Wavelength selection for laser design in mid-infrared spectroscopy

Summary

The development of miniaturized tunable laser sources for mid-infrared (MIR) spectroscopy has enabled portable, application-specific analytical devices. Recent advances in quantum cascade lasers (QCLs) and interband cascade lasers (ICLs) allow precise wavelength emission in narrow spectral regions, such as 1700–1600 cm − 1 , which is critical for protein characterization. In this study, we evaluate machine learning techniques for selecting the most informative wavelengths to guide the design of tunable laser systems, and for their ability to account for specific constraints such as the possibility to do fine and coarse laser wavelength tuning. We focus on optimizing variable selection for a laser-based device targeting peptide analysis and protein quality assessment in hydrolysates as a case study. We compare sparse modelling techniques (SPLS), filter-based (SPA, CovSel, g-CovSel), and compression methods (PVS, PVR), and propose a new algorithm (w-CovSel) to assess their ability to reduce noise and isolate key spectral features. Our results highlight the potential of providing data-driven approaches to obtain laser design which enables high-performance MIR instrumentation tailored to specific analytical tasks.

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DOI : doi.org/10.3389/fphot.2026.169...
NVA : hdl.handle.net/11250/5368344