Food Engineering Experimental Design
Course Code
PG4013
Number of Credits
3
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Statistical Applications in Experiments | Express |
| Single-factor experiments & ANOVA concepts | Expert |
| Single-factor experiments with blocks | Expert |
| Factorial experiment design | Expert |
| Full factorial 2-level experiments | Expert |
| Fractional factorial experiments | Expert |
| Response surface design | Expert |
| Mixture experiment design | Expert |
| Experimental strategies | Expert |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Able to analyze measurement data using methods of descriptive statistics, and inferential statistics for one- & two-samples |
| CPMK 2 | Able to analyze 1-factor experimental data for fully randomized and RCBD experiments by using computer. |
| CPMK 3 | Able to construct and analyze factorial experiment data using computer, which includes several design variations, such as single replicate, center point runs, and others. |
| CPMK 4 | Able to construct and analyze fractional factorial experiment data using computer, which includes variations in the design and data analysis techniques. |
| CPMK 5 | Able to construct and analyze response surface method experimental results using computer, which includes CCD and Box-Behnken designs. |
| CPMK 6 | Able to construct and analyze mixture experimental data using computer, including simplex, simplex-centroid, and extreme vertices designs. |
| CPMK 7 | Able to discuss strategies for progressive experimentation and exploration of experimental domain for optimization. |
Learning Method
- Cooperative Learning, Problem-Based Learning, Group Discussions
Learning Modality
- Offline
Assessment Methods
- Exam, Assignment, Quiz