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

## TL;DR

This paper introduces a new toolbox for analyzing Raman spectra using machine learning and neural networks to predict chemical concentrations in biomedical applications.

## Contribution

The novel contribution is an integrated toolbox combining artificial neural networks and traditional machine learning for Raman spectral analysis.

## Key findings

- The AI-Raman toolbox successfully predicted glucose concentrations from in vivo Raman spectra.
- The toolbox includes both artificial neural networks and classical machine learning algorithms for spectral analysis.
- The software's graphical interface allows customization and optimization for various biomedical applications.

## Abstract

Based on its rich information of chemical specificity, Raman spectroscopy has been widely applied for in vivo biomedical investigations. For extracting quantitative information of target constitution, it is imperative to establish a robust model for unveiling the relationship between spectral features with/without priori references. By integrating a variety of traditional machine learning and artificial neural network algorithms, an integrated Raman spectra analysis toolbox (AI-Assisted Raman Spectra Analysis Toolbox [AI-Raman] V 1.0) was developed for spectral processing, model training, and regression analysis by using MATLAB R2024a. Besides the utilization of back propagation artificial neural network and convolutional neural network algorithms, classical machine learning algorithms, such as partial least squares regression and support vector regression, were also compacted as the supporting functions of presented toolbox. A spectral dataset obtained from nailfold from different subjects was utilized to evaluated the feasibility and performance of the developed software, which demonstrated that the analysis software can predict glucose concentrations by in vivo Raman spectral measurement. With a friendly graphics interface, the analytical model can be customized and optimized for accomplishing the desired objectives, which will benefit many Raman-based inventions, especially for biomedical transformations.