Infrared milk analyzers: Milk urea nitrogen calibration

Our first objective was to redesign a modified 14-sample milk calibration sample set to obtain a well distributed range of milk urea nitrogen (MUN) concentrations while maintaining orthogonality with variation in fat, protein, and lactose concentration. Our second objective was to determine the within-and between laboratory variation in the enzymatic spectrophotometric method on the modified milk calibration samples and degree of uncertainty in MUN reference values, and then use the modified milk calibration samples to evaluate and improve the performance of mid-infrared partial least squares (PLS) models for prediction of MUN concentration in milk. Changes in the modified milk calibration sample formulation and manufacturing procedure were made to achieve the desired range of MUN concentrations. A spectrophotometric enzymatic reference method was used to determine MUN reference values, and the modified milk calibration samples were used to calibrate 3 mid-infrared milk analyzers. The within-and between-laboratory variation in the reference values for MUN were 0.43 and 0.77%, respectively, and the average expanded analytical uncertainty for the mean MUN value of the 14-sample calibration set was (mean +/- SD) 16.15 mg/100 g +/- 0.09 of milk. After slope and intercept adjustment to achieve a mean difference of zero with the calibration samples, it could be seen that the standard deviation of the differences of predicted versus reference MUN values among 3 different instruments and their PLS models were quite different. The orthogonal sample set was used (1) to determine when a PLS model did not correctly model out the background variation in fat, true protein, or anhydrous lactose; (2) to calculate an intercorrection factor to eliminate that effect, and (3) to improve the model performance (i.e., 50% reduction in standard deviation of the difference between instrument predictions and reference chemistry values for MUN).

Automated summary

The study aimed to redesign milk calibration samples, determine within-and between laboratory variation, and optimize PLS models for predicting MUN concentration.