How to achieve auto-identification in Raman analysis by spectral feature extraction & Adaptive Hypergraph

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• Miniaturization of Raman spectrometers enables rapid detection applications.
• Handling massive Raman spectral databases requires effective and automatic identification methods.
• Surface-enhanced Raman spectroscopy (SERS) is a key technique in this field.

Purpose of the Study:

• To develop an automated material identification algorithm for Raman spectra.
• To improve the efficiency and accuracy of analyzing large spectral datasets.
• To create a universal algorithm applicable across different Raman spectrometer databases.

Main Methods:

• Spectral feature extraction after background subtraction.
• Classification and identification using Adaptive Hypergraph (AH) machine learning classifier.
• Enhancement of algorithm universality using Cubic Spline Interpolation for inter-database compatibility.

Main Results:

• The proposed algorithm achieves an accuracy rate of approximately 99% for material identification.
• Adaptive Hypergraph (AH) classifier demonstrates superior performance compared to Support Vector Machine (SVM) and Random Forest (RF) without parameter tuning.
• An accuracy rate of up to 98% is achieved for cross-database identification between high and low frequency sampling spectra.

Conclusions:

• The developed algorithm provides an effective and automated solution for Raman spectral analysis and material identification.
• Adaptive Hypergraph (AH) offers a robust and parameter-free classification approach for diverse spectral targets.
• Cubic Spline Interpolation successfully enhances the algorithm's universality, enabling reliable performance across different Raman spectrometer vendors and databases.