GenoPATHX and Machine Learning tools to map the farm-to-fork dynamics of Salmonella serovars: Academia-Industry collaborative approach

This project aims to enhance food safety by studying Salmonella contamination across the poultry supply chain, from farms to processing and distribution. The study will use a new rapid detection tool, GenoPATHX, to map Salmonella presence and types at various stages. Samples, such as air filters and boot swabs from farms and meat products from processing plants, will be collected and analyzed to detect contamination trends. Additionally, an AI-powered system will be developed to integrate GenoPATHX data with processing information, helping predict and reduce Salmonella risks using machine learning. Privacy-focused data-sharing methods will also be explored to encourage collaboration among food producers without exposing sensitive data. The project includes the creation of an AI-enabled biosensor, which will provide fast, on-site detection of multiple pathogens, allowing real-time monitoring and decision-making. Lastly, the initiative will offer valuable learning opportunities for students, training them in advanced food safety technologies and preparing them for careers in food production safety. This comprehensive approach aims to improve food safety practices and reduce the risk of Salmonella outbreaks in poultry products.