SBIR/STTR Award attributes
There is a dire need in multiple market segments for technology to rapidly and accurately detect and quantify Escherichia coli and Vibrio parahaemolyticus and total fecal coliforms. We propose to develop a novel approach to Surface Enhanced Raman Spectroscopy (SERS) for detecting human pathogens in seafood reared and harvested from aquaculture farms, at point-of-sale in seafood markets, and in the environment. Our approach uses a higher energy laser wavelength (422 and 532nm) compared with the lower energy of more the commonly used laser at 785nm, a novel mixture and preparation of Ag-coated Nano Particles (AgNPs), a fiber optic probe that may be used directly on seafood tissue and environmental samples, and the use of barcoding the Raman spectra with Deep Learning classification through a Convolutional Deep Learning Neural Network (CDLNN). Taken together, the novel microfluidic instrument will allow hand held or in situ identification and quantification of pathogen species and their dominant strains. Real-time detection of E. coli and other fecal coliforms from buoys or the end of docks would allow for immediate mapping of contamination source and early response by environmental managers.
