title: [Improving partial least square regression precision in NIR multi-component analysis using artificial neural network].
abstract: 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.
authors: Jilin University, College of Communication Engineering, Changchun 130025, China.
authors: Ying-kui
authors: YK
authors: Bai
authors: Xian-jiang
authors: XJ
authors: Meng
authors: Dong
authors: D
authors: Ding
authors: Xuan-guo
authors: XG
authors: Shen
mesh_terms: 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
chemical_list: D004364:Pharmaceutical Preparations