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.
Highlights
Surface-Enhanced Raman Spectroscopy (SERS) is a commonly used sensing technique that shares the advantages of conventional Raman spectroscopy, such as easy sample preparation, molecular fingerprinting, and low signal attenuation by solvents, while improving sensitivity
An optimal preprocessing technique, model training, and evaluation method for the SERS-based Rhodamine 6G (R6G) molecule detection were proposed, and a benchmark dataset was provided to lay the foundation for advanced model construction
The proposed model showed excellent performance on the R6G molecule detection task compared to other machine learning models
Summary
Surface-Enhanced Raman Spectroscopy (SERS) is a commonly used sensing technique that shares the advantages of conventional Raman spectroscopy, such as easy sample preparation, molecular fingerprinting, and low signal attenuation by solvents, while improving sensitivity. The SERS provides greater system design flexibility than Raman spectroscopy, making it suitable for portable applications such as detection of pathogen [2], water pollutant [3,4], and counterfeit [5], etc Despite these successes, it is difficult to identify meaningful patterns in the SERS measurements and this often requires sophisticated signal processing techniques due to the inherent fluctuations and nonlinearities of signals originating from interactions between target molecules and the surface of the SERS device. Some of the recent studies have demonstrated successful biosensing applications in response to the COVID-19 pandemic, such as in the detection of SARS-CoV-2 related proteins or in the detection of the virus itself [9,10] These efforts provide examples of the successful application of machine learning techniques in biosensing. Amjad et al [11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
