Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853.
Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853.
J Dairy Sci. 2021 Jul;104(7):7426-7437. doi: 10.3168/jds.2020-18772. Epub 2021 Apr 2.
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).
我们的首要目标是重新设计一个经过改良的 14 个样本牛奶校准样本集，以获得分布均匀的牛奶尿素氮（MUN）浓度范围，同时保持与脂肪、蛋白质和乳糖浓度变化的正交性。我们的第二个目标是确定改良牛奶校准样本中酶分光光度法的内部和实验室间变异以及 MUN 参考值的不确定度，然后使用改良牛奶校准样本评估和改进中红外偏最小二乘（PLS）模型预测牛奶 MUN 浓度的性能。通过改变改良牛奶校准样本的配方和制造工艺来实现所需的 MUN 浓度范围。使用分光光度酶参考方法来确定 MUN 参考值，并使用改良牛奶校准样本校准 3 台中红外牛奶分析仪。MUN 参考值的内部和实验室间变异分别为 0.43%和 0.77%，14 个样本校准集的平均 MUN 值的平均扩展分析不确定度为（平均值±SD）16.15mg/100g±0.09 牛奶。在斜率和截距调整以实现与校准样本的平均差异为零之后，可以看出，3 种不同仪器及其 PLS 模型之间预测与参考 MUN 值的差异的标准偏差差异很大。使用正交样本集（1）确定 PLS 模型何时不能正确模拟出脂肪、真实蛋白质或无水乳糖的背景变化；（2）计算内校正因子以消除该影响；（3）提高模型性能（即，仪器预测与 MUN 参考化学值之间差异的标准偏差降低 50%）。
