Module Overview

Design of Experiments and Multivariate Analysis

Data analysis and applied modelling is an increasingly important component in the Food and Pharmaceutical industries; both in manufacture and also research and development. This module covers a spectrum of aligned, applied mathematics in Food Science, including the modelling of biochemical and physical dynamics, and in Pharmaceutical Science dealing with issues arising from process development, optimisation and the application and interrogation of research data.

The focus of the module is to introduce and expose the student to modelling approaches that exploit informative experimental designs. Using these modelling approaches the student will be able to define phenomena as applied to the optimisation and development of manufacturing processes and also research data. The student will be exposed to recent advances in modelling practices and modern tools of analysis.

The student will learn through experiential, contextualised learning, and will develop skills in defining problem objectives in dynamic food and pharmaceutical manufacturing and research environments. These problem objectives will be examined through appropriate software tools to manage such queries and will be explored and interpreted in-depth. Modelling applications will be transposed and the student will be able to engage in critical application of their mathematical knowledge and appropriate applied models to both the Food and Pharmaceutical sectors.

Module Code

FOOD 4002

ECTS Credits


*Curricular information is subject to change

Linear and Nonlinear modelling
Introduction to design of experiments
Linear modelling
Nonlinear Modelling
Empirical vs Non-Empirical modelling

Kinetic modelling of physical and chemical phenomena
Chemical reaction kinetics
Biological reaction kinetics
Pharmacokinetics and Pharmacodynamics

Introduction to Stochastic modelling
Discriminating between variability and uncertainty
Probabilistic modelling 
Quantitative risk assessment

Chemometrics/Multivariate data analysis
Data pre-processing
Exploratory Analysis
Discriminant Models 1: Unsupervised
Discriminant Models 2: Supervised
Multivariate Regression

This module will be delivered via a blend of interactive classes and associated and aligned workshops.

Module Content & Assessment
Assessment Breakdown %
Other Assessment(s)100