Raman Spectroscopy Analysis Application Documentation
Welcome to the comprehensive documentation for the Raman Spectroscopy Analysis Application, a state-of-the-art desktop software for real-time Raman spectral classification and disease detection.
📖 About This Project
The Raman Spectroscopy Analysis Application is an open-source, cross-platform desktop software designed for real-time classification and disease detection using Raman spectroscopy. Developed at the University of Toyama under the Laboratory for Clinical Photonics and Information Engineering (臨床光情報工学研究室).
Key Features
40+ Preprocessing Methods: Research-validated algorithms including baseline correction, smoothing, and normalization
Real-Time Analysis: Interactive PCA, UMAP, t-SNE, clustering, and statistical tests
Machine Learning: Complete ML pipeline with SVM, Random Forest, XGBoost, and interpretability via SHAP
Modern GUI: Intuitive PySide6/Qt6 interface with multi-language support (English/Japanese)
Production Ready: Portable executables and installers for clinical deployment
Open Source: MIT License, contributions welcome
🌐 Language Support
This documentation is available in:
English (You are here)
日本語 (Japanese) - Switch to Japanese
📋 Table of Contents
Getting Started
User Guide
Analysis Methods
- Analysis Methods Reference
- Preprocessing Methods Reference
- Exploratory Analysis Methods
- Table of Contents
- Principal Component Analysis (PCA)
- MCR-ALS
- UMAP (Uniform Manifold Approximation and Projection)
- t-SNE (t-Distributed Stochastic Neighbor Embedding)
- Hierarchical Clustering
- K-Means Clustering
- DBSCAN (Density-Based Spatial Clustering)
- Method Comparison
- Validation Metrics
- Best Practices
- See Also
- Statistical Analysis Methods
- Machine Learning Methods
API Documentation
Additional Resources
🔬 Research Context
Project Title: Real-Time Raman Spectroscopy Classification Software for Disease Detection
Institution: University of Toyama (富山大学)
Laboratory: Clinical Photonics and Information Engineering
Developer: Muhammad Helmi bin Rozain (12270294)
Program: BSc Information Intelligence Engineering (工学部の知能情報工学コース、4年生、学部生)
Supervisors: 大嶋 佑介 (Oshima Yusuke), 竹谷 皓規 (Taketani Akinori)
🤝 Contributing
We welcome contributions from the community! See our Contributing Guide for details on:
Reporting bugs and requesting features
Submitting pull requests
Code style guidelines
Documentation standards
📞 Support & Contact
Issues: GitHub Issues
Discussions: GitHub Discussions
Email: @zerozedsc
📄 License
This project is licensed under the MIT License. See the LICENSE file for details.
Note: This software is intended for research use only and is not approved for clinical diagnostic purposes.