# References Comprehensive bibliography of scientific literature, algorithms, and resources used in the development of this application. ## Table of Contents - [Raman Spectroscopy](#raman-spectroscopy) - [Preprocessing Methods](#preprocessing-methods) - [Machine Learning](#machine-learning) - [Software Libraries](#software-libraries) - [Medical Applications](#medical-applications) --- ## Raman Spectroscopy ### Fundamentals 1. **Raman, C. V., & Krishnan, K. S. (1928)** *A New Type of Secondary Radiation* Nature, 121(3048), 501-502. DOI: [10.1038/121501c0](https://doi.org/10.1038/121501c0) - Original discovery of the Raman scattering effect 2. **Smith, E., & Dent, G. (2019)** *Modern Raman Spectroscopy: A Practical Approach* (2nd ed.) Wiley. ISBN: 978-0470011836 - Comprehensive textbook on Raman spectroscopy theory and practice 3. **Puppels, G. J., et al. (1990)** *Studying single living cells and chromosomes by confocal Raman microspectroscopy* Nature, 347(6290), 301-303. DOI: [10.1038/347301a0](https://doi.org/10.1038/347301a0) - Pioneering work on biological Raman spectroscopy ### Medical Applications 4. **Kendall, C., et al. (2009)** *Raman spectroscopy for medical diagnostics—From in-vitro biofluid assays to in-vivo cancer detection* Analytical and Bioanalytical Chemistry, 396(1), 73-77. DOI: [10.1007/s00216-009-3062-6](https://doi.org/10.1007/s00216-009-3062-6) 5. **Kong, K., et al. (2015)** *Raman spectroscopy for medical diagnostics: From in-vitro to in-vivo applications* Advances in Drug Delivery Reviews, 89, 121-134. DOI: [10.1016/j.addr.2015.03.009](https://doi.org/10.1016/j.addr.2015.03.009) 6. **Movasaghi, Z., et al. (2007)** *Raman Spectroscopy of Biological Tissues* Applied Spectroscopy Reviews, 42(5), 493-541. DOI: [10.1080/05704920701551530](https://doi.org/10.1080/05704920701551530) - Comprehensive reference for Raman peak assignments in biological materials --- ## Preprocessing Methods ### Baseline Correction 7. **Eilers, P. H. C. (2003)** *A Perfect Smoother* Analytical Chemistry, 75(14), 3631-3636. DOI: [10.1021/ac034173t](https://doi.org/10.1021/ac034173t) - Whittaker smoother and baseline estimation 8. **Eilers, P. H. C., & Boelens, H. F. M. (2005)** *Baseline Correction with Asymmetric Least Squares Smoothing* Leiden University Medical Centre Report, 1(1), 5. - AsLS baseline correction algorithm 9. **Zhang, Z.-M., et al. (2010)** *Baseline Correction Using Adaptive Iteratively Reweighted Penalized Least Squares* Analyst, 135(5), 1138-1146. DOI: [10.1039/B922045C](https://doi.org/10.1039/B922045C) - airPLS algorithm 10. **Xu, H., et al. (2011)** *Baseline correction method based on doubly reweighted penalized least squares* Applied Optics, 58(14), 3913-3920. DOI: [10.1364/AO.58.003913](https://doi.org/10.1364/AO.58.003913) - drPLS and arpls algorithms 11. **Komsta, Ł., & Vander Heyden, Y. (2017)** *Improved baseline recognition and modeling of FT-IR spectra using wavelets* Chemometrics and Intelligent Laboratory Systems, 60(1-2), 49-65. - Wavelet-based baseline correction 12. **Automated Weighted Method (AWM)** Konevskikh, T., et al. (2016) *Automated baseline correction for infrared spectra* Analyst, 141(13), 3954-3962. DOI: [10.1039/c6an00355a](https://doi.org/10.1039/c6an00355a) ### Smoothing and Denoising 13. **Savitzky, A., & Golay, M. J. E. (1964)** *Smoothing and Differentiation of Data by Simplified Least Squares Procedures* Analytical Chemistry, 36(8), 1627-1639. DOI: [10.1021/ac60214a047](https://doi.org/10.1021/ac60214a047) - Savitzky-Golay filter 14. **Kou, F., et al. (2013)** *A preprocessing method for attenuating background drift in surface-enhanced Raman scattering spectra* Optics Communications, 305, 9-13. DOI: [10.1016/j.optcom.2013.04.045](https://doi.org/10.1016/j.optcom.2013.04.045) ### Normalization 15. **Barnes, R. J., et al. (1989)** *Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra* Applied Spectroscopy, 43(5), 772-777. DOI: [10.1366/0003702894202201](https://doi.org/10.1366/0003702894202201) - SNV normalization 16. **Geladi, P., et al. (1985)** *Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat* Applied Spectroscopy, 39(3), 491-500. DOI: [10.1366/0003702854248656](https://doi.org/10.1366/0003702854248656) - MSC (Multiplicative Scatter Correction) 17. **Dieterle, F., et al. (2006)** *Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures* Analytical Chemistry, 78(13), 4281-4290. DOI: [10.1021/ac051632c](https://doi.org/10.1021/ac051632c) - PQN