[Improving partial least square regression precision in NIR multi-component analysis using artificial neural network].
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 theprediction 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.
Bai
Ying-kui
YK
Jilin University, College of Communication Engineering, Changchun 130025, China.
Meng
Xian-jiang
XJ
Ding
Dong
D
Shen
Xuan-guo
XG
D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000032:analysis*; D015203:Reproducibility of Results; D019265:Spectroscopy, Near-Infrared / Q000379:methods / Q000592:standards*; D013678:Technology, Pharmaceutical / Q000379:methods / Q000592:standards
D004364:Pharmaceutical Preparations