Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Background:

• Surface-enhanced Raman spectroscopy (SERS) offers sensitive molecular fingerprinting but faces challenges in data requirements and interpretability for practical trace analysis.
• Machine learning (ML) algorithms can enhance SERS analysis, yet 'black-box' models limit real-world application due to poor interpretability and high data demands.

Purpose of the Study:

• To develop a novel ML framework (Vis-CAD) that addresses the limitations of data demand and interpretability in SERS trace analysis.
• To integrate visual random forest, characteristic amplifier, and data augmentation for improved SERS data analysis.
• To enable reliable and interpretable trace analysis of complex mixtures using SERS.

Main Methods:

• Developed Vis-CAD, a machine learning framework combining visual random forest, characteristic amplifier, and data augmentation.
• Implemented data augmentation to significantly reduce the need for large datasets.
• Utilized random forest visualization to interpret captured features and assess algorithm reliability.

Main Results:

• Achieved trustworthy accuracy of no less than 99% for trace analysis of polycyclic aromatic hydrocarbons in a mixture.
• Demonstrated that visualized features strongly correlate with characteristic Raman peaks.
• Improved sensitivity for trace analyte detection by at least one order of magnitude compared to naked-eye analysis.

Conclusions:

• Vis-CAD offers a new approach for SERS trace analysis by minimizing data requirements and providing operational transparency.
• The framework enhances the reliability and interpretability of ML in spectroscopy.
• Vis-CAD facilitates highly sensitive qualitative and quantitative analysis of trace targets in SERS and other spectroscopic fields.