Information Package / Course Catalogue
Decision Support Systems in Dairy Technology With Fuzzy Logic
Course Code: ST426
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 fuzzy logic methods to support decision-making in uncertain processes encountered in the field of dairy technology and to teach the application of these methods in areas such as dairy product quality control, process optimization and production decisions.

Course Content

Definition and importance of decision support systems Basic concepts of fuzzy logic Fuzzy sets, membership functions and rule-based systems Evaluation of quality parameters in dairy products Creation of fuzzy logic models Applications for process optimization in dairy technology Fuzzy modeling of sensory analysis results Simple modeling studies with software tools (Matlab, Python, etc.) Application examples with real data sets Literature review and project presentation

Name of Lecturer(s)
Learning Outcomes
1.Explains the basic concepts of fuzzy logic.
2.Defines problems involving uncertainty in dairy technology applications.
3.Analyzes how decision support systems can be used in dairy production processes.
4.Creates membership functions and rules.
5.Develops simple fuzzy models based on real data.
6.Performs sensory analysis and quality control applications with fuzzy logic.
7.Reports the design and implementation process of a decision support system.
Recommended or Required Reading
1.Fuzzy Logic with Engineering Applications – Timothy J. Ross
2.Fuzzy Logic in Action: Applications in Epidemiology and Beyond – P. P. Wang
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to decision support systems and their use in dairy technology
Week 2 - Theoretical
Introduction to fuzzy logic: Basic concepts
Week 3 - Theoretical
Fuzzy sets and membership functions
Week 4 - Theoretical
Rule-based systems and inference mechanisms
Week 5 - Theoretical
Quality assessment parameters in dairy products
Week 6 - Theoretical
Quality estimation with fuzzy models
Week 7 - Theoretical
Use of fuzzy logic in process control
Week 8 - Theoretical
Fuzzy modeling of sensory analysis data
Week 9 - Theoretical
Introduction to fuzzy logic software (MATLAB, Python)
Week 10 - Theoretical
Practical example: Yogurt viscosity decision support system
Week 11 - Theoretical
Practical example: Modeling of cheese ripening process
Week 12 - Theoretical
Student project presentations (preliminary)
Week 13 - Theoretical
Literature evaluation and discussion
Week 14 - Theoretical
Final project presentations and evaluation
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Project70214
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
5
5
4
5
4
OÇ-2
5
5
5
5
4
5
4
OÇ-3
5
5
5
5
4
5
4
OÇ-4
5
5
5
5
4
5
4
OÇ-5
5
5
5
5
4
5
4
OÇ-6
5
5
5
5
4
5
4
OÇ-7
5
5
5
5
4
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026