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Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach

Published online by Cambridge University Press:  10 July 2018

C. Grelet Open the ORCID record for C. Grelet [Opens in a new window] ,
A. Vanlierde Open the ORCID record for A. Vanlierde [Opens in a new window] ,
M. Hostens Open the ORCID record for M. Hostens [Opens in a new window] ,
L. Foldager Open the ORCID record for L. Foldager [Opens in a new window] ,
M. Salavati Open the ORCID record for M. Salavati [Opens in a new window] ,
K. L. Ingvartsen Open the ORCID record for K. L. Ingvartsen [Opens in a new window] ,
M. Crowe ,
M. T. Sorensen Open the ORCID record for M. T. Sorensen [Opens in a new window] ,
E. Froidmont Open the ORCID record for E. Froidmont [Opens in a new window]  and
C. P. Ferris Open the ORCID record for C. P. Ferris [Opens in a new window]
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C. Grelet
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
A. Vanlierde
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
M. Hostens
Affiliation:
Ghent University, Merelbeke, 9820, Belgium
L. Foldager
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, 8000, Denmark
M. Salavati
Affiliation:
Royal Veterinary College (RVC), London, NW1 0TU, UK
K. L. Ingvartsen
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
M. Crowe
Affiliation:
University College Dublin (UCD), Dublin, Ireland
M. T. Sorensen
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
E. Froidmont
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
C. P. Ferris
Affiliation:
Agri-Food and Biosciences Institute (AFBI), Belfast, BT9 5PX, Northern Ireland
C. Marchitelli
Affiliation:
Research Center for Animal Production and Aquaculture (CREA), Roma, 00198, Italy
F. Becker
Affiliation:
Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, 18196, Germany
T. Larsen
Affiliation:
Department of Animal Science, Aarhus University, Tjele, 8830, Denmark
F. Carter
Affiliation:
University College Dublin (UCD), Dublin, Ireland
F. Dehareng*
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium
GplusE Consortium
Affiliation:
Walloon Agricultural Research Center (CRA-W), Gembloux, 5030, Belgium Ghent University, Merelbeke, 9820, Belgium Department of Animal Science, Aarhus University, Tjele, 8830, Denmark Bioinformatics Research Centre, Aarhus University, Aarhus, 8000, Denmark Royal Veterinary College (RVC), London, NW1 0TU, UK University College Dublin (UCD), Dublin, Ireland Agri-Food and Biosciences Institute (AFBI), Belfast, BT9 5PX, Northern Ireland Research Center for Animal Production and Aquaculture (CREA), Roma, 00198, Italy Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, 18196, Germany
*
†E-mail: [email protected]
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Abstract

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

Keywords

Fourier transform mid-IR spectrometrydairy cattlepredictionbiomarkermetabolic clustering
Type
Research Article
Information
animal , Volume 13 , Issue 3 , March 2019 , pp. 649 - 658
Copyright
© The Animal Consortium 2018 

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Footnotes

a

List of authors within the GplusE consortium: Niamh McLoughlin, Alan Fahey, Elizabeth Matthews, Andreia Santoro, Colin Byrne, Pauline Rudd, Roisin O’Flaherty, Sinead Hallinan, Claire Wathes, Zhangrui Cheng, Ali Fouladi, Geoff Pollott, Dirk Werling, Beatriz Sanz Bernardo, Alistair Wylie, Matt Bell, Mieke Vaneetvelde, Kristof Hermans, Geert Opsomer, Sander Moerman, Jenne Dekoster, Hannes Bogaert, Jan Vandepitte, Leila Vandevelde, Bonny Vanranst, Johanna Hoglund, Susanne Dahl, Soren Ostergaard, Janne Rothmann, Mogens Krogh, Else Meyer, Charlotte Gaillard, Jehan Ettema, Tine Rousing, Federica Signorelli, Francesco Napolitano, Bianca Moioli, Alessandra Crisà, Luca Buttazzoni, Jennifer McClure, Daragh Matthews, Francis Kearney, Andrew Cromie, Matt McClure, Shujun Zhang, Xing Chen, Huanchun Chen, Junlong Zhao, Liguo Yang, Guohua Hua, Chen Tan, Guiqiang Wang, Michel Bonneau, Andrea Pompozzi, Armin Pearn, Arnold Evertson, Linda Kosten, Anders Fogh, Thomas Andersen, Matthew Lucey, Chris Elsik, Gavin Conant, Jerry Taylor, Nicolas Gengler, Michel Georges, Frédéric Colinet, Marilou Ramos Pamplona, Hedi Hammami, Catherine Bastin, Haruko Takeda, Aurelie Laine, Anne-Sophie Van Laere, Martin Schulze, Sergio Palma Vera.

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