014-11-12       Impact factor: 3.240

6.  Application of laboratory and portable attenuated total reflectance infrared spectroscopic approaches for rapid quantification of alpaca serum immunoglobulin G.

Authors:  Ibrahim Elsohaby; Jennifer B Burns; Christopher B Riley; R Anthony Shaw; J Trenton McClure
Journal:  PLoS One       Date:  2017-06-26       Impact factor: 3.240

7.  Genome-wide association analysis for β-hydroxybutyrate concentration in Milk in Holstein dairy cattle.

Authors:  S Nayeri; F Schenkel; A Fleming; V Kroezen; M Sargolzaei; C Baes; A Cánovas; J Squires; F Miglior
Journal:  BMC Genet       Date:  2019-07-16       Impact factor: 2.797

8.  Fatty Acid Prediction in Bovine Milk by Attenuated Total Reflection Infrared Spectroscopy after Solvent-Free Lipid Separation.

Authors:  Christopher Karim Akhgar; Vanessa Nürnberger; Marlene Nadvornik; Margit Velik; Andreas Schwaighofer; Erwin Rosenberg; Bernhard Lendl
Journal:  Foods       Date:  2021-05-11

9.  Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle.

Authors:  Kathryn M Tiplady; Thomas J Lopdell; Edwardo Reynolds; Richard G Sherlock; Michael Keehan; Thomas Jj Johnson; Jennie E Pryce; Stephen R Davis; Richard J Spelman; Bevin L Harris; Dorian J Garrick; Mathew D Littlejohn
Journal:  Genet Sel Evol       Date:  2021-07-20       Impact factor: 4.297

10.  Challenges and opportunities in genetic improvement of local livestock breeds.

Authors:  Filippo Biscarini; Ezequiel L Nicolazzi; Alessandra Stella; Paul J Boettcher; Gustavo Gandini
Journal:  Front Genet       Date:  2015-02-25       Impact factor: 4.599

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