Discrimination of Closely Related Vibrio Strains by Label-Free Surface-Enhanced Raman Spectroscopy (SERS)
TL;DR
A laser-based analytical technique, surface-enhanced Raman spectroscopy, was used to obtain distinct biochemical fingerprints from the bacterial cells, while computational pattern-recognition methods used to analyze, compare, and classify the resulting based on the spectral signals.
Abstract
Simple Summary Bacterial diseases are among the major challenges in sustainable aquaculture production, which can spread rapidly, as well as cause fish mortality and major losses in fish production. A problem in current diagnostic methods is slow, expensive, and sometimes unable to clearly differentiate between closely related bacterial species, which delays in disease control and prevention. Therefore, this study aimed to develop a faster, low-cost, and more reliable method for identifying fish pathogens commonly found in marine aquaculture systems. A laser-based analytical technique, surface-enhanced Raman spectroscopy, was used to obtain distinct biochemical fingerprints from the bacterial cells, while computational pattern-recognition methods were used to analyze, compare, and classify the resulting based on the spectral signals. The high classification performance was demonstrated using cultured bacterial samples under controlled laboratory conditions, suggesting the potential of this approach as a proof-of-concept method for bacterial identification. Overall, this study demonstrates the potential of this combined approach as a rapid and accurate method for bacterial classification under controlled laboratory conditions and supports its future development for aquaculture disease management.