CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

TABLE OF CONTENTS
ABSTRACT .................................................................................................................. iii
ACKNOWLEDGMENTS ............................................................................................. iv
TABLE OF CONTENTS ............................................................................................... v
TABLE OF FIGURES ................................................................................................ viii
TABLE OF TABLES ................................................................................................... xii
TABLE OF EQUATIONS .......................................................................................... xiii
ABBREVIATIONS ..................................................................................................... xiv
1 Introduction and Literature Review .......................................................................... 1
1.1 Introduction ....................................................................................................... 1
1.1.1 Overview of Systems Biology .................................................................... 1
1.1.2 The ‘omics’ disciplines ............................................................................... 3
1.1.3 Microbial Spoilage in Meat ........................................................................ 6
1.2 Multivariate Analyses and Chemometrics ......................................................... 7
1.3 Data pre-treatment ............................................................................................. 8
1.3.1 Mean-centering ........................................................................................... 9
1.3.2 Auto-scaling ............................................................................................... 9
1.4 Multivariate Analysis: Unsupervised Methods ............................................... 10
1.4.1 Principal Component Analysis ................................................................. 10
1.4.2 Cluster Analysis ........................................................................................ 12
1.5 Multivariate Analysis: Supervised Learning ................................................... 14
1.5.1 Partial Least Squares – Discriminant Analysis......................................... 14
1.5.2 Support Vector Machines ......................................................................... 15
1.5.3 Ensemble Models ..................................................................................... 22
1.6 Validation ........................................................................................................ 23
1.6.1 The holdout method .................................................................................. 24
1.6.2 k-fold Cross-Validation ............................................................................ 25
1.6.3 Leave-One-Out Cross-Validation ............................................................. 26
1.6.4 Bootstrapping ........................................................................................... 27
1.6.5 Model Selection, complexity and the bias-variance trade-off .................. 27
1.7 Permutation Tests ............................................................................................ 29
1.8 Aims and objectives ........................................................................................ 30
2 Development of the multivariate analysis pipeline for the detection of meat
spoilage ......................................................................................................................... 32
2.1 Introduction ..................................................................................................... 32
2.2 Materials and Methods .................................................................................... 32
2.2.1 Case study 1: “Shelf life beef fillets stored in air at 0, 5, 10, 15 and 20°C”
32
2.2.2 Data pre-Processing and Dimensionality Reduction ................................ 37
2.2.3 Standalone Classifiers: PLS-DA models with LOOCV ........................... 38
2.2.4 Ensemble of Classifiers ............................................................................ 39
2.2.5 The Architecture ....................................................................................... 42
2.2.6 Implementation in R ................................................................................. 43
2.3 Results and Discussion .................................................................................... 44
2.3.1 Principal Component Analysis ................................................................. 44
2.3.2 Classification Results ............................................................................... 47
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