Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array.

Area of Science:

• Chemical Sensing
• Data Analysis
• Machine Learning

Background:

• Gas sensor arrays are crucial for gas measurement due to their sensitivity and low power consumption.
• Cross-sensitivity remains a significant challenge, hindering accurate gas mixture analysis.
• Existing methods struggle with the complexity of identifying multiple gases simultaneously.

Purpose of the Study:

• To develop a novel method for accurate qualitative identification and quantitative analysis of gas mixtures using gas sensor arrays.
• To address the challenge of cross-sensitivity in gas sensing applications.
• To improve the performance of gas sensor array analysis through advanced machine learning techniques.

Main Methods:

• Feature extraction from raw sensor data using Principal Component Analysis (PCA).
• Qualitative identification of gas mixtures via Random Forest (RF) modeling.
• Quantitative analysis using Support Vector Regression (SVR) optimized by Particle Swarm Optimization (PSO) for hyperparameter selection (C and γ).

Main Results:

• The PCA-RF model achieved the highest average recognition rate (97%) compared to Logistic Regression (LR) and Support Vector Machine (SVM).
• PSO-optimized SVR demonstrated superior performance in fitting gas concentrations compared to standard SVR.
• The proposed method effectively solved the challenge of hyperparameter selection for SVR.

Conclusions:

• The combined PCA-RF and PSO-SVR approach offers a robust and accurate solution for gas mixture analysis.
• This method significantly enhances the reliability and precision of gas sensor array applications.
• The study demonstrates the potential of advanced machine learning for overcoming limitations in chemical sensing technology.