A³M‑CIR: An Attention-Aware Adversarial Masking Based Self-Supervised Chemometric Framework for Robust Infrared Spectral Analysis

However, its practical utilization is often constrained by instrument-dependent variability, baseline drift, acquisition noise, and limited labeled data. While deep-learning-based chemometric methods have improved performance over traditional preprocessing-driven pipelines, their reliance on supervised learning limits robustness and transferability across heterogeneous measurement conditions.

To address these challenges, we introduce A³M-CIR, an attention-aware adversarial masking-based self-supervised chemometric framework for robust infrared spectral analysis. The framework performs large-scale self-supervised pretraining on diverse unlabeled IR spectra to learn transferable representations without explicit calibration or manual annotation. Its core component, the Attention-Aware Adversarial Feature Masking Block (A³FMB), suppresses diagnostically salient absorption regions to generate informative contrastive views that emulate realistic perturbations, while a lightweight Dual-Mixing Attention (DMA) encoder captures both global spectral context and local continuity of one-dimensional vibrational signals.