Near-infrared spectroscopic sensor system for milk composition analysis: an on-farm real-time application

In previous studies, long-wave near-infrared (LW-NIR, 960 to 1,690nm wavelength range) spectroscopy accurately characterized the main components of raw milk (fat, protein and lactose). These components contain information on the udder and metabolic health of dairy cows, as milk production has a critical role in their metabolism. In current practices, milk composition is monitored post-hoc with a low frequency, or on-farm with a poor prediction performance. In this work, we present and evaluate an accurate analyzer for the on-farm, real-time monitoring of milk composition. For every milking performed by an automatic milking system (AMS), the analyzer extracts a milk sample automatically. After stabilization, the sample is introduced into a flow-through cuvette, and reflectance and transmittance LW-NIR spectra of the sample are acquired. Reflectance and transmittance reference and dark reference spectra are also measured after the sample. During a continuous trial of 34 weeks, the analyzer measured 1,926 reflectance and transmittance spectra from raw milk samples. For these, laboratory reference values were obtained for fat, protein and lactose. Prediction models for fat, protein and lactose were trained exclusively with samples acquired during the first six weeks of the trial (n=600). The prediction models were evaluated with subsequent samples (n=1,326). These models had an error (root-mean-square error of prediction, RMSEP) lower than 0.16% (% in weight/weight) for fat (range 2.01-7.95%), 0.18% for protein (2.55-4.48%) and 0.12% for lactose (4.03-5.18%). The presented analyzer can be used for accurate autonomous milk composition monitoring, with a prediction performance within ICAR requirements for at-line milk analyzers (RMSEP < 0.2%). However, drift was observed in the predictions over time. Therefore, further research and development of calibration maintenance techniques is required to correct model drift and further increase the accuracy.

ISBN:978-3-18092406-9

Publication year:2022

Accessibility:Open