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.
Keywords: Surface Enhanced Raman Spectroscopy; molecule detection; machine learning; deep learning
This repository provide ML models used in SERSNet study, and also provide real SERS measurement for the reproducibility of the results. If you found this repository useful or use our data in your studies, please cite the following papers
Citation:
[1] Seongyong Park, Jaeseok Lee, Shujaat Khan, Abdul Wahab, and Minseok Kim (2021) "SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network." Biosensors. 11:12, 490, 2021. https://www.mdpi.com/2079-6374/11/12/490
[2] Seongyong Park, Jaeseok Lee, Shujaat Khan, Abdul Wahab, and Minseok Kim (2022) "Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy" Sensors 22, no. 2: 596. https://www.mdpi.com/2079-6374/11/12/490