Adaptive JIT-Lasso modeling for online application of near infrared spectroscopy

作者：Liu, Jin; Luan, Xiaoli^*; Liu, Fei

来源：Chemometrics and Intelligent Laboratory Systems, 2018, 183: 90-95.

DOI：10.1016/j.chemolab.2018.10.012

• Near-infrared spectroscopy
• Lasso
• Just-in-time learning
• Modeling updating

Near infrared (NIR) spectroscopy has been widely employed as a non-invasive analytical tool in industry. However, most MR model are built offline which cannot address changes in process characteristics as well as nonlinearity. To solve this problem, an adaptive JIT-Lasso algorithm was proposed by merging the least absolute shrinkage and the selection operator (Lasso) algorithm into just-in-time (JIT) learning. A time-space similarity measure criterion that combined temporal relevance and spatial relevance was used to further improve the performance of the JIT-Lasso algorithm. This solved both the space nonlinearity and the time-varying issue of the process simultaneously. The proposed model updating approach not only solved the nonlinear and the time varying issues based on JIT learning framework, but also reduced the computational complexity and improved the model interpretability through Lasso. The effectiveness of the method was demonstrated on a spectroscopic dataset from an industrial petroleum desalination process. Compared with traditional partial least squares, kernel partial least square, locally weighted partial least squares, locally weighted kernel partial least squares,the proposed method achieves better performance.

• 出版日期2018-12-15
• 单位江南大学