A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm

作者：Li, Pao; Du, Guorong^*; Ma, Yanjun; Zhou, Jun; Jiang, Liwen

来源：Chemometrics and Intelligent Laboratory Systems, 2018, 176: 157-161.

DOI：10.1016/j.chemolab.2018.03.013

• Near-infrared spectroscopy
• Variable adaptive strategy
• Boosting
• Partial least squares
• Multivariate calibration

With the aid of chemometric methods, near-infrared (NIR) spectroscopy is widely applied to the analysis of complex samples. Boosting partial least squares (BPLS) is one of the important approaches and has many applications to improve the predictive stability and accuracy of the NIR models. However, accurate calculation results are difficult to obtain due to the redundant variables that contribute more collinearity and noise than relevant information to models. In this work, an algorithm named as variable adaptive boosting partial least squares (VABPLS) was proposed to get higher robustness models and enhance the prediction ability. The theory and calculation of VABPLS are just similar with BPLS algorithm, but a variable adaptive strategy based on adaptive reweighted sampling (ARS) theory is fused into the algorithm to improve the accuracy, instead of simply adding. Simultaneous weighting of samples and variables in the boosting series is found to be more effective than the single weighting. The performance of VABPIS is tested with the NIR spectral datasets of corn and tobacco leaf samples. Results show that the advantages of VABPLS are many, such as enhanced accuracy, less parameters and easy to implement, compared with the traditional approaches.

• 出版日期2018-5-15
• 单位湖南农业大学