文章基本信息

标题：SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network
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作者：Seongyong Park ; Jaeseok Lee ; Shujaat Khan 等
期刊名称：Biosensors
电子版ISSN：2079-6374
出版年度：2021
卷号：11
期号：12
DOI：10.3390/bios11120490
语种：English
出版社：MDPI Publishing
摘要：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 ( R6 G) 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 R6 G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.
关键词：Surface Enhanced Raman Spectroscopy; molecule detection; machine learning; deep learning