Mid-Infrared Spectroscopic Analysis of Raw Milk to Predict the Blood Plasma Non-Esterified Fatty Acid Concentration in Dairy Cows

• Published in: bioRxiv
• Main Authors: Aernouts, Ben, Adriaens, Ines, José Diaz Olivares, Saeys, Wouter, Mäntysaari, Päivi, Kokkonen, Tuomo, Mehtiö, Terhi, Kajava, Sari, Lidauer, Paula, Lidauer, Martin, Pastell, Matti
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 25.11.2019
• Subjects: Adipose tissue; Antiretroviral drugs; Blood levels; Cow's milk; Energy balance; Fatty acids; Metabolism; Milk; Phenotyping; Plasma; Postpartum; Regression analysis
• DOI: 10.1101/853127

In high yielding dairy cattle, severe postpartum negative energy status is often associated with metabolic and infectious disorders that negatively affect production, fertility and welfare. Mobilization of adipose tissue associated with a negative energy status is reflected through an increased level of non-esterified fatty acids (NEFA) in the blood plasma. Earlier, identification of a negative energy status through the detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently there have been attempts to predict blood NEFA concentration from milk samples. This study aimed to develop and validate a model to predict the blood plasma NEFA concentration using milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in weeks 2, 3 and 20 post-partum for 192 lactations in 3 different herds. The blood plasma samples were taken in the morning, while representative milk samples were collected during the morning and evening milk session on the same day. To predict the blood plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the two independent herds to explore their robustness and wide applicability. The final model can accurately predict blood plasma NEFA concentrations below 0.6 mmol/L with a root mean square error of prediction (RMSE) of less than 0.143 mmol/L. However, for blood plasma with more than 1.2 mmol/L NEFA, the model clearly underestimates the true level. Additionally, it was found that morning blood plasma NEFA levels were predicted with a significantly higher accuracy (p = 0.009) using MIR spectra of evening milk samples compared to morning samples, with RMSEP values of respectively 0.182 and 0.197 mmol/L and R-squared values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for a negative energy status, indicated with detrimental morning blood plasma NEFA levels (> 0.6 mmol/L), could be identified with a sensitivity and specificity of respectively 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens opportunities for regular metabolic screening and improved resilience phenotyping.