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近红外光谱分析中建模样品优选方法的研究
引用本文:王丽杰,郭建英,徐可欣.近红外光谱分析中建模样品优选方法的研究[J].红外技术,2005,27(1):75-78.
作者姓名:王丽杰  郭建英  徐可欣
作者单位:1. 哈尔滨理工大学,测控技术与通信工程学院,黑龙江,哈尔滨,150080
2. 天津大学,精密测试技术及仪器国家重点实验室,天津,300072
摘    要:结合牛奶成分近红外光谱测量系统的实例,在已定的浓度范围内针对牛奶中脂肪、蛋白质、乳糖三成分采用正交设计法优选参与建模的样品。研究中首次利用正交表的“正交性”原理优选建模样品,并针对牛奶中脂肪浓度的测量采用偏最小二乘(PLS)回归方法交互验证方式建立模型。在此基础上,将正交设计样品集与常规方法选择的样品集的脂肪PLS模型的预测结果进行了对比。实验结果表明:采用正交设计样品集与常规样品集分别建立的PLS模型的预测偏差之差低于0.02g/100g,上述两种方法PLS模型的实际预测浓度与参考浓度之差均集中在0.1g/100g,而后者样品数量约为前者的七倍。进一步的实验结果表明:从常规样品集的样品中随机抽取与正交设计样品集的样品数量相同的样品作为随机样品集并建模,其PLS模型的预测偏差高于常规方法的两倍、相关系数相对较低,并且其实际预测浓度与参考浓度之差集中在0.4g/100g。

关 键 词:PLS  脂肪  正交设计  首次  样品  预测  牛奶成分  浓度  数量  近红外光谱分析
文章编号:1001-8891(2005)01-0075-04

Optimized Method of Selecting Samples for modeling in NIR Spectral Analysis
WANG Li-jie,GUO Jian-ying,XU Ke-xin.Optimized Method of Selecting Samples for modeling in NIR Spectral Analysis[J].Infrared Technology,2005,27(1):75-78.
Authors:WANG Li-jie  GUO Jian-ying  XU Ke-xin
Abstract:In order to reduce excessive experiments and to improve model applicability, for the first time, the samples for modeling are selected by the Orthogonal Design Method and applied to NIR Spectral System for measuring milk constituents. By selecting the samples for modeling using the principle of orthogonality in the orthogonal table to fat, protein and lactose of milk, Partial Least Square (PLS) Regression model was built by means of cross validation for measuring the fat concentration of these samples, and the predictions of this model and other two models are compared. To the latter two models, a model built with samples selected by the conventional method, the other model whose sample set size is the same as the orthogonal sample set built with samples extracted randomly from the samples already selected by the conventional method. The results indicate that the difference between the prediction error of the orthogonal model and that of the conventional model is less than 0.02g/100g. For these two models, the discrepancy between the predicted concentration and the reference concentration is about 0.1g/100g. However, the sample size of the orthogonal model is about seven times that of the conventional model. Furthermore, the prediction error of the third model is larger than that of the former two models, and its correlation coefficient is smaller than the correlation coefficients of the former two models. In addition, the difference between the predicted concentration from the third model and reference concentration is about 0.4g/100g.
Keywords:Near-Infrared (NIR) Spectral Analysis  Orthogonal Design Method  Orthogonality  Milk  Partial Least Square (PLS) Regression
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