Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics

Journal Article (Journal Article)

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS “spectral unmixing” from a multiplexed mixture of 7 SERS-active “nanorattles” loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSElabel = 6.42 × 10−2. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics.

Full Text

Duke Authors

Cited Authors

  • Li, JQ; Dukes, PV; Lee, W; Sarkis, M; Vo-Dinh, T

Published Date

  • December 1, 2022

Published In

Volume / Issue

  • 53 / 12

Start / End Page

  • 2044 - 2057

Electronic International Standard Serial Number (EISSN)

  • 1097-4555

International Standard Serial Number (ISSN)

  • 0377-0486

Digital Object Identifier (DOI)

  • 10.1002/jrs.6447

Citation Source

  • Scopus