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Related Experiment Video

Updated: Jul 16, 2025

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
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RamanCMP: A Raman spectral classification acceleration method based on lightweight model and model compression

Zengyun Gong1, Chen Chen2, Cheng Chen1

  • 1College of Software, Xinjiang University, Urumqi, 830046, Xinjiang, China.

Analytica Chimica Acta
|September 14, 2023
PubMed
Summary

This study optimizes deep learning models for Raman spectroscopy using lightweight architectures and compression, significantly improving classification speed and accuracy for mineral identification. The RamanCompact (RamanCMP) framework enhances efficiency without sacrificing performance.

Keywords:
Lightweight modelModel compressionRaman spectroscopyRamanCMP

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Area of Science:

  • Spectroscopy
  • Machine Learning
  • Mineralogy

Background:

  • Raman spectroscopy is increasingly used in medical, chemical, and geological fields.
  • Optimization of deep learning techniques and model compression is needed for processing Raman spectral data efficiently.

Purpose of the Study:

  • To optimize deep learning models for Raman spectroscopy using metrics like time, accuracy, sensitivity, specificity, and FLOPs.
  • To develop a framework named RamanCompact (RamanCMP) for efficient Raman spectral data analysis.

Main Methods:

  • Proposed 1D-EfficientNet and 1D-DRSN models adapted for spectral data.
  • Implemented three model compression methods: knowledge distillation, channel conversion, and feature extraction with Linear Discriminant Analysis (LDA).
  • Utilized the RRUFF public dataset comprising 723 Raman spectroscopy samples from 10 mineral categories.

Main Results:

  • 1D-EfficientNet and 1D-DRSN models achieved over 20% accuracy improvement compared to traditional LDA and CNN models.
  • Knowledge distillation reduced model time by 9680.9s with a 2.07% accuracy loss, and improved accuracy by 20% over the CNN student model.
  • Channel conversion optimized 1D-DRSN saved 60% inference time; feature extraction reduced inference time by 93% while maintaining 94.48% accuracy.

Conclusions:

  • The study successfully combined lightweight models and model compression to enhance deep learning classification speed for Raman spectroscopy.
  • The developed RamanCompact (RamanCMP) framework offers a comprehensive approach for efficient Raman spectral analysis.
  • This work lays a foundation for future research in optimizing deep learning for spectroscopic applications.