Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 12, 2022
- Source ID
- 10.1002/jrs.6447
Entities
People
- Joy Qiaoyi Li
- Michael Sarkis
- Priya Vohra Dukes
- Tuan Vo-Dinh
- Walter Lee
Organizations
- Duke University
- National Institute of General Medical Sciences
- United States Department of Defense