Grantee Research Project Results

The Polymerase Chain Reaction (PCR) technique has been used as the gold standard of amplifying target DNAs to detect microorganisms or viruses from environmental water samples. PCR requires a power-consuming thermocycler, a set of perishable chemical reagents, standard wet lab supplies, and benchtop imaging and screening equipment. Following a 10-hour culturing process, the sample preparation, thermocycling, and imaging procedures of a few PCR trials take at least 3 hours in a lab by trained personnel.

Here, we propose a new method that uses Raman spectroscopy and deep learning to identify the bacteria genotypes. The principle behind the technology is the Raman effect, where a sample exposed to a laser will scatter light at a frequency different from that of the incident light (these are known as vibrational signatures). The process of generating spectra is considerably simpler and faster than standard PCR and allows for higher throughput, fewer preparation materials (which risk error), and non-specialists to gather high-quality data. A deep learning model is trained by thousands of documented bacteria samples in advance and is able to identify new or unknown bacteria samples from environmental water samples. The turnaround time of the post-culturing procedure of a single Raman test is only 1-2 minutes compare to the counterpart PCR trial which takes at least 3 hours. Figure 1 shows the Raman spectroscopy and deep learning model for the detection of waterborne bacteria from environmental water samples.