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Related Experiment Video

Updated: Jul 16, 2026

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

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Published on: March 22, 2019

Intelligent Algorithm-Assisted Indirect Absorption Spectroscopy for Trace Gas Sensing.

Yangkun Huang¹, Ying He¹, Shunda Qiao¹

¹National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

Intelligent algorithms are enhancing indirect absorption spectroscopy (IAS) techniques like photoacoustic spectroscopy (PAS) by overcoming hardware limitations. This approach improves trace gas sensing through advanced signal processing and AI-driven optimization.

Keywords:

deep learning digital signal processing intelligent optimization light-induced thermoelastic spectroscopy (LITES)machine learning photoacoustic spectroscopy (PAS)quartz-enhanced photoacoustic spectroscopy (QEPAS)

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Area of Science:

Spectroscopy
Analytical Chemistry
Sensor Technology

Background:

Indirect absorption spectroscopy (IAS) techniques, including photoacoustic spectroscopy (PAS), quartz-enhanced photoacoustic spectroscopy (QEPAS), and light-induced thermoelastic spectroscopy (LITES), have traditionally relied on hardware enhancements for trace gas sensing.
Advancements in hardware are facing limitations due to weak responses, cross-interferences, and complex multiphysics parameters.

Purpose of the Study:

To review the integration of algorithm-assisted methods into IAS systems.
To highlight the shift from hardware-centric to intelligent, algorithm-driven enhancements for trace gas sensing.

Main Methods:

Summarizing progress in signal processing and spectral reconstruction for weak-signal recovery and noise suppression.
Detailing data-driven parameter inversion using artificial intelligence for concentration retrieval, environmental compensation, and spectral-overlap decoupling.
Examining intelligent system optimization through surrogate modeling, swarm intelligence, and physics-guided design for key spectroscopic components like resonators and multipass cells (MPCs).

Main Results:

Algorithm-assisted methods effectively address limitations of conventional hardware enhancements in IAS.
AI models enable sophisticated data analysis for improved accuracy and multi-component recognition.
Intelligent optimization enhances the design efficiency of critical spectroscopic elements.

Conclusions:

The integration of intelligent algorithms represents a significant advancement in indirect absorption spectroscopy.
Future developments will likely focus on further synergistic integration of AI and advanced hardware for next-generation trace gas sensors.