Symmetrized Dot Pattern Infrared Absorption Spectroscopy

This manuscript presents an innovative methodology in the field of Infrared Absorption Spectroscopy (IAS) for gas sensing, termed the Symmetrized Dot Pattern (SDP) IAS. Traditional IAS techniques primarily focus on the amplitude of time-domain signals, often over-looking crucial information embedded within the signal structure. Addressing this limitation, our SDP methodology transforms these time-domain signals into graphical representations, thereby enhancing the extraction and analysis of data. We established the SDP framework, detailing the theoretical underpinnings of both IAS and SDP, complemented by simulation results from the HITRAN database, which served as a foundation for our experimental setup. In the practical application phase of our research, we have implemented this framework by configuring an IAS system tailored for recording calibration signals across a spectrum of gas concentrations, thereby setting the stage for real-world applicability. The experiments conducted under this setup have yielded promising results, demonstrating the efficacy of the SDP method in gas sensing applications. The system demonstrated an error margin ranging from 0.5% to 2.6%, coupled with a repetitive standard deviation of 4.82%, and achieving a detection limit of 3.81 ppm. These metrics not only attest to the precision and reliability of the SDP approach in detecting and quantifying gas concentrations but also show its potential application in the field of environmental monitoring and safety. Looking forward, we envision the integration of machine learning algorithms into the SDP framework. This integration is anticipated to further augment the capabilities of the SDP technique, potentially leading to more precise predictions, expedited processing times, and automated analysis of data.