Multivariate Calibration for Spectral Analysis Based on P-Spline Signal Regression with Net Analyte Signal

摘要

A P-spline signal regression with net analyte signal is proposed in this article to improve the prediction performance of the quantitative calibration model. PSR with NAS is easy to be interpreted and computed, and it has strong connections to classical regression. PSR with NAS is straightforward to use: It uses the entire ("raw") signal and works without any data preprocessing. It is superior to PSR in terms of accuracy and flexibility. PSR with NAS quantification was successfully applied to two experimental spectra data sets for analysis of leaf biochemical parameters. It is shown that the P-spline signal regression with net analyte signal procedure for these two data sets performs better than PLS, PCR, and PSR in terms of precision. Furthermore, since it can take full advantage of the relevant information, PSR with NAS has better adaptability for the complex spectra model, which has the potential capability for multivariate calibration and spectral analysis. It is expected that the optimal prediction accuracy can be obtained when the most informative wavelength bands, the fitting pretreatment method, and the PSR with NAS calibration are all used in the study. Therefore, PSR with NAS is a highly competitive and promising method. The excellent prediction performance by PSR with NAS for leaf biochemical parameter determination can be expanded and made more stable for future practical applications.