SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network

Abstract

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

Detection And Recognition Of Volatile Substances In Mothballs Using Laser-Induced Breakdown Spectroscopy And Raman Spectroscopy

“…19 Park et al evaluated the efficacy of several machine learning models for a molecule detection and classification based on the surface-enhanced Raman spectroscopy benchmark dataset of Rhodamine 6G (R6G). 20 The laws governing the training data can be learnt and used to classify or predict the features of new input data. With the assistance of machine learning, differences in the type and content of elements can be traced based on the wavelength or intensity of the characteristic spectral lines, enabling the identification of the category to which a detected substance belongs.…”

Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy

“…[10][11][12][13][14][15][16][17] Although these studies have mainly focused on building application-specific predictive models, some studies have suggested methods to improve the consistency of predictive performance under independent test conditions. 18,19 In addition, Liu et al 20 proposed a convolutional neural network (CNN)-based unified solution for Raman signal analysis. Park et al 21 also proposed a pseudosiamese network to identify the best matching signal from reference spectra.…”

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy

“…Janani et al [25] proposed an Independent Component Analysis (ICA)-based Brain-Computer Interface (BCI) application of functional near-infrared spectroscopy signals. Park et al [26] proposed a neural network model for detecting R6G molecules. These examples illustrate the successful implementation of machine learning techniques on the spectroscopy dataset.…”