Revolutionizing Diagnostics: Machine Learning Unleashes the Power of Surface-Enhanced Raman Spectroscopy

Published on: 

A recent study published in Applied Spectroscopy presents a new approach to biomedical diagnosis through the surface-enhanced Raman spectroscopy (SERS)-based detection of micro-RNA (miRNA) biomarkers using a comparative study of interpretable machine learning (ML) algorithms (1). Led by Joy Q. Li of Duke University, the research team conducted more SERS research by introducing a multiplexed SERS-based nanosensor, named the inverse molecular sentinel (iMS) for miRNA detection. As machine learning (ML) increasingly becomes a vital tool in spectral analysis, the researchers grappled with the high dimensionality of SERS data, a challenge for traditional ML techniques prone to overfitting and poor generalization (1).

The team explored the performance of ML methods, including convolutional neural network (CNN), support vector regression, and extreme gradient boosting, both with and without non-negative matrix factorization (NMF) for spectral unmixing of four-way multiplexed SERS spectra from iMS assays (1). Remarkably, CNN stands out for achieving high accuracy in spectral unmixing. However, the incorporation of NMF before CNN proves revolutionary, drastically reducing memory and training demands without compromising model performance on SERS spectral unmixing (1).

The study also used these ML models to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, emerged as the top performers, demonstrating high accuracy in spectral unmixing (1).

To enhance transparency and understanding, the researchers employed gradient class activation maps and partial dependency plots to interpret the predictions. This approach not only showcases the potential of CNN-based ML in spectral unmixing of multiplexed SERS spectra, but it also underscores the significant impact of dimensionality reduction on performance and training speed (1).


This research highlights the intersection of spectroscopy and machine learning, providing new opportunities for precise and efficient diagnostics that could enhance biomedical applications and improve patient outcomes.

This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.


(1) Li, J. Q., Neng-Wang, H., Canning, A. J., et al. Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms. Appl. Spectrosc. 2023, ASAP. DOI: 10.1177/0037028231209053