Grantee Research Project Results
PCR-Free Environmental Waterborne Bacteria Detection Using Raman Spectroscopy and Deep Learning
EPA Grant Number: SU840575Title: PCR-Free Environmental Waterborne Bacteria Detection Using Raman Spectroscopy and Deep Learning
Investigators: Li, Yiyan
Institution: Fort Lewis College
EPA Project Officer: Brooks, Donald
Phase: I
Project Period: August 1, 2023 through July 31, 2024
Project Amount: $25,000
RFA: 19th Annual P3 Awards: A National Student Design Competition Focusing on People, Prosperity and the Planet Request for Applications (RFA) (2022) RFA Text | Recipients Lists
Research Category: P3 Awards
Description:
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.
Figure 1. Raman spectroscopy and deep learning model for the detection of waterborne bacteria from environmental water samples.
Objective:
The objectives of this proposed project are: 1) Develop a deep learning model and a Raman spectroscopy workstation to rapidly detect environmental waterborne bacteria in the lab. 2) Use the Raman station to test the water samples of Animas River in Colorado. 3) Educate communities (including the Native American tribes) in the four-corner area on the importance of water resource protection and the technologies for environmental protection.
Expected Results:
The results of this study are: (1) Deliver a PCR-free environmental waterborne bacteria detection workstation using Raman spectroscopy and deep learning to our local non-profit environmental study partners for testing. (2) The water quality data from Animas River in the Durango area will be collected using the Raman workstation prototype. (3) The equipment design and the water quality data will be published in journal papers and conference proceedings.
Supplemental Keywords:
PCR-free, Raman spectroscopy, Water-borne bacteria detection
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.