Evaluating the validity of spectral calibration models for quantitative analysis following signal preprocessing

When paired with high-powered chemometric analysis, spectrometric methods offer great promise for the high-throughput analysis of complex systems. Effective classification or quantification often relies on signal preprocessing to reduce spectral interference and optimize the apparent performance of a calibration model. However, less frequently addressed by systematic research is the affect of preprocessing on the statistical accuracy of a calibration result. The present work demonstrates the effectiveness of two criteria for validating the performance of signal preprocessing in multivariate models in the important dimensions of bias and precision. To assess the extent of bias, we explore the applicability of the elliptic joint confidence region (EJCR) test and devise a new means to evaluate precision by a bias-corrected root mean square error of prediction. We show how these criteria can effectively gauge the success of signal pretreatments in suppressing spectral interference while providing a straightforward means to determine the optimal level of model complexity. This methodology offers a graphical diagnostic by which to visualize the consequences of pretreatment on complex multivariate models, enabling optimization with greater confidence. To demonstrate the application of the EJCR criterion in this context, we evaluate the validity of representative calibration models using standard pretreatment strategies on three spectral data sets. The results indicate that the proposed methodology facilitates the reliable optimization of a well-validated calibration model, thus improving the capability of spectrophotometric analysis.