Thesis subject
Predicting Food Safety Issues Using Artificial Intelligence (MSc)
Ensuring food safety involves monitoring and controlling various risk factors that can lead to contamination, such as pesticides, antibiotics, and toxins. The global nature of food production and distribution further complicates these efforts, as it requires the coordination and regulation of safety standards across diverse environments and supply chains. The utilization of open access data and artificial intelligence (AI) has emerged as a promising approach to better predict and mitigate food safety issues.
Short description
Food safety is a crucial aspect of public health, with contamination and foodborne illnesses posing significant risks worldwide. In recent years, there has been a growing interest in utilizing open access data and artificial intelligence (AI) to predict food safety issues and identify preventive strategies. This MSc project aims to develop AI algorithms that can predict food safety issues (e.g., pesticides, antibiotics, toxins) based on various factors including environmental conditions, supply chain dynamics, and historical contamination data.
Objectives
- Conduct a comprehensive literature review on AI techniques, food safety, and prediction models.
- Develop AI algorithms that can forecast food safety issues based on various environmental, supply chain, and historical contamination factors.
- Identify the most significant factors influencing food safety and their relative importance.
- Develop a systems model that can integrate the various factors influencing food safety, such as temperature fluctuations, supply chain interruptions, and contamination events.
- Provide insights and recommendations on how to improve food safety monitoring and preventive measures.
Tasks
The work in this master thesis entails:
- Literature review:Conduct a review of existing research studies to identify relevant studies on food safety prediction using open access data and AI techniques. This will provide a foundation of knowledge and identify research gaps.
- Data collection and preparation:Identify relevant open access data sources and collect and preprocess the data, including environmental data, supply chain data, and historical contamination records.
- AI Algorithms development:Develop AI algorithms that can forecast food safety issues. Perform feature selection and sensitivity analysis to identify the most significant factors influencing food safety outcomes.
- Results reporting and documentation:Prepare a comprehensive report summarizing the research methodology, results, and conclusions.
Literature
- Liu, Ningjing ; Bouzembrak, Yamine ; Bulk, Leonieke M. van den; Gavai, Anand ; Heuvel, Lukas J. van den; Marvin, Hans J.P. Automated food safety early warning system in the dairy supply chain using machine learning (2022) Food Control 136 .
- HJP Marvin, Y Bouzembrak (2020). A system approach towards prediction of food safety hazards: Impact of climate and agrichemical use on the occurrence of food safety hazards, Agricultural Systems 178, 102760. https://doi.org/10.1016/j.agsy.2019.102760
- Bouzembrak, Yamine ,Marvin, Hans J.P. (2019). Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables : A Bayesian Network approach. Food Control 97, p. 67 - 76. https://doi.org/10.1016/j.foodcont.2018.10.021
Requirements
- Required skills/knowledge: Food and health, Machine Learning, Programming (Python/R).
Key words: Artificial Intelligence, food safety, early warning systems.
Contact person(s)
Dr. Yamine Bouzembrak (yamine.bouzembrak@wur.nl)
Prof. Bedir Tekinerdogan (bedir.tekinerdogan@wur.nl)