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首页> 外文期刊>Journal of dairy research >Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process
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Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process

机译：用于预测挤奶过程中牛奶成分的近红外校准模型的鲁棒性

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The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.
机译：就预测原料乳中脂肪，蛋白质和乳糖的含量，测试了使用近红外光谱（NIRS）在线分析原料乳的鲁棒性。在六个月的时间里，在354次挤奶的挤奶过程中，在三个不同的农场上获得了原料奶（n = 3119）的近红外（NIR）光谱。计算校准模型的依据是：每个农场的随机数据集（完全随机的内部校准）；每个农场访问的前三分之二（内部校准）；三个场中的两个的整个数据集（外部校准），以及内部和外部数据集的组合。对每个服务器场的其余数据集（内部验证）或对其余服务器场的数据（外部验证）进行验证。当将完全随机的内部校准集用于牛奶分析时，可获得出色的校准结果。在这种情况下，用于预测脂肪，蛋白质和乳糖含量的RPD值分别约为10、5和3。农场内部校准的预测结果差得多，特别是对于蛋白质和乳糖的预测，RPD值分别约为2和1。在外部农场的光谱上进行验证时，预测准确性得到了提高，这主要是由于在外部校准集中，在饲喂日粮和个体奶牛影响方面样品变化较大。结果表明，将其他场信息添加到校准集中后，可以实现进一步的改进。强大的校准模型的主要要求之一是能够预测未来未知牛奶样品中的牛奶成分。预测的鲁棒性和质量随着例如校准模型中的喂养和母牛个体奶组成的变化的增加而增加。

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