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Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups—for example, amide, amino acid, and carboxylic acid—we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.
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This content will become publicly available on January 17, 2026
Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning
Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis and detection paradigm.
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- PAR ID:
- 10598388
- Editor(s):
- Mitra, Saheli
- Publisher / Repository:
- PLOS
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0317130
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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