[Research on concentration of multi-component pollution gas based on SVM with kernel optimized by rough set]

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Context:

• Infrared spectroscopy is crucial for quantitative analysis of gas mixtures.
• Traditional methods face challenges with complex spectral data and low separability.
• Accurate quantification of multi-component pollution gases is essential for environmental monitoring.

Purpose:

• To introduce a novel support vector machine (SVM) regression method optimized by rough set (RS) for infrared spectrum quantitative calculation.
• To enhance the accuracy of gas concentration prediction in multi-component pollution gas mixtures.
• To evaluate the performance of the RS-SVM method against traditional algorithms.

Summary:

• The study applies a rough set-optimized kernel function to support vector machine regression for infrared spectral data.
• This approach projects spectral data into a higher-dimensional space for accurate gas concentration calculation.
• The RS-SVM method demonstrates superior precision over other methods, particularly when spectral separability is low, with an average error below 0.13.

Impact:

• Provides a more precise and robust method for quantitative analysis of gas mixtures using infrared spectroscopy.
• Offers a significant advancement in environmental monitoring and pollution detection technologies.
• Highlights the effectiveness of combining rough set theory with machine learning for complex spectral data analysis.