Deep learning-based cross-device standardization of surface-enhanced Raman spectroscopy for enhanced bacterial recognition
Year: 2025
Type: article
Abstract: to over 98 % across devices, while SuperRaman achieved up to 100 % classification accuracy post-transformation. Compared to existing approaches, SERS-D2DNet delivered the lowest MAE (0.024 to 0.034), while SuperRaman surpassed state-of-the-art classifiers. The combined framework requires only 6.6 million parameters, a compact 9 MB footprint, and a 3.27 ms inference time, making it well-suited for portable deployment. This study establishes a scalable, real-time solution for rapid sepsis detection and pathogen identification, bridging the performance gap between portable and laboratory-grade SERS systems.
Authors: Sakib Mahmud, Faizul Rakib Sayem, Manal Hassan, Yanjun Yang, Muhammad E.H. Chowdhury, Susu M. Zughaier, Faycal Bensaali, Yiping Zhao
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,750
Award: G4359612149
OpenAlex ID: W4414412873