Aarhus University Seal / Aarhus Universitets segl

Mogens Agerbo Krogh

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • C. Grelet, Wallon Agricultural Research Centre
  • ,
  • A. Vanlierde, Wallon Agricultural Research Centre
  • ,
  • M. Hostens, Universiteit Gent
  • ,
  • L. Foldager
  • M. Salavati, The Royal Veterinary College
  • ,
  • Klaus Lønne Ingvartsen
  • M. Crowe, University College Dublin, Dublin
  • ,
  • Martin Tang Sorensen
  • E. Froidmont, Wallon Agricultural Research Centre
  • ,
  • C. P. Ferris, Agri-Food and Biosciences Institute, Belfast
  • ,
  • C. Marchitelli, Research Center for Animal Production and Aquaculture (CREA)
  • ,
  • F. Becker, Research Institute for the Biology of Farm Animals, Dummerstoft
  • ,
  • T. Larsen
  • F. Carter, University College Dublin, Dublin
  • ,
  • GplusE Consortium, Genotype Plus Environment Consortium (www.gpluse.eu)
  • ,
  • F. Dehareng, Wallon Agricultural Research Centre

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.

Original languageEnglish
Pages (from-to)649-658
Number of pages10
Publication statusPublished - Mar 2019

    Research areas

  • biomarker, dairy cattle, Fourier transform mid-IR spectrometry, metabolic clustering, prediction, BETA-HYDROXYBUTYRATE, NEGATIVE-ENERGY BALANCE, MIDINFRARED SPECTRA, HOLSTEIN, SUBCLINICAL KETOSIS, DISEASES, GENE-EXPRESSION, UTERINE HEALTH, PHYSIOLOGICAL IMBALANCE, DAIRY-CATTLE, Fatty Acids, Nonesterified/blood, Insulin-Like Growth Factor I/metabolism, Spectroscopy, Fourier Transform Infrared/methods, Cattle, Female, Milk, Blood Glucose/metabolism, Animal Husbandry/methods, Animals, Energy Metabolism, Blood Chemical Analysis/veterinary, Cluster Analysis

See relations at Aarhus University Citationformats

ID: 130944911