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A knowledge-guided structured Bayesian regression framework for spectroscopic calibration
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DOI：10.1016/j.chemolab.2026.105729.png)
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Abstract
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• A knowledge-guided structured Bayesian regression is proposed for calibration. • VIHC partitions spectra using RCs to incorporate response-variable information. • BAPL globally optimizes interval combinations via likelihood-driven Bayesian learning. • Structured sparsity and continuity are jointly captured and exploited. • SBR improves prediction accuracy while enhancing physicochemical interpretability.
Keywords:
Structured Bayesian regression
Spectroscopic calibration
Response-variable information
Structured sparsity
Physicochemical interpretability
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