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

(2019) ANIMAL. 13(3). p.649-658
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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 beta-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 R-2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R-2 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
NEGATIVE-ENERGY BALANCE, DAIRY-CATTLE, BETA-HYDROXYBUTYRATE, SUBCLINICAL, KETOSIS, GENE-EXPRESSION, PHYSIOLOGICAL IMBALANCE, MIDINFRARED SPECTRA, UTERINE HEALTH, DISEASES, HOLSTEIN, Fourier transform mid-IR spectrometry, dairy cattle, prediction, biomarker, metabolic clustering

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Chicago
Grelet, C, A Vanlierde, Miel Hostens, L Foldager, M Salavati, KL Ingvartsen, M Crowe, et al. 2019. “Potential of Milk mid-IR Spectra to Predict Metabolic Status of Cows Through Blood Components and an Innovative Clustering Approach.” Animal 13 (3): 649–658.
APA
Grelet, C., Vanlierde, A., Hostens, M., Foldager, L., Salavati, M., Ingvartsen, K., Crowe, M., et al. (2019). Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. ANIMAL, 13(3), 649–658.
Vancouver
1.
Grelet C, Vanlierde A, Hostens M, Foldager L, Salavati M, Ingvartsen K, et al. Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. ANIMAL. 2019;13(3):649–58.
MLA
Grelet, C et al. “Potential of Milk mid-IR Spectra to Predict Metabolic Status of Cows Through Blood Components and an Innovative Clustering Approach.” ANIMAL 13.3 (2019): 649–658. Print.
@article{8606356,
  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 beta-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 R-2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R-2 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.},
  author       = {Grelet, C and Vanlierde, A and Hostens, Miel and Foldager, L and Salavati, M and Ingvartsen, KL and Crowe, M and Sorensen, MT and Froidmont, E and Ferris, CP and Marchitelli, C and Becker, F and Larsen, T and Carter, F and Dehareng, F and McLoughlin, Niamh and Fahey, Alan and Matthews, Elizabeth and Santoro, Andreia and Byrne, Colin and Rudd, Pauline and O'Flaherty, Roisin and Hallinan, Sinead and Wathes, Claire and Cheng, Zhangrui and Fouladi, Ali and Pollott, Geoff and Werling, Dirk and Bernardo, Beatriz Sanz and Wylie, Alistair and Bell, Matt and Van Eetvelde, Mieke and Hermans, Kristof and Opsomer, Geert and Moerman, Sander and De Koster, Jenne and Bogaert, Hannes and Vandepitte, Jan and Vande Velde, Leila and Van Ranst, Bonifacius and Hoglund, Johanna and Dahl, Susanne and Ostergaard, Soren and Rothmann, Janne and Krogh, Mogens and Meyer, Else and Gaillard, Charlotte and Ettema, Jehan and Rousing, Tine and Signorelli, Federica and Napolitano, Francesco and Moioli, Bianca and Crisa, Alessandra and Buttazzoni, Luca and McClure, Jennifer and Matthews, Daragh and Kearney, Francis and Cromie, Andrew and McClure, Matt and Zhang, Shujun and Chen, Xing and Chen, Huanchun and Zhao, Junlong and Yang, Liguo and Hua, Guohua and Tan, Chen and Wang, Guiqiang and Bonneau, Michel and Pompozzi, Andrea and Pearn, Armin and Evertson, Arnold and Kosten, Linda and Fogh, Anders and Andersen, Thomas and Lucey, Matthew and Elsik, Chris and Conant, Gavin and Taylor, Jerry and Gengler, Nicolas and Georges, Michel and Colinet, Frederic and Pamplona, Marilou Ramos and Hammami, Hedi and Bastin, Catherine and Takeda, Haruko and Laine, Aurelie and Van Laere, Anne-Sophie and Schulze, Martin and Vera, Sergio Palma},
  issn         = {1751-7311},
  journal      = {ANIMAL},
  language     = {eng},
  number       = {3},
  pages        = {649--658},
  title        = {Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach},
  url          = {http://dx.doi.org/10.1017/S1751731118001751},
  volume       = {13},
  year         = {2019},
}

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