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

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Spectrometric methods combined with chemometrics enable high-throughput analysis of complex systems.
• Signal preprocessing is crucial for reducing interference and optimizing calibration models.
• The impact of preprocessing on statistical accuracy of calibration results requires further investigation.

Purpose of the Study:

• To demonstrate effective criteria for validating signal preprocessing in multivariate models.
• To assess preprocessing's impact on bias and precision in calibration results.
• To provide a graphical diagnostic for optimizing pretreatment strategies.

Main Methods:

• Utilized the elliptic joint confidence region (EJCR) test to assess bias.
• Developed a bias-corrected root mean square error of prediction for precision evaluation.
• Applied and evaluated these criteria on three spectral data sets with standard pretreatment strategies.

Main Results:

• The proposed criteria effectively gauge the success of signal pretreatments in suppressing spectral interference.
• The methodology provides a straightforward means to determine optimal model complexity.
• Demonstrated reliable optimization of multivariate calibration models using standard pretreatment strategies.

Conclusions:

• The developed methodology enhances the statistical accuracy and reliability of spectrometric analysis.
• It enables confident optimization of complex multivariate models through graphical diagnostics.
• Improves the overall capability of spectrophotometric analysis through validated preprocessing.