Coupling enhanced SERS substrates and 1D dilated convolutional neural network: A new model to improve trace detection and identification

Abstract

In this paper, a Raman spectral detection and identification method combined with a portable Raman device is proposed to realize the real-time detection of trace substances. By employing a composite Surface-Enhanced Raman Scattering (SERS) substrate coupled with surface plasmon polaritons (SPP) and localized surface plasmons (LSP), the Raman detection performance for trace can be improved significantly. The one-dimensional dilated convolutional neural network (1D CNN) model proposed in this paper combined with a spatial attention mechanism can accurately identify Raman spectra. Together, a combination of the two provides great advantages, especially for the identification of weak signals with indistinguishable characteristic peaks. At the same time, for the public Raman dataset Rruff, the model has higher accuracy and generalization ability than traditional Raman spectral recognition algorithms. The evidence presented in this paper reveals that the LSP–SPP​ coupled SERS substrate combined with a 1D CNN-based spectral identification algorithm can pave the way for the development of an accurate and portable Raman detection of trace.