Abstract On-farm monitoring of milk composition can support close control of the udder and metabolic health of individual dairy cows. In previous studies, near-infrared (NIR) spectroscopy applied to milk analysis has proven useful for predicting the main components of raw milk (fat, protein, and lactose). In this contribution, we present and evaluate a precise tool for online milk composition analysis on the farm. For each milking, the online analyzer automatically collects and analyses a representative milk sample. The system acquires the NIR transmission spectra of the milk samples in the wavelength range from 960 to 1690 nm and performs a milk composition prediction afterward. Over a testing period of 8 weeks, the sensor collected 1165 NIR transmittance spectra of raw milk samples originating from 36 cows for which reference values were obtained for fat, protein, and lactose. For the same online sensor system, two calibration scenarios were evaluated: training post-hoc prediction models based on a representative set of calibration samples (n = 319) acquired over the entire testing period, with different cows in the calibration and test set, and training real-time prediction models exclusively on the samples acquired in the first week of the testing period (n = 308). The obtained prediction models were thoroughly tested on all the remaining samples not included in the calibration sets (n respectively 846 and 857). For the post-hoc prediction models, this resulted in an overall prediction error (root-mean-squared error of prediction, RMSEP) smaller than 0.080% (all % are in wt/wt) for milk fat (range 1.5–6.3%), protein (2.6–4.3%) and lactose (4–5.1%), with a coefficient of determination R2 of 0.989, 0.947 and 0.689 for fat, protein, and lactose respectively. For the real-time prediction models, the RMSEP was smaller than 0.092% for milk fat and lactose, and 0.110% for protein, with an R2 of 0.989 (fat), 0.894 (protein) and 0.644 (lactose). The milk lactose predictions could be further improved (RMSEP = 0.088%, R2 = 0.675) by taking into account a cow-specific bias. The presented online sensor system using the real-time prediction approach can thus be used for detailed and autonomous on-farm monitoring of milk composition after each individual milking, as its accuracy is well within the requirements by the International Committee for Animal Recording (ICAR) for on-farm milk analyzers and even meet the standards for laboratory analysis systems for fat and lactose. For this real-time prediction approach, a drift was observed in the predictions, especially for protein. Therefore, further research on the development of online calibration maintenance techniques is required to correct for this model drift and further improve the performance of this sensor system.

中文翻译：

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使用农场近红外传感器进行在线牛奶成分分析

摘要 牛奶成分的农场监测可以支持对奶牛个体的乳房和代谢健康的密切控制。在之前的研究中，应用于牛奶分析的近红外 (NIR) 光谱已被证明可用于预测原料奶的主要成分（脂肪、蛋白质和乳糖）。在这篇文章中，我们展示并评估了一种用于农场在线牛奶成分分析的精确工具。对于每次挤奶，在线分析仪会自动收集和分析具有代表性的牛奶样品。该系统在 960 到 1690 nm 的波长范围内获取牛奶样品的 NIR 透射光谱，然后进行牛奶成分预测。在为期 8 周的测试期间，该传感器收集了来自 36 头奶牛的原料奶样品的 1165 个 NIR 透射光谱，获得了脂肪、蛋白质和乳糖的参考值。对于相同的在线传感器系统，评估了两种校准方案：基于在整个测试期间获得的一组代表性校准样本 (n = 319) 训练事后预测模型，校准和测试集中有不同的奶牛，以及仅在测试期的第一周（n = 308）获得的样本上训练实时预测模型。所获得的预测模型在所有未包含在校准集中的剩余样本上进行了彻底测试（n 分别为 846 和 857）。对于事后预测模型，这导致了整体预测误差（预测的均方根误差，RMSEP) 小于 0.080%（所有百分比均以 wt/wt 为单位），乳脂（范围 1.5–6.3%）、蛋白质（2.6–4.3%）和乳糖（4–5.1%），决定系数 R2 为 0.989 ，脂肪、蛋白质和乳糖分别为 0.947 和 0.689。对于实时预测模型，乳脂和乳糖的 RMSEP 小于 0.092%，蛋白质的 RMSEP 小于 0.110%，R2 为 0.989（脂肪）、0.894（蛋白质）和 0.644（乳糖）。通过考虑奶牛特定的偏差，可以进一步改进牛奶乳糖预测（RMSEP = 0.088%，R2 = 0.675）。所提出的使用实时预测方法的在线传感器系统因此可用于在每次挤奶后对牛奶成分进行详细和自主的农场监测，因为其准确度完全符合国际动物记录委员会 (ICAR) 对农场牛奶分析仪的要求，甚至符合脂肪和乳糖实验室分析系统的标准。对于这种实时预测方法，在预测中观察到漂移，尤其是对于蛋白质。因此，需要进一步研究在线校准维护技术的发展，以纠正这种模型漂移并进一步提高该传感器系统的性能。