Doctor of Philosophy (PhD)
School of Electrical Engineering & Computer Science
Real-time monitoring of phytoplankton groups provides important information about aquatic ecological states, nutrient abundance, and water pollution. A rapid and accurate method for monitoring phytoplankton in water is commonly performed by detecting fluorescence emission from the plankton; however, commercially available portable fluorescence sensors are still expensive, bulky, and limited in functions, such as lacking the capability of selectively detecting multiple phytoplankton groups. In this regard, a low-cost and portable fluorometer platform for phytoplankton detection was developed in order to address the issues that current portable fluorometers have.
This dissertation has four main goals: (1) perform a study on fluorescence measurement principles and a comprehensive review on portable fluorometers for phytoplankton detection; (2) characterize different phytoplankton groups and their photopigments; (3) develop and integrate necessary mechanical and electronical parts for the portable fluorometer system; and (4) demonstrate/validate the developed device.
The target analytes, including photopigments (chlorophyll a and phycocyanin) and different phytoplankton groups (green and blue algae), were fully characterized with a benchtop fluorometer (Horiba, Japan) using excitation-emission matrix (EEM) fluorescence spectroscopy. The fluorescence sensor utilized three different wavelengths of light emitting diodes (LEDs) for selective stimulation in order to concurrently measure and distinguish green and cyanobacteria samples. As a demonstration, the system was also tested on-site in order to validate the field deplorability of the system using an environmental water sample collected directly from a lake. The results suggest that our developed fluorometer could be used as a portable phytoplankton monitoring system for concurrent detection of different phytoplankton groups.
Shin, YoungHo, "LED-Based Optical Sensing Platforms for Multi-Analyte Detection" (2021). LSU Doctoral Dissertations. 5508.
Available for download on Tuesday, March 15, 2022