References

Comprehensive bibliography of scientific literature, algorithms, and resources used in the development of this application.

Table of Contents

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

    • 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

    • Pioneering work on biological Raman spectroscopy

Medical Applications

  1. 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

  2. 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

  3. Movasaghi, Z., et al. (2007)
    Raman Spectroscopy of Biological Tissues
    Applied Spectroscopy Reviews, 42(5), 493-541.
    DOI: 10.1080/05704920701551530

    • Comprehensive reference for Raman peak assignments in biological materials

Preprocessing Methods

Baseline Correction

  1. Eilers, P. H. C. (2003)
    A Perfect Smoother
    Analytical Chemistry, 75(14), 3631-3636.
    DOI: 10.1021/ac034173t

    • Whittaker smoother and baseline estimation

  2. 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

  3. Zhang, Z.-M., et al. (2010)
    Baseline Correction Using Adaptive Iteratively Reweighted Penalized Least Squares
    Analyst, 135(5), 1138-1146.
    DOI: 10.1039/B922045C

    • airPLS algorithm

  4. 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

    • drPLS and arpls algorithms

  5. 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

  6. Automated Weighted Method (AWM)
    Konevskikh, T., et al. (2016)
    Automated baseline correction for infrared spectra
    Analyst, 141(13), 3954-3962.
    DOI: 10.1039/c6an00355a

Smoothing and Denoising

  1. 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

    • Savitzky-Golay filter

  2. 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

Normalization

  1. 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

    • SNV normalization

  2. 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

    • MSC (Multiplicative Scatter Correction)

  3. 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

    • PQN normalization

Feature Engineering

  1. Geurts, P., Ernst, D., & Wehenkel, L. (2006)
    Extremely randomized trees
    Machine Learning, 63(1), 3-42.
    DOI: 10.1007/s10994-006-6226-1

    • Basis for feature importance methods

  2. 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

    • Wavelet transform theory

Machine Learning

Dimensionality Reduction

  1. 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

    • Original PCA paper

  2. 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

    • UMAP algorithm

  3. van der Maaten, L., & Hinton, G. (2008)
    Visualizing Data using t-SNE
    Journal of Machine Learning Research, 9, 2579-2605.

    • t-SNE algorithm

Classification

  1. Cortes, C., & Vapnik, V. (1995)
    Support-Vector Networks
    Machine Learning, 20(3), 273-297.
    DOI: 10.1007/BF00994018

    • SVM algorithm

  2. Breiman, L. (2001)
    Random Forests
    Machine Learning, 45(1), 5-32.
    DOI: 10.1023/A:1010933404324

    • Random Forest algorithm

  3. 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

    • XGBoost algorithm

  4. Barker, M., & Rayens, W. (2003)
    Partial least squares for discrimination
    Journal of Chemometrics, 17(3), 166-173.
    DOI: 10.1002/cem.785

    • PLS-DA algorithm

Interpretability

  1. 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)

  2. 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

    • LIME algorithm

Validation

  1. 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

    • Cross-validation theory

  2. 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

    • Nested cross-validation

Software Libraries

Core Dependencies

  1. The Qt Company (2023)
    Qt for Python (PySide6)
    https://www.qt.io/qt-for-python

    • GUI framework

  2. Harris, C. R., et al. (2020)
    Array programming with NumPy
    Nature, 585(7825), 357-362.
    DOI: 10.1038/s41586-020-2649-2

    • NumPy library

  3. 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

    • SciPy library

  4. 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

    • pandas library

  5. Pedregosa, F., et al. (2011)
    Scikit-learn: Machine Learning in Python
    Journal of Machine Learning Research, 12, 2825-2830.

    • scikit-learn library

  6. Hunter, J. D. (2007)
    Matplotlib: A 2D graphics environment
    Computing in Science & Engineering, 9(3), 90-95.
    DOI: 10.1109/MCSE.2007.55

    • matplotlib library

Specialized Libraries

  1. 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

    • RamanSPy library (preprocessing and analysis tools)

  2. 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

    • pybaselines library (baseline correction methods)

  3. 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

  1. 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

  2. Sheng, D., et al. (2022)
    Advancing Clinical Translation of Raman Spectroscopy
    Translational Biophotonics, 4(3), e202200003.
    DOI: 10.1002/tbio.202200003

  3. 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

Pre-disease (未病) Detection

  1. 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

    • Related to 未病 (mibyō) concept in preventive medicine

  2. 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

  1. 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

  2. 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

    • Comprehensive protocol for Raman analysis

Quality Control

  1. ASTM International (2020)
    ASTM E1840-96(2020) Standard Guide for Raman Shift Standards for Spectrometer Calibration
    DOI: 10.1520/E1840-96R20

  2. ISO 18115-1:2023
    Surface chemical analysis — Vocabulary — Part 1: General terms and terms used in spectroscopy

    • International standards for spectroscopic analysis

Software Citation

If you use this software in your research, please cite:

@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

  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)