Information Package / Course Catalogue
Artificial Intelligence and Machine Learning Applications in Food Safety
Course Code: ST319
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The aim of this course is to introduce students to the potential use of artificial intelligence and machine learning techniques in the field of food safety; to teach data-based solution approaches for early detection, control and prevention of food-borne risks. It is aimed that students will gain the competence to develop decision support systems in food safety processes by understanding the advantages and limitations of different algorithms.

Course Content

This course examines the application of artificial intelligence (AI) and machine learning (ML) techniques in the field of food safety. Focus is on topics such as food-borne risk detection, traceability systems, quality control processes, and contaminant prediction. Students will learn how to select appropriate algorithms for food safety data analysis, apply basic modeling methods, and develop AI-powered decision systems. Innovative AI-based solutions for topics such as HACCP, food traceability, and contamination prevention will also be discussed. The course is supported by real-world examples and case studies.

Name of Lecturer(s)
Learning Outcomes
1.Identifies the main risks and control points related to food safety.
2.Explains the concepts of artificial intelligence and machine learning.
3.Suggests solutions to food safety problems using AI algorithms.
4.Evaluates the integration of AI with HACCP and traceability systems.
5.Discusses the potential of AI-based early warning and prediction systems.
Recommended or Required Reading
1.Artificial Intelligence in Food Safety: Concepts and Applications” Yazar: Joseph Jwu-Shan Jen, Wiley, 2022
2.Machine Learning and Data Science in the Food Industry” Yazar: Robert D. Brown, CRC Press, 2020
Weekly Detailed Course Contents
Week 1 - Theoretical
Food safety concept and basic principles
Week 2 - Theoretical
Introduction to artificial intelligence and machine learning
Week 3 - Theoretical
Types and sources of data used in food safety
Week 4 - Theoretical
Classification of food-borne hazards and risk prediction with AI
Week 5 - Theoretical
AI applications in control and traceability systems
Week 6 - Theoretical
Contaminant detection with image processing and sensor data
Week 7 - Theoretical
Classification algorithms (SVM, k-NN, Random Forest)
Week 8 - Theoretical
Regression and prediction models (Logistic Regression, ANN)
Week 9 - Theoretical
Deep learning and use cases in food safety
Week 10 - Theoretical
Case study: Microbial contamination prediction with AI
Week 11 - Theoretical
Case study: Traceability algorithms in the food chain
Week 12 - Theoretical
AI integration into HACCP systems
Week 13 - Theoretical
AI-based early warning systems
Week 14 - Theoretical
General evaluation and project presentations
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Individual Work141014
Midterm Examination1415
Final Examination1415
TOTAL WORKLOAD (hours)52
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
5
5
4
4
4
5
4
OÇ-2
5
5
5
4
4
4
OÇ-3
5
5
5
4
4
5
4
OÇ-4
4
4
4
4
4
4
4
5
OÇ-5
4
4
4
5
4
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026