Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning

Abstract

Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.

Ultra-sensitive detection of PFASs using surface enhanced Raman scattering and machine learning: a promising approach for environmental analysis

“…The obtained SERS spectra cannot be directly compared to Raman spectra. 54 This deserves further investigation. Second, the PFAS molecules still have low affinity to any substrates presented in this work, which was suggested by the concentration-dependent…”

Uncovering Art’s Vanishing Hues with Surface-Enhanced Raman Scattering: Drawing Inspiration from the Past for the Future

“…In addition to EC-SERS, we anticipate that advances in machine learning for spectral analysis and classification will further expand the capabilities of SERS , and other tools in nanoscience to increasingly complex samples and problems in conservation. For example, we recently demonstrated that several artists’ colorants (i.e., rhodamine and anthraquinone dyes) can be classified with machine learningdown to the single-molecule detection limitusing their intrinsic fluorescence dynamics .…”

SERS microscopy as a tool for comprehensive biochemical characterization in complex samples

“…77,120–124 Such a collection of spectra under a large set of (relevant) experimental conditions would yield a ‘profile’ of a particular type of molecule that would allow its robust recognition in a multitude of interactions with a SERS substrate. 120,125,126…”