Learning outcome

A candidate who has completed the course will have a learning outcome in the form of aquired knowledge, skills, and general competence, as described below.

Knowledge

The candidate will understand:

• multivariate data analysis techniques and algorithms
• experimental design approaches
• multivariate calibration techniques and algorithms
• Orthogonal Signal Correction OSC and algorithms
• the operations involved in acoustic chemometrics
• the theory and practice of sampling

Skills

The candidate will be able to:

• program software needed for multivariate analysis (PCA)
• program software for multivariate calibration (PCR, PLS)
• analyze multivariate data
• calibrate multivariate prediction models
• plan experimental work based on Design of Experiments (DOE)

General competence

Course Description

The course includes topics within (but not restricted to):

• Multivariate calibration – advanced theory
• Acoustic Chemometrics
• AMT (Angle Measure Technique)
• 3-way data decomposition (N-way)
• Model reduction
• Process monitoring (Projection of Latent Structures, 2PLS)
• OPLS (Orthogonal Partial Least Squares Regression)
• Wavelet transform
• Fourier Transform (FT), Fast Fourier Transform (FFT)
• Validation of regression models

Teaching and Learning Methods

Lectures, Exercises, Assignments, data analysis on computers, experimental work.

Assessment Methods

Final written exam.

Minor adjustments may occur during the academic year, subject to the decision of the Dean