<p dir="ltr">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 <i>Bacteroidales</i> as an indicator of fecal contamination on fresh produce farms.</p><p dir="ltr">In pursuit of this, I collaborated with lab members to engineer a Field-Applicable Rapid Microbial Loop-mediated isothermal Amplification Platform, FARM-LAMP. This device is portable (164 x 135 x 193 mm), energy-efficient (operating under 20 W), achieves the target 65°C with ± 0.2°C fluctuations, and is compatible with paper-based biosensors for loop-mediated isothermal amplification (LAMP). Subsequently, I led the fabrication of the microfluidic Field-Applicable Sampling Tool, FAST, designed to deliver high-throughput (10 samples per device), equal flow-splitting of fluids to paper-based biosensors, eliminating the need for a laboratory or extensive training. FARM-LAMP achieved 100% concordance with standard lab-based tests when deployed on a commercial lettuce farm and FAST achieved an average accuracy of 89% in equal flow-splitting and 70% in volume hydration.</p><p dir="ltr">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 sample positivity for fecal contamination, ranging from 0% (no contamination) to nearly 100% (definite contamination) and the concentration of field samples. It utilizes calculus-based mathematics, such as first and second derivative analysis, and incorporates image analysis techniques, including hue, saturation, and value (HSV) binning to a sigmoid function, along with contrast limited adaptive histogram equalization (CLAHE). Additionally, I developed a preliminary graphical user interface in Python that defines a prediction model for the concentration of <i>Bacteroidales</i> based on local weather patterns.</p><p dir="ltr">This thesis encompasses hardware development for on-field quantification and the creation of a preliminary user-interface application to assess fecal contamination risk on fresh produce farms. Integrating these devices with a user-interface application allows for rapid interpretation of results on-farm, aiding in the effective development of strategies to ensure safety in fresh produce operations.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26352133 |
| Date | 23 July 2024 |
| Creators | Ashley Deniz Kayabasi (19194448) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY-NC-SA 4.0 |
| Relation | https://figshare.com/articles/thesis/Devices_for_On-Field_Quantification_of_i_Bacteroidales_i_for_Risk_Assessment_in_Fresh_Produce_Operations/26352133 |
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