[Miniature near-infrared fibre optic spectrometer for the quantitative detection of protein and fat in milk powder].

IF 0.7 4区 化学 Q4 SPECTROSCOPY 光谱学与光谱分析 Pub Date : 2013-07-01
Zhong-Wei Zhang, Zhi-Yu Wen, Tian-Ling Zeng, Kang-Lin Wei, Yu-Qian Liang
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Abstract

The method based on miniature near-infrared spectrometer combined with Y fiber optic probe to detect the protein and fat in milk powder by diffuse reflectance spectroscopy in the wavelength range of 900-1 700 nm was proposed. By selecting the appropriate spectral bands, the correction models of protein and fat were established with partial least squares algorithm using Unscrambler 9.7 Chemometrics software. The determination coefficients R2 of the correction modes are 0.987 and 0.986 for protein and fat respectively, and the root mean square errors RMSEC are 0.385 and 0.419 respectively. Using these correction models to predict the protein and fat contents with 30 sets of forecast sample data, the prediction standard deviation is SEP(Protein) = 0.751 for protein, and is SEP(Fat) = 1.109 for fat. The results indicate that these correction models have prediction capability with unknown samples and meet the on line requirements.

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[用于奶粉中蛋白质和脂肪定量检测的微型近红外光纤光谱仪]。
提出了基于微型近红外光谱仪结合Y型光纤探头在900 ~ 1 700 nm波长范围内漫反射光谱法检测奶粉中蛋白质和脂肪的方法。通过选择合适的光谱波段,利用Unscrambler 9.7化学计量学软件,用偏最小二乘算法建立蛋白质和脂肪的校正模型。蛋白质和脂肪校正模式的决定系数R2分别为0.987和0.986,均方根误差RMSEC分别为0.385和0.419。利用这些校正模型对30组预测样本数据进行蛋白质和脂肪含量的预测,蛋白质的预测标准差为SEP(protein) = 0.751,脂肪的预测标准差为SEP(fat) = 1.109。结果表明,修正模型具有对未知样本的预测能力,满足在线要求。
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来源期刊
光谱学与光谱分析
光谱学与光谱分析 Physics and Astronomy-Instrumentation
CiteScore
1.60
自引率
14.30%
发文量
18084
审稿时长
1.44444 months
期刊介绍:
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