Determination of blood metabolites in early lactation dairy cows using milk mid-infrared spectra

During early lactation, dairy cows normally experience an unbalanced energy status that can lead to the occurrence of several metabolic disorders. Blood metabolic profile is a valid tool to monitor and identify metabolic diseases, but blood sampling and analysis is a time-consuming and expensive procedure, being also stressful for the animals. Mid-infrared (MIR) spectroscopy is routinely implemented for milk composition analysis of cow milk, being a cost-effective and non-destructive method. Thus, the aim of this study was to investigate the feasibility of predicting blood metabolites from milk MIR spectra. To achieve this goal, 20 herds rearing Holstein-Friesian, Brown Swiss or Simmental dairy cows, located in Trentino Alto Adige and Veneto regions, have been visited between December 2017 and June 2018. At each visit, blood and milk samples were collected within one hour from all lactating cows within 35 days in milk. Blood samples were analysed through reference procedures and milk MIR spectra were collected during milk analyses. Backward interval partial least squares (BiPLS) algorithm was applied to build prediction models for considered metabolic traits. Results showed that BiPLS improved the predictive ability of the models for the studied traits compared with traditional PLS analysis. Blood β-hydroxybutyrate, urea, non-esterified fatty acids and cholesterol were the most predictable traits, with coefficients of determination in external validation of 0.71, 0.64, 0.55 and 0.45, respectively. On the other hand, prediction models for other analysed metabolites were not enough accurate for routine analysis or population studies. Results of the present study suggest the potential of milk MIR spectra to predict important blood metabolites, leading to the possibility to easily access to metabolic status information of early lactation cows.