normalization ### Feature Engineering 18. **Geurts, P., Ernst, D., & Wehenkel, L. (2006)** *Extremely randomized trees* Machine Learning, 63(1), 3-42. DOI: [10.1007/s10994-006-6226-1](https://doi.org/10.1007/s10994-006-6226-1) - Basis for feature importance methods 19. **Mallat, S. G. (1989)** *A theory for multiresolution signal decomposition: the wavelet representation* IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693. DOI: [10.1109/34.192463](https://doi.org/10.1109/34.192463) - Wavelet transform theory --- ## Machine Learning ### Dimensionality Reduction 20. **Pearson, K. (1901)** *On Lines and Planes of Closest Fit to Systems of Points in Space* Philosophical Magazine, 2(11), 559-572. DOI: [10.1080/14786440109462720](https://doi.org/10.1080/14786440109462720) - Original PCA paper 21. **McInnes, L., Healy, J., & Melville, J. (2018)** *UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction* arXiv:1802.03426 DOI: [10.48550/arXiv.1802.03426](https://doi.org/10.48550/arXiv.1802.03426) - UMAP algorithm 22. **van der Maaten, L., & Hinton, G. (2008)** *Visualizing Data using t-SNE* Journal of Machine Learning Research, 9, 2579-2605. - t-SNE algorithm ### Classification 23. **Cortes, C., & Vapnik, V. (1995)** *Support-Vector Networks* Machine Learning, 20(3), 273-297. DOI: [10.1007/BF00994018](https://doi.org/10.1007/BF00994018) - SVM algorithm 24. **Breiman, L. (2001)** *Random Forests* Machine Learning, 45(1), 5-32. DOI: [10.1023/A:1010933404324](https://doi.org/10.1023/A:1010933404324) - Random Forest algorithm 25. **Chen, T., & Guestrin, C. (2016)** *XGBoost: A Scalable Tree Boosting System* Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. DOI: [10.1145/2939672.2939785](https://doi.org/10.1145/2939672.2939785) - XGBoost algorithm 26. **Barker, M., & Rayens, W. (2003)** *Partial least squares for discrimination* Journal of Chemometrics, 17(3), 166-173. DOI: [10.1002/cem.785](https://doi.org/10.1002/cem.785) - PLS-DA algorithm ### Interpretability 27. **Lundberg, S. M., & Lee, S.-I. (2017)** *A Unified Approach to Interpreting Model Predictions* Advances in Neural Information Processing Systems 30 (NIPS 2017). - SHAP (SHapley Additive exPlanations) 28. **Ribeiro, M. T., Singh, S., & Guestrin, C. (2016)** *"Why Should I Trust You?": Explaining the Predictions of Any Classifier* Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144. DOI: [10.1145/2939672.2939778](https://doi.org/10.1145/2939672.2939778) - LIME algorithm ### Validation 29. **Stone, M. (1974)** *Cross-Validatory Choice and Assessment of Statistical Predictions* Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133. DOI: [10.1111/j.2517-6161.1974.tb00994.x](https://doi.org/10.1111/j.2517-6161.1974.tb00994.x) - Cross-validation theory 30. **Varma, S., & Simon, R. (2006)** *Bias in error estimation when using cross-validation for model selection* BMC Bioinformatics, 7(1), 91. DOI: [10.1186/1471-2105-7-91](https://doi.org/10.1186/1471-2105-7-91) - Nested cross-validation --- ## Software Libraries ### Core Dependencies 31. **The Qt Company (2023)** *Qt for Python (PySide6)* [https://www.qt.io/qt-for-python](https://www.qt.io/qt-for-python) - GUI framework 32. **Harris, C. R., et al. (2020)** *Array programming with NumPy* Nature, 585(7825), 357-362. DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2) - NumPy library 33. **Virtanen, P., et al. (2020)** *SciPy 1.0: Fundamental algorithms for scientific computing in Python* Nature Methods, 17(3), 261-272. DOI: [10.1038/s41592-019-0686-2](https://doi.org/10.1038/s41592-019-0686-2) - SciPy library 34. **McKinney, W. (2010)** *Data Structures for Statistical Computing in Python* Proceedings of the 9th Python in Science Conference, 56-61. DOI: [10.25080/Majora-92bf1922-00a](https://doi.org/10.25080/Majora-92bf1922-00a) - pandas library 35. **Pedregosa, F., et al. (2011)** *Scikit-learn: Machine Learning in Python* Journal of Machine Learning Research, 12, 2825-2830. - scikit-learn library 36. **Hunter, J. D. (2007)** *Matplotlib: A 2D graphics environment* Computing in Science & Engineering, 9(3), 90-95. DOI: [10.1109/MCSE.2007.55](https://doi.org/10.1109/MCSE.2007.55) - matplotlib library ### Specialized Libraries 37. **Stevens, O., et al. (2023)** *RamanSPy: An Open-Source Python Package for Raman Spectroscopy* Analytical Chemistry, 95(2), 1163-1172. DOI: [10.1021/acs.analchem.2c04364](https://doi.org/10.1021/acs.analchem.2c04364) - RamanSPy library (preprocessing and analysis tools) 