Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy.

Area of Science:

• Spectroscopy
• Signal Processing
• Artificial Intelligence

Background:

• Absorption spectroscopy is crucial for gas detection, but noise often hinders accurate analysis.
• Traditional filtering methods like Savitzky-Golay (S-G) require manual parameter selection, limiting real-time adaptability.
• Nitrogen dioxide (NO2) detection is vital for environmental monitoring and breath analysis.

Purpose of the Study:

• To develop an improved Savitzky-Golay (S-G) filtering algorithm for denoising NO2 absorption spectra.
• To integrate a deep learning (DL) network for real-time, adaptive adjustment of S-G filter parameters.
• To evaluate the performance of the adaptive S-G filter against traditional methods and demonstrate its effectiveness in a gas sensing system.

Main Methods:

• Developed an adaptive Savitzky-Golay (S-G) filter by incorporating a deep learning (DL) network.
• The DL network dynamically adjusts the S-G filter's window size and polynomial order.
• Compared the adaptive S-G filter's performance against the multi-signal averaging filtering (MAF) algorithm.
• Implemented the optimized S-G filter in a quantum-cascade-laser (QCL) based NO2 gas sensor.

Main Results:

• The adaptive S-G filter effectively denoises NO2 absorption spectra.
• The DL network's self-adjusting capability overcomes the limitations of fixed parameter selection in traditional digital signal processing.
• A sensitivity enhancement factor of 5 was achieved using the optimized S-G filtering algorithm.
• The algorithm generated high-quality gas absorption spectra suitable for sensitive detection.

Conclusions:

• The developed adaptive S-G filtering algorithm significantly improves the quality of NO2 absorption spectra.
• Deep learning integration provides real-time adaptability, enhancing the robustness of spectral denoising.
• The enhanced spectral quality enables more accurate NO2 detection, with applications in environmental monitoring and exhaled breath analysis.