Development of AI-powered Rapid Pathogen Detection in Food Microbiology

Thesis Abstract

Foodborne illnesses caused by pathogenic microorganisms remain a significant public health challenge, exacerbated by the limitations of traditional microbiological detection methods which are often labor-intensive, time-consuming, and prone to human error. The urgent need for a rapid, accurate, and scalable detection system has directed research efforts toward integrating advancements in artificial intelligence (AI) with microbiological diagnostics. This study aims to develop and validate an AI-powered platform capable of rapid pathogen detection in food samples, thereby enhancing food safety monitoring and mitigating health risks associated with contaminated consumables. The specific objectives include designing a machine learning model trained on normalized spectroscopic and imaging data, evaluating the model's accuracy in identifying common foodborne pathogens such as Salmonella, Escherichia coli, and Listeria monocytogenes, and comparing its performance against conventional culture-based and molecular techniques. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative model evaluation. The quantitative component involves collecting a diverse sample set of 500 food samples spanning meat, dairy, and produce, sourced from local supermarkets and food processing facilities. Data acquisition employs multispectral imaging and Raman spectroscopy to generate distinctive microbiological signatures. These data are then processed through feature extraction algorithms, including principal component analysis (PCA), followed by training various machine learning classifiers such as support vector machines (SVM), random forests, and convolutional neural networks (CNN). The models are evaluated using metrics including accuracy, precision, recall, and F1-score through 10-fold cross-validation. The qualitative aspect assesses practical deployment feasibility via user-interface usability testing and stakeholder interviews. Expected findings suggest that the AI platform will achieve detection accuracy exceeding 95%, with CNN-based models demonstrating superior performance over traditional classifiers. The system is anticipated to significantly reduce detection time from 48 hours to under one hour, providing real-time feedback for food safety management. Furthermore, the AI platform is expected to exhibit high scalability and adaptability to different food matrices and pathogen types, supported by robust feature extraction techniques. The study hypothesizes a strong positive correlation between spectral-imaging features and microbial presence, substantiated through regression analysis, and anticipates that the integrated AI approach will outperform existing rapid detection methods in both sensitivity and specificity. By pioneering an innovative digital detection system, this research intends to contribute substantially to the microbiology and food safety fields. It offers an evidence-based framework for implementing AI-driven pathogen detection in routine food safety inspections, with implications for reducing outbreak incidents and enhancing regulatory compliance. The study also aims to establish foundational knowledge on the spectral and imaging signatures associated with specific pathogens, creating a reference database for future applications. The main conclusion indicates that AI-powered microbiological diagnostics are capable of revolutionizing food safety protocols, characterized by high accuracy, rapidity, and operational feasibility. It is recommended that food industry stakeholders consider integrating this technology into existing safety management systems and that further research explore the deployment of portable, AI-enabled detection devices in field conditions. Limitations identified include the potential variability in sample quality and the need for extensive training data to ensure robustness across diverse food types. Overall, this research provides a significant leap toward smart, automated microbiological diagnostics, contributing to improved public health outcomes and advancing the intersection of microbiology with digital innovation.