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

Grelet, C and Vanlierde, A and Hostens, M and Foldager, L and Salavati, M and Ingvartsen, K L and Crowe, M and Sorensen, M T and Froidmont, E and Ferris, C P and Marchitelli, C and Becker, F and Larsen, T and Carter, F and Dehareng, F and McLoughlin, N and Fahey, A and Matthews, E and Santoro, A and Byrne, C and Rudd, P and O'Flaherty, R and Hallinan, S and Wathes, D C and Cheng, Z R and Fouladi-Nashta, A A and Pollott, G E and Werling, D and Bernardo, B S and Wylie, A and Bell, M and Vaneetvelde, M and Hermans, K and Opsomer, G and Moerman, S and Dekoster, J and Bogaert, H and Vandepitte, J and Vandevelde, L and Vanranst, B and Hoglund, J and Dahl, S and Ostergaard, S and Rothmann, J and Krogh, M and Meyer, E and Gaillard, C and Ettema, J and Rousing, T and Signorelli, F and Napolitano, F and Moioli, B and Crisa, A and Buttazzoni, L and McClure, J and Matthews, D and Kearney, F and Cromie, A and McClure, M and Zhang, S J and Chen, X and Chen, H C and Zhao, J L and Yang, L G and Hua, G H and Tan, C and Wang, G Q and Bonneau, M and Pompozzi, A and Pearn, A and Evertson, A and Kosten, L and Fogh, A and Andersen, T and Lucey, M and Elsik, C and Conant, G and Taylor, J and Gengler, N and Georges, M and Colinet, F and Pamplona, M R and Hammami, H and Bastin, C and Takeda, H and Laine, A and Van Laere, A S and Schulze, M and Vera, S P and GplusE Consortium (2019) Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. Animal, 13 (3). pp. 649-658.

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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 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.

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