Multivariate calibration for spectral analysis based on P-spline signal regression with net analyte signal

In this article, a P-spline signal regression (PSR) with net analyte signal (NAS) method is proposed to construct a quantitative calibration model with high precision. The PSR with NAS method creates a basis coefficient vector by using a projection matrix for computing the NAS of the target analyte. PSR with NAS inherits the advantages of PSR, but is superior to PSR in terms of accuracy and flexibility. Two visible-near infrared (vis-NIR) spectra data sets from basic research experiments, including a leaf chlorophyll experiment and a leaf water experiment, are used to evaluate the performance of PSR with NAS. The root mean squared error of prediction (RMSEP), residual predictive deviation (RPD), and correlation coefficient indicate that the PSR with NAS method gives a better predictive accuracy than other calibration methods. Moreover, the results imply that PSR with NAS has distinct adaptability for the complex spectra model. It is shown that PSR with NAS is a promising multivariate calibration approach for spectral analysis.