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

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-infrared 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 6 experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status were defined by k-means clustering (k= 3) based on the 4 blood components. Milk mid-infrared 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 4 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. While 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 dataset into 3 groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the 3 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-infrared 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.