ArticleACS omega2024
RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.ACS omega · 2026Article
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.bioRxiv : the preprint server for biology · 2026Article
- A Setup for Automatic Raman Measurements in High-Throughput Experimentation.Biotechnology and bioengineering · 2025Article
- Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinomaWorld journal of gastroenterology · 2025Article
- Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.
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Registered trials
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