Design of Experiments for Process and Product Optimization
A.Y. 2026/2027
Learning objectives
The proper design of experiments is an essential part of any scientific investigation. As such, the planning of the trials and the analysis of the results are integral to the food industry and food science. This course will cover all stages of the experimental method, from identifying the problem and defining the objectives, to gathering information and designing the experiment for data collection and analysis. Tools for discussing the results will be outlined, considering bibliographic search engines and representation of results in tables and figures. The course encompasses the use of various softwares for statistical inference, experimental design, and scientific search engines. The programme includes theoretical lessons, practical sessions in the computer lab, and discussion and resolution of case studies proposed by the instructor and/or the students. In the short term, the course aims to provide tools for conducting and writing an experimental thesis, as well as for solving industrial problems in the field of research and development.
Expected learning outcomes
At the end of the course, the students will be able to apply the experimental method approach, as well as to use and implement methods for optimizing processes, products, and formulations to enhance process robustness and sustainability.
· Summarize the basic and advanced design of experimental methods
· Perform statistical inference
· Demonstrate knowledge of basic and advanced process optimization methods
· Use factorial (fractional) designs and response surface methods through dedicated software
· Analyze the data and present the most important results
· Discuss the results, outline conclusions, and provide recommendations
· Summarize the basic and advanced design of experimental methods
· Perform statistical inference
· Demonstrate knowledge of basic and advanced process optimization methods
· Use factorial (fractional) designs and response surface methods through dedicated software
· Analyze the data and present the most important results
· Discuss the results, outline conclusions, and provide recommendations
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
1. Principles, purposes, and procedures of the experimental method. Definition of objectives and factors of the process, bibliographic search engines, tools for data collection and analysis and methods for the representation of the results in tables and figures during and after the trials. Tools for discussing the results.
2. Analysis of variance (ANOVA).
3. Simple and Multiple Linear Regression.
4. Full and Fractional factorial designs at two levels. Methods for improving the resolution of fractional designs. The Plackett-Burman design. Adding central points to factorial designs.
5. Response Surface Designs: Central Composite Design (CCD), Face-Centered Design (FCD), and Box-Behnken Design (BBD).
6. Optimization procedures for one or more response variables.
7. Mixture optimization: Simplex Lattice Design.
- Computer-based exercises in the IT lab with practical examples of data collection and analysis, process/product modeling and optimization using Excel and several statistical softwares.
2. Analysis of variance (ANOVA).
3. Simple and Multiple Linear Regression.
4. Full and Fractional factorial designs at two levels. Methods for improving the resolution of fractional designs. The Plackett-Burman design. Adding central points to factorial designs.
5. Response Surface Designs: Central Composite Design (CCD), Face-Centered Design (FCD), and Box-Behnken Design (BBD).
6. Optimization procedures for one or more response variables.
7. Mixture optimization: Simplex Lattice Design.
- Computer-based exercises in the IT lab with practical examples of data collection and analysis, process/product modeling and optimization using Excel and several statistical softwares.
Prerequisites for admission
Some basic concepts of statistics.
Teaching methods
The course will be delivered through:
Lectures (3 CFU) with IT support (PowerPoint presentations, bibliographic search engines, Excel spreadsheets, statistical softwares) aimed at transferring theoretical knowledge for understanding and applying the explained methods. The students are required to bring their laptops.
Computer Lab Exercises (1 CFU) aimed at gaining familiarity with problem-solving.
Lectures (3 CFU) with IT support (PowerPoint presentations, bibliographic search engines, Excel spreadsheets, statistical softwares) aimed at transferring theoretical knowledge for understanding and applying the explained methods. The students are required to bring their laptops.
Computer Lab Exercises (1 CFU) aimed at gaining familiarity with problem-solving.
Teaching Resources
- The slides presented during lectures, accompanied by explanatory text, exercises with solutions, and examples of practical exams are available on the myARIEL portal.
- " Design and Analysis of Experiments", Douglas Montgomery, McGraw-Hill.
- Response Surface Methodology. Process and product optimization using design experiments. Wiley series in probability and statistics. Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook
- "Formulation Simplified: Finding the Sweet Spot through Design and Analysis of Experiments with Mixtures", M.J. Anderson, P.J. Whitcomb, M.A. Bezener
- " Design and Analysis of Experiments", Douglas Montgomery, McGraw-Hill.
- Response Surface Methodology. Process and product optimization using design experiments. Wiley series in probability and statistics. Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook
- "Formulation Simplified: Finding the Sweet Spot through Design and Analysis of Experiments with Mixtures", M.J. Anderson, P.J. Whitcomb, M.A. Bezener
Assessment methods and Criteria
The exam is written (duration: 120 minutes) and consists of an assessment of theoretical knowledge and the solution of a problem on the computer using statistical software.
Only for those attending (> 75% total lessons), it is possible to replace the exam with the preparation of a thesis in which the student presents a case study. Details for writing this paper will be provided during the lessons.
The final grade, expressed out of thirty, will be communicated by email. There will be six exam sessions per year: two during each break between semesters and one during the instructional break within each semester. Additional sessions may be organized upon student request.
Students with Specific Learning Disabilities (SLD) or disabilities are requested to contact the instructor by email at least 15 days before the scheduled exam date to arrange individualized accommodations. The email addressed to the instructor must be also forwarded as CC to the respective University Services: [email protected] (for students with SLD) and [email protected] (for students with disabilities).
Only for those attending (> 75% total lessons), it is possible to replace the exam with the preparation of a thesis in which the student presents a case study. Details for writing this paper will be provided during the lessons.
The final grade, expressed out of thirty, will be communicated by email. There will be six exam sessions per year: two during each break between semesters and one during the instructional break within each semester. Additional sessions may be organized upon student request.
Students with Specific Learning Disabilities (SLD) or disabilities are requested to contact the instructor by email at least 15 days before the scheduled exam date to arrange individualized accommodations. The email addressed to the instructor must be also forwarded as CC to the respective University Services: [email protected] (for students with SLD) and [email protected] (for students with disabilities).
AGRI-07/A - Food Science and Technology - University credits: 4
Computer classroom exercises : 16 hours
Lessons: 24 hours
Lessons: 24 hours
Professor:
Hidalgo Vidal Alyssa Mariel
Shifts:
Turno
Professor:
Hidalgo Vidal Alyssa MarielProfessor(s)
Reception:
By date