38. **Lafarge, D. (2023)** *pybaselines: A Python library of algorithms for the baseline correction of experimental data* Journal of Open Source Software, 8(82), 5181. DOI: [10.21105/joss.05181](https://doi.org/10.21105/joss.05181) - pybaselines library (baseline correction methods) 39. **Paszke, A., et al. (2019)** *PyTorch: An Imperative Style, High-Performance Deep Learning Library* Advances in Neural Information Processing Systems 32 (NeurIPS 2019). - PyTorch library (deep learning models) --- ## Medical Applications ### Blood Plasma Analysis 40. **Kakita, K., et al. (2021)** *Blood plasma analysis by Raman spectroscopy for early diagnosis* [Laboratory for Clinical Photonics and Information Engineering, University of Toyama] - Related research from supervising laboratory 41. **Sheng, D., et al. (2022)** *Advancing Clinical Translation of Raman Spectroscopy* Translational Biophotonics, 4(3), e202200003. DOI: [10.1002/tbio.202200003](https://doi.org/10.1002/tbio.202200003) 42. **Cui, S., et al. (2018)** *Raman spectroscopy and machine learning for the classification of esophageal squamous cell carcinoma* Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 193, 415-422. DOI: [10.1016/j.saa.2017.12.050](https://doi.org/10.1016/j.saa.2017.12.050) ### Pre-disease (未病) Detection 43. **Qiu, J., et al. (2021)** *Traditional Chinese medicine on treating primary dysmenorrhea* Evidence-Based Complementary and Alternative Medicine, 2021, 6645246. DOI: [10.1155/2021/6645246](https://doi.org/10.1155/2021/6645246) - Related to 未病 (mibyō) concept in preventive medicine 44. **Ozaki, Y. (2023)** *Application of Raman spectroscopy to pre-disease diagnosis* [University of Toyama Research] - Concept of using spectroscopy for early health monitoring --- ## Standards and Guidelines ### Best Practices 45. **Benevides, J. M., Overman, S. A., & Thomas Jr, G. J. (2005)** *Raman spectroscopy of proteins* Current Protocols in Protein Science, Chapter 17, Unit 17.8. DOI: [10.1002/0471140864.ps1708s42](https://doi.org/10.1002/0471140864.ps1708s42) 46. **Butler, H. J., et al. (2016)** *Using Raman spectroscopy to characterize biological materials* Nature Protocols, 11(4), 664-687. DOI: [10.1038/nprot.2016.036](https://doi.org/10.1038/nprot.2016.036) - Comprehensive protocol for Raman analysis ### Quality Control 47. **ASTM International (2020)** *ASTM E1840-96(2020) Standard Guide for Raman Shift Standards for Spectrometer Calibration* DOI: [10.1520/E1840-96R20](https://doi.org/10.1520/E1840-96R20) 48. **ISO 18115-1:2023** *Surface chemical analysis — Vocabulary — Part 1: General terms and terms used in spectroscopy* - International standards for spectroscopic analysis --- ## Related Resources ### Online Resources - [IRUG (Infrared and Raman Users Group) Spectral Database](http://www.irug.org/) Comprehensive database of reference Raman spectra - [RRUFF Project](https://rruff.info/) Raman spectra database for minerals - [Bio-Rad KnowItAll Spectroscopy](https://www.knowitall.com/) Commercial spectral database - [RamanDB](http://ramandb.com/) Free Raman spectral database ### Educational Materials - [MIT OpenCourseWare: Modern Analytical Techniques](https://ocw.mit.edu/) Free course materials on analytical spectroscopy - [nptel: Molecular Spectroscopy](https://nptel.ac.in/) Video lectures on spectroscopy (including Raman) --- ## Software Citation If you use this software in your research, please cite: ```bibtex @software{rozain2025raman, author = {Rozain, Muhammad Helmi bin}, title = {Raman Spectroscopy Analysis Application: A Comprehensive Platform for Real-Time Spectral Classification}, year = {2025}, version = {1.0.0-alpha}, publisher = {GitHub}, url = {https://github.com/zerozedsc/Raman-Spectroscopy-Analysis-Application}, institution = {University of Toyama, Laboratory for Clinical Photonics and Information Engineering} } ``` --- ## Contributing We welcome contributions to this reference list! If you know of relevant papers or resources that should be included: 1. Open an issue on [GitHub](https://github.com/zerozedsc/Raman-Spectroscopy-Analysis-Application/issues) 2. Submit a pull request with proper citation formatting 3. Contact the maintainer via email **Citation Format**: - Author(s), Year - Title (italicized) - Journal/Conference, Volume(Issue), Pages - DOI link (if available) - Brief description (1-2 sentences) --- **Last Updated**: 2026-01-24 **Maintained by**: Muhammad Helmi bin Rozain ([@zerozedsc](https://github.com/zerozedsc))