Gas concentration prediction in photoacoustic spectroscopy using PSO-EAP-CNN to address correlation degradation.

Photoacoustic spectroscopy (PAS) gas detection is frequently compromised by noise-induced correlation degradation, which significantly impacts measurement accuracy. To mitigate this issue, an advanced convolutional neural network (CNN) architecture, termed PSO-EAP-CNN, is proposed, which combines particle swarm optimization (PSO) with an ensemble augmented prediction (EAP) strategy. The proposed framework employs a multi-scale feature extraction mechanism through its convolutional architecture, while simultaneously optimizing network parameters via PSO, thereby achieving accelerated convergence and improved prediction stability. The incorporation of the EAP strategy further enhances the model's robustness and generalization ability under noisy conditions. Experimental results demonstrate significant improvements: compared to baseline CNN, PSO-EAP-CNN reduces MAE by 43.76 %, RMSE by 39.25 %, and MAPE by 51.15 %; compared to ordinary least squares regression, improvements reach 68.55 %, 67.43 %, and 75.21 % respectively. The model runs in only 10 seconds per execution. This work advances PAS-based gas detection, offering enhanced accuracy and noise resilience for practical trace gas analysis.