利用神经网络提高偏最小二乘法的NIR多组分分析精度  

Improving Partial Least Square Regression Precision in NIR Multi-Component Analysis Using Artificial Neural Network

作　　者：白英奎[1] 孟宪江[1] 丁东[1] 申铉国[2] 

机构地区：[1]吉林大学通信工程学院,吉林长春130025 [2]吉林大学物理学院,吉林长春130025

出　　处：《光谱学与光谱分析》2005年第3期381-383,共3页Spectroscopy and Spectral Analysis

基　　金：教育部高等学校骨干教师资助计划基金 (2 0 0 0 931 )资助

摘　　要：提出了一种神经网络 (ANN)和偏最小二乘法 (PLS)结合的新的近红外 (NIR)多组分分析法。该方法首先把训练样本中待测组分涵盖的浓度区间分成若干个子区间 ,利用各个子区间的训练样本分别建立PLS校正模型 ,然后利用ANN对未知样本进行分类 ,判断其所属的浓度子区间 ,应用对应子区间上的校正模型计算预测样本的组分浓度。和传统的PLS比较 ,此方法改善了模型的适应性 ,显著地提高了预测精度。实验及数据处理结果证明了本方法的有效性。The present paper presents a new NIR multi-component analysis method with Artificial Neural Network(ANN) and Partial Least Square Regression(PLS). First, this method divides the concentration range of training samples into some sub-ranges, and respectively computes a PLS correlation model in each sub-range with the sub-range's training samples. Then, the authors classify prediction samples according to its concentration sub-range with ANN and judge which sub-range the prediction sample belongs to. Finally, the authors compute the concentration of prediction component with the PLS correlation model of the sub-range according to ANN. The experiment and the result of data processing show that this method improves the model's applicability, and evidently enhances prediction precision compared to traditional PLS.

关 键 词：子区间 多组分分析 校正模型 偏最小二乘法 证明 样本 预测精度 PLS NIR 浓度 

分 类 号：O657[理学—分析化学] O174.13[理学—化学]