Enhancing standardization through score-augmented projection-based calibration transfer

Abstract

Reconstructing statistical models for novel instrumentation entails substantial time and financial investments. To obviate the necessity for such model reconstruction, standardization techniques are widely employed. In this context, we introduce a pioneering standardization method termed Score-Augmented Projection-Based Standardization (SA-PBS). Central to SA-PBS is the extraction of spectral sub-spaces containing interferents common to both the primary and secondary instruments. This extraction employs orthogonal projections as the foundational principle. Execution of the standardization procedure involves the judicious selection of a subset from the primary instrument's recorded dataset, facilitated by the Convex hull tool. This technique ensures the inclusion of independent and information-rich samples from the calibration dataset. Crucially, a subset of samples from the secondary instrument is also incorporated to ensure comprehensive coverage of interferents.Validation of the efficacy of the proposed SA-PBS method is conducted through quantitative analyses of target analytes across diverse scenarios, utilizing the Partial Least Squares method. Comparative assessments are performed against alternative standardization approaches, namely Direct Standardization (DS), Piecewise Direct Standardization (PDS), and Transfer Component Analysis (TCA).