Chemometrics
Kemometri

FMS210F, 7.5 credits

Valid from: Autumn 2021
Decided by: Margareta Sandahl
Date of establishment: 2021-04-14

General Information

Division: Food Technology
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: KLGN10
Teaching language: English

Aim

Build on the knowledge in design of experiments in order to be able to plan and perform more complicated experiments, as well as analyse data in several dimensions.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• be able to explain and use basic methods in factorial design
• be able to explain and use basic methods in cluster analysis, discriminant analysis, principal components, and partial least squares
• be able to evaluate and discuss results obtained through use of multivariate statistical methods

Competences and Skills

For a passing grade the doctoral student must

• plan a factorial design experiment
• suggest which multivariate statistical method should be used on a given problem
• structure and analyse multi-dimensional data materials using computer software for multivariate methods, and critically assess the result
• report the solutions of multivariate statistical problems in written reports and orally at seminars
• autonomously be able to analyse a specific problem at an advanced level with multivariate statistical methods for experimental design and multivariate data analysis and to be able to discuss these.

Course Contents

Complete and reduced factorial designs. Response surface analysis. Cluster analysis, discriminant analysis, principal component analysis (PCA), and partial least squares (PLS).

Course Literature

Brereton, R.: Data driven extraction for science.. Wiley, 2018. ISBN 9781118904664.

Instruction Details

Types of instruction: Lectures, seminars, laboratory exercises, exercises, self-study literature review

Examination Details

Examination formats: Written report, written assignments, seminars given by participants. The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Grading scale: Failed, pass
Examiner:

Admission Details

Admission requirements: FMA420 Linear Algebra or FMA421 Linear Algebra with Scientific Computation or FMA656 Mathematics, Linear Algebra or FMAA20 Linear Algebra with Introduction to Computer Tools or FMAB20 Linear Algebra
Assumed prior knowledge: A basic course in mathematical statistics and basic MATLAB programming.

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: http://www.food.lth.se/