Universal Calibration: Can Models Travel Successfully Across Instruments?

Inter-instrument variability is a major obstacle in multivariate spectroscopic analysis, affecting the reliability and portability of calibration models. This tutorial addresses the theoretical and practical challenges of model transfer across instruments. It covers spectral variability sources—such as wavelength shifts, resolution differences, and line shape variations—and presents key standardization techniques including direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). We discuss the underlying mathematics of these approaches using matrix notation and highlight limitations that must be considered for reliable universal calibration.

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Publication Details

Journal
Spectroscopy
Published
2025-08-11
DOI
https://doi.org/10.56530/spectroscopy.ie7568a1
Citations
1
Primary Topic
Domain Adaptation and Few-Shot Learning
Type
article
Field-Weighted Citation Impact
2.48
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article

Universal Calibration: Can Models Travel Successfully Across Instruments?

Jerome Workman
1 citations
Spectroscopy
Domain Adaptation and Few-Shot Learning
2.48
article

Universal Calibration: Can Models Travel Successfully Across Instruments?

Jerome Workman
article en
1 citations

Abstract

Inter-instrument variability is a major obstacle in multivariate spectroscopic analysis, affecting the reliability and portability of calibration models. This tutorial addresses the theoretical and practical challenges of model transfer across instruments. It covers spectral variability sources—such as wavelength shifts, resolution differences, and line shape variations—and presents key standardization techniques including direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). We discuss the underlying mathematics of these approaches using matrix notation and highlight limitations that must be considered for reliable universal calibration.

Spectroscopy
Institute of Spectroscopy (RU)
Openalex Percentile: Top 9%
Domain Adaptation and Few-Shot Learning
2.48
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