AI and Data Science in the Food Sector

Learning Results

Upon completion of the course, students will gain the ability to apply statistical and data science techniques to a wide range of topics related to food science, food technology and nutrition.

Skills acquired

• Search, analysis and synthesis of data and information, using the necessary technologies
• Individual work
• Group work
• Work in an multi-disciplinary environment
• Creative and critical thinking

1. FOOD QUALITY (4 hours)

• Applications of Multivariate data analysis (MVDA) in food quality.
• Principal Components Analyses (PCA)
• Partial Least Squares Regression (PLS)
• Applications of Machine learning in food quality
• Support Vector Machine (SVM).

2. FOOD SAFETY (6 hours)
Big Data Applications in Food Safety

• Food Risk assessment
• Application of Quantitative Risk Assessment Methods for Food Quality
• Understanding Uncertainty and Variability in Risk Assessment
• Quantitative Methods for Microbial Risk Assessment in Foods
• Online food safety databases
• Sampling Protocols
• Statistical aspects of sampling for microbiological analyses
• Bayesian network for optimizing sample size

3. FOOD AUTHENTICITY AND ADULTERATION (4 hours)

• Food Adulteration Detection System using machine learning
• Food Adulteration Detection System using vision-based methods
• IoT Based Food Adulteration Detection Methods

4. FOOD AND HUMAN HEALTH (6 hours)

• Nutrigenomics – Nutrimetabolomics
• Application of Genomics & Metabolic Signatures in Nutrition-related Research
• Toxicogenomics
• Principles of Data Mining in Toxicogenomics
• Human Gut Microbiome
• Machine learning for data integration in human gut microbiome

5. In silico METHODOLOGIES in Food Science (4 hours)

• Identification of bioactive compounds for applications in food using in silico screening techniques
• Quantitative structure−activity relationships (QSAR)

6. FOOD LOGISTICS (2 hours)

• Blockchain technology

1. Goyal, K., Kumar, P. & Verma, K. Food Adulteration Detection using Artificial Intelligence: A Systematic Review. Arch Computat Methods Eng. 2022, 29, 397–426 (2022).

2. González-Domínguez, R. Food Authentication: Techniques, Trends and Emerging Approaches. Foods 2020, 9, 346.

3. Li, P., Luo, H., Ji, B. et al. Machine learning for data integration in human gut microbiome. Microb Cell Fact 21, 241 (2022).

4. Gopaiah Talari, Enda Cummins, Cronan McNamara, John O’Brien, State of the art review of Big Data and web-based Decision Support Systems (DSS) for food safety risk assessment with respect to climate change, Trends in Food Science & Technology, (126), 192-204, 2022.

5. Atiya Khan, Amol D. Vibhute, Shankar Mali, C.H. Patil, A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications, Ecological Informatics, Volume 69, 2022.

6. Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos N. Plataniotis, Deep learning and machine vision for food processing: A survey, Current Research in Food Science, Volume 4, 2021, 233-249,

7. Carolien Buvé, Wouter Saeys, Morten Arendt Rasmussen, Bram Neckebroeck, Marc Hendrickx, Tara Grauwet, Ann Van Loey, Application of multivariate data analysis for food quality investigations: An example-based review, Food Research International, 151, 2022,