CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Figure 5-14 The consensus of the first two Principal Components based on the fusion of the two experimental techniques from case study 3 using GPA and CPCA respectively ......................................................................................................... 137 Figure 5-15 Overall accuracies (%CC) for the standalone and integrated datasets of case study 3 ......................................................................................................... 139 Figure 5-16 Class prediction rates of the standalone (prior and after PCA) and integrated datasets for case study 3 .................................................................... 140 Figure 5-17 Distribution plots of the permutation tests on the datasets of case study 3 using RBF SVMs and PLS-DA respectively ...................................................... 142 Figure 5-18 Superimposed density plots of the permutation tests on the datasets of case study 3 using PLS-DA and nonlinear (RBF) SVMs ................................... 143 Figure 5-19 Boxplots representing the outcome of permutation testing when RBF SVMs and PLS-DA are applied on the datasets of case study 3 ........................ 145 Figure 5-20 Execution times of the permutation tests on the datasets of case study 3 ............................................................................................................................ 146 Figure 5-21 PCA scores plots with 95% confidence ellipses for case study 4 ........... 149 Figure 5-22 The consensus of the first two Principal Components based on the fusion of the two experimental techniques of case study 4 using GPA and CPCA respectively ......................................................................................................... 150 Figure 5-23 Overall accuracies (%CC) for the standalone datasets of case study 4 .. 153 Figure 5-24 Classification Results for the integrated datasets of case study 4........... 154 Figure 5-25 Class prediction rates of the standalone (prior and after PCA) datasets for case study 4 ......................................................................................................... 156 Figure 5-26 Class prediction rates of the integrated datasets for case study 4 ........... 157 Figure 5-27 Distribution plots of the permutation tests on the datasets of case study 4 using RBF SVMs ................................................................................................ 159 Figure 5-28 Distribution plots of the permutation tests on the data of case study 4 using PLS-DA .................................................................................................... 160 Figure 5-29 Superimposed density plots of the permutation tests on the datasets of case study 4 using PLS-DA and nonlinear (RBF) SVMs ................................... 161 Figure 5-30 Boxplots representing the outcome of permutation testing when RBF SVMs and PLS-DA are applied on the datasets of case study 4 ........................ 164 Figure 5-31 Execution times of the permutation tests on the datasets of case study 4 ............................................................................................................................ 165 Figure 5-32 Investigating the common trends across all four individual case studies168 Figure 6-1 The “designer-reader-data trinity” of data visualisation ........................... 171 Figure 6-2 Construction process of a ggplot2 graph – the layered grammar approach ............................................................................................................................ 174 Figure 6-3 The workflow from Sweave to an automatically generated PDF file....... 176 Figure 6-4 The progress of web technologies and programming languages over time ............................................................................................................................ 178 Figure 6-5 Comparison between the classic and the AJAX web application model .. 180 Figure 6-6 Scatterplots produced by the graphics and ggplot2 package respectively 186 Figure 6-7 Scatterplots with density estimation using the KernSmooth and ggplot2 packages.............................................................................................................. 187 Figure 6-8 Comparison of static data representation and powerful feature-rich visualisation ........................................................................................................ 190 x
Figure 6-9 Sweave example for the dynamic construction of a PDF report directly from R ................................................................................................................. 191 Figure 6-10 Interactive dendrogram generated by the iWebPlots package representing the outcome of HCA when applied on the HPLC data of case study 1 .............. 192 Figure 6-11 Partial view of the implemented web interface for the FTIR dataset of case study 1 ................................................................................................................ 193 xi
Page 1 and 2: CRANFIELD UNIVERSITY Eleni Anthippi
Page 3 and 4: ABSTRACT Muscle foods such as meat,
Page 5 and 6: TABLE OF CONTENTS ABSTRACT ........
Page 7 and 8: 6.2.4 The iWebPlots package .......
