期刊
ISA TRANSACTIONS
卷 107, 期 -, 页码 206-213出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.07.020
关键词
Near-infrared spectroscopy modeling; Temperature-correction; Multilevel LASSO; Viscosity measurement of bisphenol-A
资金
- National Natural Science Foundation of China [61722306, 61833007, 61991402]
Temperature variation will affect the prediction accuracy when using the near-infrared (NIR) analytical model to measure the viscosity of bisphenol-A. In order to correct the effect of temperature on the prediction performance of NIR model, a multilevel least-absolute shrinkage and selection operator (LASSO) is proposed in this paper. The multilevel LASSO algorithm combines LASSO with the multilevel simultaneous component analysis (MLSCA) to enhance the robustness of the model under temperature changes and external disturbances. MLSCA is applied to decompose the molecular spectral data into two parts. One part denotes the property caused by temperature, the other means the changes of concentration. LASSO, a sparse regression model, is used to select the variables and perform the regularization to further enhance the robustness and interpretability of the model. Experimental results demonstrate the effectiveness of the proposed model in measuring bisphenol-A viscosity, which provides a more stable prediction result compared with the existing ones without temperature corrections. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
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