2025 (Current Year) Faculty Courses School of Materials and Chemical Technology Undergraduate major in Materials Science and Engineering
Material Chemometrics
- Academic unit or major
- Undergraduate major in Materials Science and Engineering
- Instructor(s)
- Shun Omagari
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue
- Class
- -
- Course Code
- MAT.P330
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
"Chemometrics" is a field of advanced extraction and analysis of massive data, and optimization of experimental method based on chemical experiments and simulations. In this course, one will learn the basics of chemometrics and be able to apply the skills.
Course description and aims
(1) Understand the concept of ""errors"".
(2) Understand average and standard deviation of various statistical distributions.
(3) Be able to calibrate experimental tools based on correlation and regression.
(4) Be able to use the experimental design method to design experiments, surveys, and simulations.
(5) Be able to use the right multivariate analysis method to analyze and interpret the data from experiments, surveys, and simulations.
Keywords
Chemometrics, Experimental design, Statistics, Data analysis, Multivariate analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Mostly classes and exercises, and will also conduct group works. Exercises will be set for each day. Class: 60 minutes, exercises: 40 minutes.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | [Chemometrics] [Introduction] Errors in analytical experiments [Statistics in Repetition] Statistical Distribution, Sampling and Confidence | Understand the concept and importance of Chemometrics. Learn the concept of errors and how it relates to the experimental design. Furthermore, learn sampling method, its effect on errors and confidence based on various statistical distributions. |
| Class 2 | [Significance Test (1)] Comparison and test in Statistics [Significance Test (2)] Outliers, Variance | Learn the concept of significance in statistics and be able to compare and test statistics. As an example, learn about variance and outliers as well as how to test for them. |
| Class 3 | [Experimental Design and Optimization (1)] Blocking and Latin Square [Experimental Design and Optimization (2)] Interactions and Factorial Design | Learn blocking and Latin square method for designing experiment with various parameters. Additionally, understand the concept of interaction that arises between parameters and the importance of factorial design. |
| Class 4 | [Multivariate Analysis (1)] Introduction [Multivariate Analysis (2): Classification] k-nearest neighbor, discriminant analysis [Multivariate Analysis (3): Classification] Cluster Analysis, Principal Component Analysis | Understand various methods for analyzing multivariate data. Then compare supervised methods like k-nearest neighbor and discriminant analysis, and non-supervised methods like cluster analysis and principal component analysis. |
| Class 5 | [Multivariate Analysis (4): Factor] Factor Analysis [Multivariate Analysis (5): Regression] Regression Analysis | Understand the method to reduce factors in multivariate systems. Also, learn the concept and interpretation of regression. |
| Class 6 | [Calibration (1)] Calibration: Correlation and Regression [Calibration (2)] Regression and Choosing Analytic Method | Understand the validity of applying correlation and regression in correction and calibration in analytical chemistry. Learn to be able to assess the validity of the experiment method based on regression analysis. |
| Class 7 | [Group Work] Discussion [Presentation] | Group work and discussion |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend some time preparing for class and reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Original lecture notes will be distributed online
Reference books, course materials, etc.
James N. Miller，Jane C. Miller "Statistics and Chemometrics for Analytical Chemistry", Pearson Education 2005.
Evaluation methods and criteria
Will be graded based on practice problems of each day, group work, and exams.
Related courses
- Exercise on Information Processing
Prerequisites
Recommended to obtain the credits in the related classes