OpenAlex ·
Automated feature-region selection for soft-sensor development from spectral-like measurements
- Published
- Source
- OpenAlex
- Published in
- arXiv (Cornell University)
- DOI
- 10.48550/arxiv.2609.27082
- Original
- Read it at the publisher
- Topics
Abstract
Sustainable production increasingly relies on process analytical chemistry and process analytical technology to support monitoring, control, and automation. Techniques used in these contexts, including Raman spectroscopy, infrared spectroscopy, and electrochemical voltammetry, generate high-dimensional signals ordered along physical measurement axes, referred to here as spectral-like measurements. These signals can contain redundant, weakly informative, and noisy regions, complicating the development of data-driven soft sensors to map them to process variables. Selecting informative regions is nontrivial, as visually prominent regions are not necessarily the most predictive, while synergistic effects among regions cannot be readily inferred. Here, we introduce an automated feature-region selection framework that identifies a parsimonious set of contiguous regions while preserving channel ordering. The framework combines channel-level target correlation and a signal-to-noise indicator into a latent information fingerprint. Variable-width candidate intervals are derived from peaks in this fingerprint, and their combinations are ranked using held-out validation data. Final selection favours models using fewer channels among candidates with comparable predictive performance. The framework is demonstrated using cyclic voltammetric measurements of glucose acquired with a gold electrode sensor across four progressively broader nominal concentration ranges up to 350 g/L. Gaussian process regression is used within the framework to accommodate possible nonlinear relationships, account for observation noise, and quantify epistemic uncertainty. The selected models retained only 11-42 of the original 400 channels and reduced held-out test root mean squared error by 88.3-94.6% relative to full-feature Gaussian process regression benchmarks.
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