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Developing an Integrated Toolbox for Raman Spectral Analysis with Both Artificial Neural Networks and Machine

Xiangtao Kong1, Jie Xu1, Guodi Fan2

  • 1Institute of Photonics and Photon-Technology, Northwest University, Xi'an 710069, China.

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|February 27, 2026
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Summary
This summary is machine-generated.

A new AI-Assisted Raman Spectra Analysis Toolbox (AI-Raman) uses machine learning to analyze in vivo Raman spectroscopy data. This tool accurately predicts glucose concentrations from nailfold spectral measurements, aiding biomedical applications.

Keywords:
AI-RamanRaman spectramachine learningneural networkquantitative analysis

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Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Computational Biology

Background:

  • Raman spectroscopy offers rich chemical specificity for in vivo biomedical investigations.
  • Extracting quantitative molecular information requires robust models linking spectral features to composition.
  • Developing advanced analytical tools is crucial for translating Raman spectroscopy into clinical practice.

Purpose of the Study:

  • To develop an integrated software toolbox for processing and analyzing Raman spectral data.
  • To implement various machine learning and artificial neural network algorithms for quantitative analysis.
  • To evaluate the performance of the developed toolbox for in vivo biomedical measurements.

Main Methods:

  • Developed the AI-Assisted Raman Spectra Analysis Toolbox (AI-Raman) V 1.0 using MATLAB R2024a.
  • Integrated classical machine learning algorithms (partial least squares regression, support vector regression) and artificial neural networks (back propagation, convolutional neural networks).
  • Utilized a nailfold spectral dataset from diverse subjects to assess the software's feasibility and predictive accuracy.

Main Results:

  • The AI-Raman toolbox successfully processed Raman spectra and performed regression analysis.
  • The software demonstrated the ability to predict glucose concentrations from in vivo nailfold Raman spectral measurements.
  • The user-friendly graphical interface allows for customization and optimization of analytical models.

Conclusions:

  • The AI-Raman toolbox provides a robust platform for quantitative analysis of in vivo Raman spectroscopy data.
  • The developed software facilitates accurate glucose concentration prediction, showing significant potential for biomedical applications.
  • AI-Raman is a valuable tool for advancing Raman-based technologies, particularly in the field of biomedical diagnostics and monitoring.