Thesis subject

AI for Food Integrity: Global Commodity Flows and Prices Analysis (MSc)

Food fraud poses significant risks to public health, consumer trust, and economic stability within the food industry, prompting the need for robust regulatory measures and advanced detection technologies to combat this global phenomenon effectively. This MSc project aims to develop an advanced AI-driven system for the detection and anticipation of food safety and fraud risks by analyzing global commodity flows and prices.

Short description

This MSc project aims to develop an advanced AI-driven system for the detection and anticipation of food safety and fraud risks by analyzing global commodity flows and prices. The project will utilize various open access data sources and employ cutting-edge machine learning techniques to improve the accuracy and effectiveness of risk detection.


Objectives

  1. To develop an AI model for analyzing global commodity flows and prices to detect food safety and fraud risks.
  2. To integrate multiple open access data sources and apply advanced AI models for time series analysis and anomaly detection.

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. Example sources include: World Bank data, FAOSTAT, EUROSTAT.
  • AI Algorithms development:Develop AI algorithms (e.g., anomaly detection) that can forecast fraud and food safety issues analyzing global commodity flows and prices.
  • Results reporting and documentation:Prepare a comprehensive report summarizing the research methodology, results, and conclusions.

Literature

  • Marvin, Hans J.P. ; Hoenderdaal, Wouter ; Gavai, Anand K. ; Mu, Wenjuan ; Bulk, Leonieke M. van den; Liu, Ningjing ; Frasso, Gianluca ; Ozen, Neris ; Elliott, Chris ; Manning, Louise ; Bouzembrak, Yamine (2022). Global media as an early warning tool for food fraud; an assessment of MedISys-FF. Food Control 137 .

Requirements

  • Required skills/knowledge: Food and health, Machine Learning, Programming (Python/R)

    Key words: Artificial Intelligence, food quality, food safety and health.

    Contact person(s)

    Yamine Bouzembrak (yamine.bouzembrak@wur.nl)
    Tarek Alskaif (tarek.alskaif@wur.nl)