BME Master's Defense Announcement for Ashley Kayabasi (M. Verma, advisor)

Everyone is invited to attend the public presentation beginning at 11:00am.

Title: Devices for On-Field Quantification of Bacteroidales for Risk Assessment in Fresh Produce Operations

Exam day details: Friday, July 12^th at 11am in ABE 1164 or through Zoom (https://purdue-edu.zoom.us/j/92130331747)

Committee members: Dr. Mohit Verma (chair), Dr. Leopold Green, and Dr. Haley Oliver

Abstract:

The necessity for on-farm, point-of-need (PON) nucleic acid amplification tests (NAATs) arises from the prolonged turnaround times and high costs associated with traditional laboratory equipment. This thesis aims to address these challenges by developing devices and a user-interface application designed for the efficient, accurate, and rapid detection of Bacteroidales as an indicator of fecal contamination on fresh produce farms.

Initially, I collaborated with others to engineer a field-applicable heater-imager platform compatible with paper-based biosensors using loop-mediated isothermal amplification (LAMP). This compact, energy-efficient device is user-friendly and capable of real-time heat generation. Subsequently, I led the fabrication of a microfluidic sample processing tool suitable for field applications. This tool enables high-throughput fluid delivery to paper-based biosensors without necessitating a laboratory or extensive training. Both devices underwent rigorous testing through a series of in-lab or on-field experiments to either validate their efficacy in detecting fecal contamination on fresh produce farms or their ability to simplify experimental procedures with an on-farm application.

A crucial aspect of device development is ensuring that results are easily interpretable by users. To this end, I developed a Python-based image analysis codebase to quantify the percent positivity of samples for fecal contamination. This program also generates an equation to quantify the concentration of field samples, utilizing calculus-based mathematics combined with image analysis factors. Additionally, I developed a graphical user interface in Python that defines a prediction model for the concentration of Bacteroidales based on local weather patterns and conditions.

This thesis encompasses the development of hardware devices for on-field quantification and the creation of a user-interface application for assessing the risk of fecal contamination on fresh produce farms. The integration of these devices with a user-interface application facilitates the acquisition of rapid results directly on the farm. This capability enables the interpretation of results and the development of effective strategies to ensure safety in fresh produce operations.