Statistical Techniques of Product Optimization

General

• Code: 276-190609
• Semester: 6th
• Study Level: Undergraduate
• Course type: Επιλογής, Ειδικού υποβάθρου
• Teaching and exams language: Greek
• Teaching Methods (Hours/Week): Lectures (2)
• ECTS Units: 3
• Course homepage: https://moodle.teithe.gr/course/view.php?id=607
• Instructors: Petridis Dimitrios

Course Contents

The quality of a product depends on the quality of raw materials, its composition and processing. Optimizing quality, both during the development of new products and when improving what is already produced, requires the study of the effect of the above parameters on its chemical, rheological, organoleptic and other quality characteristics product. This effect is studied in detail through well-organized specific pilot projects and the application of selected statistical analyses. This procedure identifies better manufacturing conditions and the most important characteristics affecting decisively the preference for the product by the consumer.

Summary of contents

• Principles for setting up pilot projects: concept of treatment, pilot units, repeatability and effectiveness of the pilot project.
• Completely randomized designs: synthesis of the plan and ways of its randomization, analysis of variance, comparison of treatments.
• Full factorial designs: fixed, random and mixed, hierarchical. Study and assessment of variation of hierarchical experimental units.
• Complete and incomplete balanced designs: methods to limit the effect of groups, selection and efficiency of balanced designs in incomplete groups.
• Fractional factorial experiments2k-p, 3k-pand mixed: assessing the effects of the most important factors, designs and selection from resolution III, IV and V, advantages of Plackett-Burman and Box-Behnken designs.
• Analysis of the response surface: advantages of composite central designs.
• Analysis of mixture experiments: two-, three- and four-component mixture designs.
• Statistical analysis and description of first, second and specific third-degree models. Contour and trace plots.
• Analysis of repeated measurements: variability within and between groups, specific analysis of variance and relationship between measurements.
• Diagnostic criteria of validity between data and models in experimental designs.
• Selection of the most appropriate pilot projects and application of specific statistical analyses with popular statistical programs (MINITAB, JMP).

Educational Goals

• Acquiring knowledge in specialized statistical analysis techniques related to food design and development.
• Understanding and solidifying all the parameters for evaluating the texture of a product.
• Ability to statistically describe and analyze processing parameters and evaluate each one’s contribution to the final product design.
• Gaining experience from the application of special statistical analyzes in the experimental.
• product design and optimization.
• Possibility of producing a new product by statistically exploiting the optimal composition of the basic components.

General Skills

• Searching, analyzing, interpreting and synthesizing data and information, using the necessary technologies.
• Adjusting in industrial production situations.
• Readiness in decision making.
• Autonomous work.
• Group work.
• Working in an international environment.
• Working in an interdisciplinary environment.
• Developing and spreading innovative ideas.
• Product design and quality management.

Teaching Methods

Face to face:

• Lectures (theory and problems) in the classroom.
• Practical exercises (practice in the statistical software MINITAB by processing data from the food industry).

Use of ICT means

• Presentation with PowerPoint slides using PC and projector.
• Posting course material and communicating with students on the Moodle online platform.
• Use of electronic devices for recording and statistical processing of data.

Teaching Organization

Students Evaluation

Evaluation methods:

• Written final exams including.
• Multiple choice questions.
• Critical thinking questions.
• Problems based on data from the food industry using the statistical software MINITAB.

The course material and the evaluation criteria are presented and analyzed at the beginning of the semester in the classroom but also permanently online.

Recommended Bibliography

1. Drain D (1997). Handbook of experimental methods for process improvement. Chapman & Hall, London, pp. 317.
2. Khuri A.I. & Cornell J.A. (1987). Response surfaces. Marcel Dekker Inc., N. York, pp. 405.
3. Kuehl O.R (2000). Design of experiments: Statistical principles of research design and analysis. 2^Nd edition, Duxbury, London, pp. 666.
4. Mason R.L., Gunst R.F. & Hess J.L. (1989). Statistical design and analysis of experiments. Wiley & Sons, pp. 692.
5. Mead R., Curnow R.N. & Hasted A.M. (1993). Statistical methods in agriculture and experimental biology. 2nd edition. Chapman & Hall, London, pp. 412.
6. Montgomery D.C. (20011). Design and analysis of experiments. 5th edition. Wiley & Sons Inc. N. York, pp. 684.
7. Ο`Mahoney Μ. (1986). Sensory evaluation of food. (Tannenbaum S.T. & Walstra P. Eds). Marcel Dekker Inc. New York, pp 487.
8. Piepel G.F. & Cornell J.A. (1994). Mixture Experiment Approaches: Examples, Discussion, and Recommendations. J. Quality Technology, 26(3):177-196.

Related Research Journals

1. Food Science and Technology International.
2. Journal of Texture Studies.
3. Food Chemistry.
4. Journal of Food Engineering.