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首页> 外文会议>International Conference on Experimental Mechanics >On-line milk spectrometry: analysis of bovine milk composition
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On-line milk spectrometry: analysis of bovine milk composition

机译：在线乳光谱法：牛奶组合物分析

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We present partial least squares (PLS) regressions to predict the composition of raw, unhomogenised milk using visible to near infrared spectroscopy. A total of 370 milk samples from individual quarters were collected and analysed on-line by two low cost spectrometers in the wavelength ranges 380-1100 nm and 900-1700 nm. Samples were collected from 22 Friesian, 17 Jersey, 2 Ayrshire and 3 Friesian-Jersey crossbred cows over a period of 7 consecutive days. Transmission spectra were recorded in an inline flowcell through a 0.5 mm thick milk sample. PLS models, where wavelength selection was performed using iterative PLS, were developed for fat, protein, lactose, and somatic cell content. The root mean square error of prediction (and correlation coefficient) for the nir and visible spectrometers respectively were 0.70%(0.93) and 0.91%(0.91) for fat, 0.65%(0.5) and 0.47%(0.79) for protein, 0.36%(0.49) and 0.45%(0.43) for lactose, and 0.50(0.54) and 0.48(0.51) for log_(10) somatic cells.
机译：我们呈现局部最小二乘（PLS）回归以预测使用近红外光谱法可见的原料无量化牛奶的组成。在波长范围380-1100nm和900-1700nm的波长范围内，在线上收集来自各个季度的370个来自各个季度的牛奶样品。在连续7天的时间内从22辆弗里斯，17个泽西，2个牛仔裤和3辆弗里斯 - 泽西杂交牛来收集样品。通过0.5mm厚的牛奶样品在型流体晶面中记录透射光谱。 PLS模型，使用迭代PLS进行波长选择的模型，用于脂肪，蛋白质，乳糖和体细胞含量。 NIR和可见光度计的预测（和相关系数）的根均方误差分别为脂肪的0.70％（0.93）和0.91％（0.91），0.65％（0.5）和0.47％（0.79），0.36％ （0.49）和0.45％（0.43）乳糖，0.50（0.54）和0.48（0.51）用于log_（10）体细胞。

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