617-3015/01 – Chemometry (ChM)
| Gurantor department | Department of Chemistry | Credits | 6 |
| Subject guarantor | prof. Ing. Petr Praus, Ph.D. | Subject version guarantor | prof. Ing. Petr Praus, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FMT | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Knowledge of the basic chemometrics’ terms, validation parameters and methods of statistical treatment of experimental data. Practical utilization of the available software for chemometrics calculations and for processing of measured data.
Teaching methods
Lectures
Experimental work in labs
Other activities
Summary
The aim of the lectures is to acquaint the students with the issue of the probability, statistics and data processing. Students will study how to use the validation procedures and how to evaluate correctly the measured data. The focus of the practical exercises is on the training of the computer software for chemometric calculations and other applications. Another aim of the practical courses is to help the students with the processing of their own data measured within diploma thesis and the treatment of the data obtained during other research activities.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Credit test and oral exam.
E-learning
Other requirements
1. Participation in seminars - 90%
2. Submission of a semester work (MS Word)
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Definition of the chemometry. Theory of the probability and its definition. Conditional probability.
2. Random variable. Definition of random variable. Discrete and continuous random variable. Histogram, histogram design. Polygon.
3. Theory of errors. Classification of the errors. Propagation of the errors. Precision and accuracy.
4. Analysis of one-dimensional data. Types of probability distributions.
5. Moment characteristics of one-dimensional data. Shape, position and variability. Quantile and robust characteristics.
6. Verification of the independence, normality and homogeneity of the data set.
7. Analysis of small data sets. Horn's method.
8. Statistical tests. Testing of the results; testing of the accuracy of the average value; conformity testing of two average values; testing of the conformity of two standard deviation values.
9. Analysis of variance, ANOVA. One- way ANOVA. Two- way ANOVA.
10. Multidimensional analysis: factor analysis, principle component analysis, discriminatory analysis, cluster analysis.
11. Validation. Definition of the validation, validation process, types of validation, validation parameters (accuracy of the method, repeatability, output error, deviation, outliers identification, confidence interval, determination of the uncertainties, recovery, robustness of analytical procedure.
12. Calibration. Calibration procedure; linearity - estimation of the correlation coefficient; linear equation; linear regression parameters - standard deviation; parameter regression equation test; limits of the detection;
13. Nonlinear calibration. Standard addition method.
14. Linear regression. Effective data (outliers observations, extremes).
Exercice
1. Introductory exercise, probability calculation.
2. Histogram, histogram design. Polygon. (Using MS-Excel and QC-Expert software.)
3. Analysis of one-dimensional data. Types of probability distributions. Moment characteristics of one-dimensional data. Shape, position and variability. Quantile and robust characteristics. (Using MS-Excel and QC-Expert software.)
4. Verification of the independence, normality and homogeneity of the data set. (Using MS-Excel and QC-Expert software.)
5. Analysis of small data sets. Horn's method. (By computing and using QC-Expert software.)
6. Assignment and processing of 1. credit work.
7. Statistical tests. Testing of the results; testing of the accuracy of the average value; conformity testing of two average values; testing of the conformity of two standard deviation values.
8. Analysis of variance, ANOVA. One- way ANOVA. Two- way ANOVA.
9. Multidimensional analysis: factor analysis, principle component method, discriminatory analysis, cluster analysis.
10. Validation. Validation parameters (accuracy of the method, repeatability, output error, deviation, outliers identification, confidence interval, determination of the uncertainties, recovery, robustness of analytical procedure.)
11. Calibration. Calibration procedure; linearity - estimation of the correlation coefficient; linear equation; linear regression parameters - standard deviation; parameter regression equation test; limits of the detection.
12. Linear regression. Effective data (outliers observations, extremes).
13. Assignment and processing of 2. credit work. (Using MS-Excel and QC-Expert software.)
14. Control of credit works, credit.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction