[Quantitative analysis of multi-component gas mixture based on KPCA and SVR]

Area of Science:

• Spectroscopy
• Chemometrics
• Machine Learning

Context:

• Quantitative analysis of mid-infrared spectra is crucial for identifying and quantifying chemical components.
• Traditional methods may face challenges with complex mixtures and high-dimensional spectral data.
• Accurate and efficient analysis of multi-component gas mixtures is essential in various industrial and environmental applications.

Purpose:

• To develop a novel quantitative analysis method for mid-infrared spectra of multi-component gas mixtures.
• To combine Kernel Principal Component Analysis (KPCA) with Support Vector Regression (SVR) for enhanced spectral analysis.
• To evaluate the performance of the KPCA-SVR model in terms of accuracy and efficiency compared to SVR alone.

Summary:

• A quantitative analysis model was created by mapping mid-infrared spectra nonlinearly into a high-dimensional feature space using Gaussian kernels (KPCA).
• Principal components were extracted and used as inputs for Support Vector Regression (SVR) to build a model for seven-component gas mixtures.
• The KPCA-SVR model demonstrated significantly lower prediction RMSE and reduced modeling/prediction times compared to the SVR model.

Impact:

• KPCA effectively extracts nonlinear features, maximizes spectral information, reduces noise, and lowers spectral dimensions.
• The combined KPCA-SVR approach improves analytical precision and significantly decreases the time required for modeling and analysis.
• This study concludes that KPCA-SVR is a highly effective new method for quantitative analysis in infrared spectroscopy.