Robust Identification of Gas Mixtures from FTIR Spectra using Attention Mechanism to Mitigate Instrument Line Shape Variations

Fourier transform infrared spectroscopy enables rapid, nondestructive identification of mixture components through characteristic absorption peaks. However, in practical applications, challenges such as instrument line shape variations, overlapping absorption peaks, and various measurement errors significantly complicate the identification of mixtures. To address this, we developed an innovative deep learning framework based on an attention mechanism. Extensive experiments were conducted on a self-constructed data set comprising ten distinct instrument line shapes and eight gas components. Remarkably, it attained exact match ratios exceeding 91.7% when applied to the other nine instrument line shapes, outperforming existing methods by margins ranging from 25% to 88%. These findings demonstrate the model's robust generalization capability and efficient deployment flexibility, while more importantly highlighting its significant potential for cross-device applications, other FTIR mixture analyses, and similar spectroscopic challenges, such as transfer function in near-infrared spectroscopy.