Page 9: Figure 3-12 Speedup produced by the
Page 13 and 14: TABLE OF EQUATIONS Equation 1 Mean-
Page 15 and 16: RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
Page 17 and 18: Figure 1-1 Evolution from molecular
Page 19 and 20: 1.1.2.1 Fourier Transform Infrared
Page 21 and 22: 1.1.3 Microbial Spoilage in Meat Sy
Page 23 and 24: The multivariate techniques applied
Page 25 and 26: 1.4 Multivariate Analysis: Unsuperv
Page 27 and 28: 1.4.2 Cluster Analysis Cluster anal
Page 29 and 30: 1.5 Multivariate Analysis: Supervis
Page 31 and 32: In any linearly separable binary da
Page 33 and 34: Where ( ) are the Lagrange multipli
Page 35 and 36: Every kernel is characterised by a
Page 37 and 38: The “one-against-all” approach
Page 39 and 40: Furthermore, metrics such as the bi
Page 41 and 42: 1.6.3 Leave-One-Out Cross-Validatio
Page 43 and 44: In such cases, the model becomes qu
Page 45 and 46: 1.8 Aims and objectives The overall
Page 47 and 48: 2 Development of the multivariate a
Page 49 and 50: Sensory panel scores were of the hi
Page 51 and 52: DF (Hz) 2.2.1.5 Electronic Nose (e-
Page 53 and 54: Figure 2-3 Data intersection The fi
Page 55 and 56: 3. Validation Techniques In the cas
Page 57 and 58: Figure 2-4 The process of construct
Page 59 and 60: 2.3 Results and Discussion 2.3.1 Pr
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(b) HPLC data (c) e-nose data Figur
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In the case of FTIR, linear classif
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2.3.2.2 Class Prediction Accuracies
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Figure 2-7 Class prediction rates o
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The topology of the three-dimension
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Figure 2-9 Three-dimensional error
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Furthermore, a thorough comparison
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In the master/slave architecture, a
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precision of the classification acc
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Figure 3-3 The steps of the Nelder-
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3.3 Results and Discussion 3.3.1 Li
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3.3.2 Nonlinear Models In order to
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9) 10) 11) 12) 13) 14) 15) 16) Figu
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As an additional visual aid, these
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a) Number of iterations b) Number o
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Figure 3-10 Comparison of the execu
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Figure 3-12 Speedup produced by the
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4 Integration of Heterogeneous Data
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4.2.2 Procrustes Analysis Procruste
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symmetric method since the ordering
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Figure 4-3 Steps of CPCA for the da
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4.2.5 Data Integration and Analysis
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Figure 4-4 Data integration workflo
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a) GPA b) CPCA Figure 4-6 The conse
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accuracies as the linear models; po
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Figure 4-8 Classification Results f
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Similar to FTIR, the PLS-DA ensembl
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4.3.3 Permutation Tests Even though
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Even though the interpretation of t
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Figure 4-12 Distribution plots of t
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RBF SVMs Datasets Original %CC Mean
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Figure 4-14 Boxplots representing t
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5 Application of the multivariate a
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Figure 5-1 Mean FTIR spectra for ca
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5.2.2.2 Fourier Transform Infrared
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5.2.2.4 Data Overview For each expe
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5.2.3.3 High Throughput Liquid Chro
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Figure 5-5 displays the PCA scores
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(a) GPA (b) CPCA Figure 5-6 The con
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observation confirms that indeed CP
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(a) RBF SVMs (b) PLS-DA Figure 5-9
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Figure 5-12 Execution times of the
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(a) FTIR data (b) Raman data Figure
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The classification results for the
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Figure 5-16 Class prediction rates
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(a) RBF SVMs (b) PLS-DA Figure 5-17
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Figure 5-20 Execution times of the
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(a) FTIR data (b) HPLC data 148
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(a) GPA (b) CPCA Figure 5-22 The co
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The classification results of the f
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Figure 5-24 Classification Results
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Figure 5-25 Class prediction rates
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The permutation results for the dat
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Figure 5-28 Distribution plots of t
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Figure 5-30 Boxplots representing t
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5.4 Comparison of the individual ca
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Figure 5-32 Investigating the commo
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6 Development of improved visualisa
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6.2.2 Generating static graphs As d
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Figure 6-2 Construction process of
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6.2.2.2 Generating dynamic reports
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activity, while simultaneously the
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or disappeared (Wang et al., 2008).
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ectangles, circles and/or irregular
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In addition to the static figures g
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c) graphics package d) ggplot2 pack
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In addition to aesthetics, faceting
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Datasets FTIR HPLC e-nose PLS-DA (L
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Figure 6-10 Interactive dendrogram
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6.4 Conclusion The work reported in
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large number of individual models i
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The generated suite of tools, as pr
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In addition, the difficulty to corr
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REFERENCES Almeida, J. A. S., Barbo
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Processing., Proceedings of the 12t
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Clarke, B., Fokoué, E. and Zhang,
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spectroscopy and machine learning",
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Gower, J. C. (1975), "Generalized p
Page 227 and 228:
Jardon, M. (2006), Systems Biology:
Page 229 and 230:
Nicolaou, N., Xu, Y. and Goodacre,
Page 231 and 232:
Shah, A. A., Barthel, D., Lukasiak,
Page 233 and 234:
Vera, G., Jansen, R. and Suppi, R.
Page 235:
Xu, Y. and Goodacre, R. (2012), "Mu
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