Microfluidic chip for detection of protozoan parasites

Each year, waterborne diseases have caused approximate 2 million deaths based on World Health Organization around the world. Contaminated water is being consumed by people although much preventions and steps have me introduced. There are some limitations to the conventional techniques like required...

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Bibliographic Details
Main Author: Zainab Che Omar
Other Authors: Liu Aiqun
Format: Final Year Project
Language:English
Published: 2013
Subjects:
Online Access:http://hdl.handle.net/10356/54451
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Institution: Nanyang Technological University
Language: English
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Summary:Each year, waterborne diseases have caused approximate 2 million deaths based on World Health Organization around the world. Contaminated water is being consumed by people although much preventions and steps have me introduced. There are some limitations to the conventional techniques like required professional personal, time consuming and labor-intensive for the detection. Currently, the water industry is looking at rapid Cryptosporidium and Giardia detection system that will prevent more deaths around the world leading to the motivations of this project to create fast and effective microbial detection system. Microfluidics is a great tool that is widely used in various industries. The narrow channels are able to precisely detect microbes individually based on hydrofocusing method. This will accurately identify the pathogen scattering pattern. This project is basically to engineer a real-time detection system which has not been produced in market. It consist the design and fabrication procedures of microfluidic chip using glass that is one of the most used micro fabrication. This report will cover the integration of the detection system for both hardware and software. Principal Component Analysis (PCA) will be introduced in the project to determine the scattering pattern of different pathogens. It is a method that initiated in 1988 by Kirby and Sirovich. This technique is denoted as the use of eigenfaces. Experiments related PCA will be done to find the different distance measures; volume of training images; as well as recognition rate of the system to achieve better optimisation.