Quartz-enhanced photoacoustic spectroscopy (QEPAS) has become a versatile tool for detection of trace gases at extremely low concentrations, leveraging the high quality (Q)-factor of quartz tuning forks. However, this high Q-factor imposes an intrinsic spectral resolution limit for fast wavelength sweeping with tunable laser sources due to the long ringing time of the tuning fork. Here, we introduce a technique to coherently control and damp the tuning fork by phase-shifting the modulation sequences of the driving laser. Particularly, we send additional laser pulses into the photoacoustic cell with a timing that corresponds to a π phase shift with respect to the tuning fork oscillation, effectively stopping its oscillatory motion. This enables acquisition of a complete methane spectrum spanning 3050–3450 nm in just three seconds, preserving the spectral shape. Our measured data is in good agreement with the theoretically expected spectra from the HITRAN database when convolved with the laser linewidth of . This will leverage the use of QEPAS with fast-sweeping OPOs in real-world gas sensing applications beyond laboratory environments with extremely fast acquisition speed enabled by our coherent control scheme.

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.