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.