SERSNet: Surface-enhanced raman spectroscopy based biomolecule detection using deep neural network
Park S. Lee J. Khan S. Wahab A. Kim M.
December 2021MDPI
Biosensors
2021#11Issue 12
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.
Deep learning , Machine learning , Molecule detection , Surface Enhanced Raman Spectroscopy
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Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi, 39177, South Korea
Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, South Korea
Department of Mathematics, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Department of Bio and Brain Engineering
Department of Mechanical System Engineering
Department of Aeronautics
Department of Mathematics
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