Rapid high-throughput detection and automated counting of coliform bacteria and Escherichia coli using an on-chip imaging system
Abstract
Coliform bacteria (i.e., total coliform, fecal coliform, and Escherichia coli (E.coli)) form an indicator of possible contamination of intestinal parasites and pathogenic organisms in water. Reliable detection of the presence/absence of coliform bacteria and E.coli in water is critical to determine the bacterial quality of water since it is practically difficult to check for every type of disease causing organism. Current methods used to detect and enumerate coliform bacteria and E.coli are based on the incubation of filtered/unfiltered water samples for least 24 hours and analyzing the samples using a benchtop microscope. These methods offer high sensitivity and specificity but are time-consuming, not field portable, and need an expert to operate bulky and expensive optical imaging equipment. In this proposal we will design and develop a lensfree on-chip monitoring platform to image, detect, and count coliform bacteria and E.coli in water samples. This compact and cost-effective platfonn will be field portable and easy to use so that it can be used by minimally trained personnel even in resource limited settings. The performance of the system will be tested with organism-specific cultures and the results will be compared against EPA-approved reference methods. We aim to detect one colony forming unit (CFU) of total coliform or E.coli in-JOO ml of water (i.e., I CFU/IOOmL) in~ 8 hours. The specific objectives of this project can be summarized as follows: (I) Design and development of a field portable lensfree coliform monitoring system, integrated with a custom-made incubator - all weighing less than or equal 5 pounds, excluding the controlling laptop and server back-end. (2) Analysis of the bacterial growth process on a filter membrane to reveal hyperspectral lensfree diffraction properties of each bacterial colony as a function of spectrum and time for sensitive and specific detection, automated classification and counting of colonies using machine learning. (3) Design and development of a graphical user interface to control the entire lensfree coliform monitoring system and related digital analysis. (4) Testing the performance of the winner design and the associated experimental platform using organism-specific cultures, getting hyperspectral maps of each. (5) Testing the ability of the platform to detect chlorine injured E.coli. (6) Comparison of the results obtained from our computational sensing platform with EPA-approved methods and refinement of our detection and classification algorithms and imaging design, as needed. (7) Independent testing and validation the performance of the platform using field samples. We believe that the scientific output of this study can be broadly useful in microbiology field as well as for US Army needs by enabling a field portable, compact and easy to use biomedical imaging platform to rapidly detect and enumerate coliform bacteria and E.coli in water samples.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 07, 2018
- Source ID
- W911NF1710161
Entities
People
- Aydoğan Özcan
Organizations
- Army Contracting Command
- Office of the Secretary of Defense
- University of California, Los Angeles