BAARC: Uncertainty-Driven Adaptive Partial Spectral Measurement to Full Reconstruction for Material Classification

Spectroscopic instruments with limited wavenumber coverage require accurate scan positioning or multiple measurements to complete the material analysis. This often requires prerequisite knowledge about the material under diagnosis to measure the spectral regions with distinguishing peaks. In our study, we simulate a Bayesian Adaptive Acquisition with Reconstruction-driven Classification (BAARC) framework for Raman spectral measurements, using masked training to learn spectral features that dynamically drive laser channel selection until the material is identified. We present a jointly trained two-level framework that takes progressive partial measurements of small wavenumber patches across the complete spectral range and, at each step, first attempts to reconstruct the full spectra until the model uncertainty is low. Secondly, material classification is performed at each reconstruction step until stoppage. Uncertainty quantification during reconstruction steps helps determine the measurement position of our next patch, while combining it with classification uncertainty determines measurement termination. By closing the loop between hardware acquisition and deep probabilistic modeling, the proposed system minimizes physical measurement steps while maintaining both spectral fidelity and diagnostic accuracy. Experimental evaluations demonstrate that our framework achieves high-accuracy sample identification using only a fraction of full-spectrum channels, offering a scalable solution for real-time, high-throughput spectroscopic instruments.