JDS communications ·18 Sept 2026 ·Added 12 Nov 2026

Toward routine health phenotyping: High-throughput prediction of metabolic, immune, and inflammatory biomarkers from milk mid-infrared spectroscopy in early-lactation dairy cows

P.N. Ho, J.E. Hemsworth, C.M. Reich, C.R. Bath, Z. Liu, S. Rochfort, M. Khansefid, M.S. Tahir, M. Haile-Mariam, M.E. Goddard, L.C. Marett, S.R.O. Williams, C.K.M. Ho, M.J. Berkhout, R. Xiang, A.J. Chamberlain

DOI ↗

Abstract

This study evaluated the potential of milk mid-infrared (MIR) spectroscopy, combined with routinely available on-farm variables, for predicting serum metabolic, immune, and inflammatory biomarkers in early-lactation cows. Data included 5,936 blood samples from 4,442 cows across 23 Australian dairy herds, with paired milk MIR spectra and serum measurements for up to 14 biomarkers. Prediction models were developed using partial least squares regression and evaluated using nested 10-fold random cross-validation and leave-one-herd-out validation. The results show that although basic herd-test data, including milk fat, protein, and lactose concentration, as well as on-farm variables, including DIM, calving age, breed, and herd, could predict serum biomarkers, combining MIR spectra with these on-farm variables produced the best overall performance. In random cross-validation, BUN was predicted most accurately (R2 = 0.79), and BHB and nonesterified fatty acids (NEFA) showed moderate accuracy (R2 = 0.57 and 0.45, respectively). Blood urea nitrogen also showed the strongest external validation performance, with leave-one-herd-out R2 = 0.58 and comparable accuracy for predicting records collected after 70 DIM (R2 = 0.65). The BHB and NEFA had moderate leave-one-herd-out accuracy but did not transfer beyond early lactation. Most other biomarkers showed low or inconsistent external validation performance. Overall, MIR spectroscopy combined with on-farm variables shows promise for routine prediction of BUN, BHB, and NEFA, which can be used for monitoring and genetic evaluation of, for example, ketosis and energy deficit. Initial random cross-validation results for glucose, bilirubin, and cholesterol were promising, but more data are needed to improve the prediction accuracy and robustness of the predictions.