Information Package / Course Catalogue
Artificial Intelligence-Based Quality Control Systems II
Course Code: ST408
Course Type: Required
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 4
Objectives of the Course

The aim of this course is to enable students to apply artificial intelligence-based technologies in the quality control of milk and dairy products. Students will learn modern AI approaches for monitoring, classification, prediction, and optimization of quality parameters in dairy processing. The course also aims to develop skills in applying computer vision, sensor technologies, machine learning, and big data analytics to solve quality control problems in the dairy industry.

Course Content

This course focuses on advanced artificial intelligence applications for quality control in milk and dairy products. Topics include computer vision for defect detection, sensor data analysis, prediction of microbiological and physicochemical quality, shelf-life modelling, intelligent manufacturing systems, and digital quality management. Current scientific studies and industrial case studies are discussed to demonstrate the practical implementation of AI technologies in dairy processing.

Name of Lecturer(s)
Learning Outcomes
1.Explains the principles of quality control in milk and dairy products.
2.Evaluates quality control analyses of milk and dairy products.
3.Explains and applies AI-based quality control methods.
4.Analyzes quality data obtained using artificial intelligence.
5.Evaluates artificial intelligence applications in the dairy industry.
Recommended or Required Reading
1.1. Oysun,G.,1991, Süt ve mamulleri analiz yöntemleri, E.Ü.Z.F. Yay. No:504 Bornova,230s.
2.2. Metin, M., Öztürk, G.F., 2002. Süt ve Mamulleri Analiz Yöntemleri. E.Ü.Ege Meslek Yüksekokulu Yayınları No: 24
3.3. Yaygın,H.,Gönç,S.,Oktar,E., Kılıç.S., 1985, Süt ve mamulleri muayene ve analiz yöntemleri, E.Ü.Z.F. Teksir No:211 Bornova İzmir.
4.4. Yöney,Z.,1973,Süt ve mamulleri muayene ve analiz metodları, A.Ü. Ziraat Fak. Yay. No:491 Ankara,182s 5. Kurt,A.,Songül,Ç.,Çağlar,A.,1999, Süt ve mamulleri muayene ve analiz metotları rehberi, A.Ü. Ziraat Fak. Yay. No:18, Erzurum,238s.
5.Walstra, P., Wouters, J. T. M., & Geurts, T. J. (2006). Dairy Science and Technology (2nd ed.). CRC Press.
6.Sun, D.-W. (Ed.). (2023). Computer Vision Technology for Food Quality Evaluation (3rd ed.). Academic Press.
Weekly Detailed Course Contents
Week 1 - Theoretical
AI-supported quality control of raw milk: Determination of physicochemical properties, microbiological quality, and adulteration (e.g., added water and neutralizers) using artificial intelligence techniques.
Week 2 - Theoretical
Quality control of drinking milk: Verification of pasteurization and UHT processing, evaluation of homogenization efficiency, and prediction of quality parameters using artificial intelligence.
Week 3 - Theoretical
AI-supported quality control of yoghurt: Monitoring fermentation and evaluating pH, acidity, viscosity, syneresis, and textural properties.
Week 4 - Theoretical
Quality control of cheese: Monitoring ripening, aroma development, texture, color, salt, moisture, and defects using computer vision and machilearning.
Week 5 - Theoretical
Quality control of butter: AI-based analysis of fat composition, oxidative stability, color, texture, and quality defects. (Quiz)
Week 6 - Theoretical
Quality control of ice cream: Artificial intelligence applications for predicting viscosity, overrun, melting properties, texture, and sensory qualiy.
Week 7 - Theoretical
Microbiological quality control of dairy products: AI-assisted rapid pathogen detection, image analysis, biosensors, and predictive microbiological models.
Week 8 - Theoretical
AI-supported quality control applications in the dairy industry: Recent scientific studies, industrial applications, case studies, and student presentations. (Midterm Exam)
Week 9 - Theoretical
Overview of AI-supported quality control systems and industrial applications in milk and dairy products
Week 10 - Theoretical
Detection of quality defects in dairy products using computer vision and image processing (cheese, yoghurt, butter, and ice cream)
Week 11 - Theoretical
Artificial intelligence applications using sensor technologies, electronic nose (E-nose), electronic tongue (E-tongue), and spectroscopic techniques
Week 12 - Theoretical
Machine learning applications for predicting microbiological and physicochemical quality of milk and dairy products.
Week 13 - Theoretical
Artificial intelligence for shelf-life prediction, modelling quality changes, and optimization of dairy processing
Week 14 - Theoretical
Current research, case studies, and student project presentations on artificial intelligence applications in the dairy industry.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Lecture - Practice141128
Quiz1224
Midterm Examination1426
Final Examination1426
TOTAL WORKLOAD (hours)100
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
4
5
4
OÇ-2
4
5
4
OÇ-3
4
5
4
OÇ-4
4
5
4
OÇ-5
4
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026