Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing

In this study, a novel chemometric algorithm for improved evaluation of analytical data is presented and applied to three spectroscopic data sets obtained by different analytical methods. This so-called secured principal component regression (sPCR) was developed for detecting and correcting uncalibrated spectral features newly emerging in spectra after finalizing the PCR calibration, which may result in major concentration errors. Hence, detection and correction of uncalibrated features is essential. Furthermore, detected uncalibrated features provide qualitative information for sensing and process monitoring applications indicating problems in the process flow. After conventional PCR calibration, sPCR analyzes measurement data in two steps: The first step investigates whether the obtained data set is consistent with the calibration model or not. If spectroscopic features are found that cannot be modeled by the principal components, they are extracted from the measurement spectrum. This corrected spectrum is then evaluated by conventional PCR. In the Experimental Section, sPCR was successfully applied to three data sets obtained by different spectroscopic measurements in order to corroborate general applicability of the proposed concept. For each data set, one of several substances was excluded from the calibration acting in the sPCR assessment as uncalibrated absorber. The test sets consisted of disturbed and undisturbed samples. A total of 109 out of 110 test samples were correctly classified as disturbed or undisturbed by an uncalibrated absorber. It was confirmed that the extracted disturbance spectra are in accordance with the spectra of the uncalibrated analytes. The concentration results obtained with sPCR were found to be equivalent to conventional PCR results in the case of undisturbed samples and more precise for disturbed samples.