An improved particle swarm optimization algorithm for cavity-enhanced absorption spectroscopy

Cavity-enhanced absorption spectroscopy (CEAS) has become increasingly important in gas detection for air pollutants, human breath gases analysis, and monitoring industrial processes. However, the accuracy and sensitivity of CEAS are limited by noise and function optimization algorithms. To solve this problem, An improved particle swarm optimization (PSO) algorithm for CEAS was developed in this paper. The sensitivity of CEAS can be improved by combining various noise reduction filtering algorithms with PSO. Compared with traditional nonlinear fitting algorithms, the absorption coefficient can be achieved to 1.06×10^-8 cm^-1, with a minimum detectable limit of 5.31ppm, which is improved by 2.2 times. This research contributes to enhancing the precision of trace gas detection.