Doctoral Dissertations

Date of Award

12-2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Biomedical Engineering

Major Professor

Michael K. Danquah

Committee Members

Jacqueline Ann Johnson, Tao Wu, Jeff Reinbolt

Abstract

Although foodborne illnesses are often perceived as preventable, contamination by Staphylococcus aureus (S. aureus) remains a critical public health and food safety challenge. S. aureus produces heat-stable enterotoxins that resist conventional cooking and processing methods, making its detection and control difficult. Traditional laboratory techniques such as culture, enzyme-linked immunosorbent assay (ELISA), or polymerase chain reaction (PCR) are accurate but often slow, expensive, and impractical for on-site food monitoring. Outbreaks may go undetected until it is too late, posing serious health risks and economic losses.

Rapid, accurate, and portable detection methods are needed to bridge this gap. In this work, we developed a novel electrochemical aptasensor for real-time detection of S. aureus in food matrices. Aptamers are short, single-stranded oligonucleotides that bind specific targets with high affinity and are employed as biorecognition elements. Our aptasensor integrates gold nanoparticles (AuNPs) onto screen-printed carbon electrodes (SPCEs) that bind to aptamers specific to the IsdA protein (a key surface biomarker of S. aureus), serving as the biorecognition element and offering high selectivity and sensitivity. The sensor's performance is validated by cyclic voltammetry (CV) and impedance spectroscopy (EIS), while scanning electron microscopy (SEM) and fluorescence imaging confirm its structural and functional properties.

To deepen the understanding of aptamer–target interactions, we performed kinetic and equilibrium studies directly in real food matrices, extracting binding constants, maximum binding capacity, and association/dissociation rates. These analyses revealed the sensor’s quantitative performance under realistic conditions, providing insight into binding behavior and supporting rational biosensor design. To further support field deployment, we introduce a smartphone-compatible system for data acquisition and real-time interpretation, using machine learning algorithms to analyze complex voltammetry signals. We also fabricated a novel electrode system using alternative nanomaterials, such as silver nanoparticles (AgNPs) and titanium dioxide nanoparticles (TiO2NPs), and compared them with AuNPs to improve the aptasensing performance.

This research lays the foundation for a portable, low-cost, high-performance biosensor capable of early detection of S. aureus. This platform can help reduce food recalls, enhance consumer safety, and guide the future development of biosensors targeting a broad range of pathogens across clinical, environmental, and industrial domains.

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