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Characterizing viral samples using machine learning for Raman and absorption spectroscopy
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  • Miad Boodaghidizaji,
  • Shreya Milind Athalye,
  • Sukirt Thakur,
  • Ehsan Esmaili,
  • Mohit Verma,
  • Arezoo Ardekani
Miad Boodaghidizaji
Purdue University

Corresponding Author:[email protected]

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Shreya Milind Athalye
Purdue University System
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Sukirt Thakur
Purdue University System
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Ehsan Esmaili
Purdue University System
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Mohit Verma
Purdue University
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Arezoo Ardekani
Purdue University
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Machine learning methods can be used as robust techniques to provide invaluable information for analyzing biological samples in pharmaceutical industries, such as predicting the concentration of viral particles of interest in biological samples. Here, we utilized both convolutional neural networks and random forests to predict the concentration of the samples containing measles, mumps, rubella, and varicella-zoster viruses (ProQuad®) based on Raman and absorption spectroscopy. We prepared Raman and absorption spectra datasets with known concentration values, then used the Raman and absorption signals individually and together to train RFs and CNNs. We demonstrated that both RFs and CNNs can make predictions with R2 values as high as 95%. We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Additionally, we further verified the advantage of using joint Raman-absorption with principal component analysis (PCA). Furthermore, our method can be extended to characterize properties other than concentration, such as the type of viral particles.
22 Aug 2022Submitted to MicrobiologyOpen
26 Aug 2022Submission Checks Completed
26 Aug 2022Assigned to Editor
31 Aug 2022Reviewer(s) Assigned
11 Sep 2022Review(s) Completed, Editorial Evaluation Pending
13 Sep 2022Editorial Decision: Revise Minor
01 Oct 20221st Revision Received
03 Oct 2022Assigned to Editor
03 Oct 2022Submission Checks Completed
03 Oct 2022Review(s) Completed, Editorial Evaluation Pending
04 Oct 2022Reviewer(s) Assigned
18 Oct 2022Editorial Decision: Revise Minor
25 Oct 20222nd Revision Received
26 Oct 2022Assigned to Editor
26 Oct 2022Submission Checks Completed
27 Oct 2022Review(s) Completed, Editorial Evaluation Pending
31 Oct 2022Editorial Decision: Accept
Dec 2022Published in MicrobiologyOpen volume 11 issue 6. 10.1002/mbo3.1336