Novel near-infrared spectrum analysis tool: Synergy adaptive moving window model based on immune clone algorithm

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Traditional spectrum analysis often relies on manual, experience-based selection of pre-processing methods and spectral variables, which is time-consuming and inefficient.
• Existing optimization strategies may not adequately address the complexities of spectral data, particularly for intricate spectral structures.

Purpose of the Study:

• To introduce a novel, automated spectrum analysis tool, SA-MWM-ICA (Synergy Adaptive Moving Window Modeling based on Immune Clone Algorithm).
• To overcome the limitations of traditional methods by integrating an immune clone algorithm for optimizing spectral analysis parameters.
• To enhance the performance and efficiency of quantitative spectral modeling.

Main Methods:

• The study introduces the immune clone algorithm, inspired by the human immune system, into spectral analysis.
• Antibodies represent quantitative model performance, with regions for pre-processing and spectral variable optimization (moving windows).
• High-affinity antibodies are selected for cloning and hyper-mutation to generate improved models, mimicking biological immune responses.

Main Results:

• SA-MWM-ICA demonstrated superior performance in quantitative models compared to traditional methods like PLS, MWPLS, and GAPLS, particularly for complex spectra.
• The method effectively automates the selection of pre-processing methods and spectral variables, providing interpretable results.
• Validation was performed using simulated, gasoline, and soil near-infrared spectra datasets.

Conclusions:

• SA-MWM-ICA offers a robust and efficient approach to spectrum analysis, significantly improving quantitative model accuracy.
• The algorithm converges rapidly, making it suitable for various spectral analysis applications beyond near-infrared spectroscopy, including infrared spectroscopy.
• The automated optimization process simplifies complex spectral data analysis and enhances model reliability.