Raman spectral classification algorithm of cephalosporin based on VGGNeXt

Yang, Siwei; Xie, Yuhao; Liu, Jiazhen; Zhao, Shuai; Jin, Shangzhong; De, Zhang; Chen, Qiang; Huang, Jie; Liang, Pei

doi:10.1039/d2an01355j

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…We call this process Spectral Recognition, in analogy with Facial Recognition (Figure ). Past efforts have largely focused on applying spectral recognition algorithms to match unknown Raman spectra with Raman libraries (i.e., matching Raman to Raman or SERS to SERS) − and have frequently employed various similarity metrics, such as Pearson’s correlation, Euclidean distance, cosine similarity, and other methods. − These approaches are poorly suited for dealing with different spectroscopic variants such as Raman and SERS. These metrics tend to be sensitive to nuisance variations such as noise and uncharacteristic background peaks, which can be induced by substrate type and molecular adsorbate conformation.…”

Section: Introductionmentioning

confidence: 99%

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

Ju,

Neumann,

Bajomo

et al. 2023

ACS Nano

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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.

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