Main Field of Study and progress level:
Chemistry: First cycle, in-depth level of the course cannot be classified
Grading scale: TH teknisk betygsskala
Responsible department: Department of Chemistry
Contents
Introduction to the chemometric concept: In this part the students are given an overview of the chemometric concept and how chemometrics works as a common theme in, i) definition of aims, ii) planning of experiments, iii) generation of information rich data, iv) modeling and evaluation, v) visualization of large data sets and vi) validation and predictions.
The basics of (chemical) data analysis: In this part focus is on the model concept and variability, and how variability can be utilized in data analysis.
Experimental design: This part highlights how experimental design can be used to make data contain information, how these data can be analyzed and evaluated, and how this philosophy can be used to optimize (chemical) systems and processes where many variables are affecting the outcome. Different types experimental designs as well as analysis and optimization methods are covered.
Multivariate data analysis: This part deals with how large complex data built up by many and correlated variables can be analyzed so that, i) an overview of multivariate data can be obtained, ii) similarities and differences between observations can be detected and interpreted, iii) relations between blocks of data can be modeled and interpreted. Specific applications are covered, such as, quantitative structure activity relationships (QSARs), multivariate calibration, multivariate classification as well as monitoring and control of industrial and other processes. Different types of multivariate projection methods are presented, such as, principal components analysis (PCA), partial least squares projections to latent structures (PLS) and ortogonal PLS (OPLS)
Required Knowledge
Passed courses in Chemistry, 45 credits, or the equivalent.
Literature
The literature list is not available through the web.
Please contact the faculty.
