# Vibrational Fingerprinting of Gas Mixtures Using COCO-QEPAS

## TL;DR

This paper introduces a new method using COCO-QEPAS to rapidly and accurately detect and monitor multiple trace gases at low concentrations in real time.

## Contribution

The novel use of in situ learning with COCO-QEPAS enables rapid fingerprinting of arbitrary gas mixtures without prior composition knowledge.

## Key findings

- Real-time analysis of mixtures with up to four trace gases at ppm-level was demonstrated.
- Empirical mode decomposition improved spectral feature recovery at the noise floor.
- Principal component regression enabled accurate quantitative analysis in the ppb regime.

## Abstract

Detection and simultaneous monitoring of multiple trace gases is vital in scientific and industrial processes. Here, we use coherent control in quartz-enhanced photoacoustic spectroscopy (COCO-QEPAS) with an in situ learning method for rapid fingerprinting of trace gases to identify and monitor arbitrary gases at very low concentrations, without prior knowledge of gas composition. We validate this on various mixtures, including CH4/C2H2/C2H4/C2H6/NO2/NH3. To this end, we demonstrate real-time analysis of mixtures containing up to four trace gases at ppm-level, monitoring changes in seconds using linear regression. The scalability of simultaneously distinguishable gases is straightforward. Furthermore, we expand fingerprinting to 10 ppm with a detection limit of 180 ppb CH4, and apply empirical mode decomposition as an adaptive, data-driven filtering method to recover characteristic spectral features at the noise floor. For quantitative analysis in the ppb regime, we employ principal component regression as a calibration model that exploits correlations across the full spectrum. Consequently, our method offers significant potential for sensing applications where speed, accuracy, and simplicity are critical.