Raman spectroscopy offers numerous advantages in bacterial identification, including rich molecular information, quick processing, and great sensitivity. However, accurately identifying bacterial species remains challenging due to the similarity of Raman spectra among various species. This paper introduces a method that combines Transformer networks and Raman spectra for the swift and precise identification of pathogenic bacteria. Our lightweight transformer model, called RamanFormer, outperforms conventional convolutional neural network (CNN) models in identification accuracy and model complexity on the Bacteria-ID dataset and Custom-built dataset. RamanFormer has only about 1/35 and 1/184 of the network parameters compared with CNNs. On the Bacteria-ID dataset, RamanFormer reached a state-of-the-art (SOTA) isolate-level accuracy of 87.03%. We also evaluated the model using clinical bacterial isolates and discovered that it had a SOTA of 99.98% identification accuracy in the 8-antibiotic empiric group task using just ten bacterial spectra per patient isolate. Additionally, RamanFormer also achieved 97.32% identification accuracy on the Custombuilt dataset. Our approach is thus capable of quickly and correctly classifying different bacterial pathogens based on the Raman spectra and could be used for additional Raman spectra identification tasks. The code for RamanFormer will be accessed at https://github.com/Bo-Zhou-gogogo/Raman-transformer.
We described and characterized an experimental system for micro-Raman spectroscopy of individual floating-particle or
living cell trapped by single beam gradient optical trap (optical tweezers). This system combined a micro-Raman
spectroscopy and optical tweezers technique, equipped an IR laser and another visible laser as trapping and Raman
excitation beams, respectively. The Raman spectrum of floating-cell trapped by optical trap in liquid media has the
advantage of eliminating the interference of cover-slips and confining cell Brownian motion. Moreover, using
independent lasers enables optimizing the laser parameters for separately purpose, and modulating the Raman exciting
beam position relative to trapping beam, also it is necessary for Raman imaging. Applying this system the Raman spectra
of single living rat erythrocyte and saccharomyces cells was obtained with high spatial resolution. The results showed
that this approach significantly improved the signal-to-noise ratio of Raman spectra of living cell compared with
conventional way that immobilizes the floating-cell on the surface of cover-slips. This technique would provide a wide
useful approach for the Raman spectroscopy of suspended micro-objects in aqueous solution, especial for single floatingcells.
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