CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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In order to test the applicability and generic nature of the package, it was thoroughly examined on various different Case Studies. The package constructs dynamic barplots, histograms and scatterplots, among others. Figure 6-10 demonstrates the most commonly applied visual aid for the outcome of hierarchical cluster analysis (HCA) – the dendrogram – as generated directly by the iWebPlots package. The lines connecting the nodes in the dendrogram represent the distance (the degree of dissimilarity) between the leaves or clusters. In this instance, the HCA algorithm has been applied on the PCA scores of the HPLC dataset for case study 1. Similar to the PCA score plots of Section 2.3.1, it can be concluded from the figure that mainly spoiled and fresh samples present good clustering, whereas semi-fresh samples are usually grouped with either one of the two. The leaves of the cluster are all interactive, while they can be easily coloured upon the user’s request. The web pages offer fully interactive plots and data tables with dynamic and asynchronous features (Figure 6-11) as described in Section 6.2.5. The users can interact with each point in the plot, which automatically filters the corresponding entry in the data table, whereas rollover and click upon events display dynamic tooltips with associated metadata. In addition, the users can highlight one, multiple or all available classification groups (fresh, semi-fresh, spoiled). Furthermore, the samples’ names can be dynamically enabled or disabled in the plots, whereas the users can alternate between two-dimensional and three-dimensional plots as easily. The scaling of the data and the axes can be altered at any time asynchronously without reloading the whole page. Finally, the users can view the spectroscopic data on the web interface upon demand. 189
Datasets FTIR HPLC e-nose PLS-DA (LOOCV) 80% 67% 69% 90% 33% 88% 40% 0% 62% PLS-DA (BOOT) 64% 24% 71% 86% 26% 92% 27% 0% 64% k-fold Linear SVM 65% 21% 73% 94% 16% 87% 6% 0% 86% k-fold RBF SVM 55% 10% 79% 94% 6% 99% 13% 0% 82% BOOT RBF SVM 41% 19% 77% 87% 23% 97% 6% 1% 90% Figure 6-8 Comparison of static data representation and powerful feature-rich visualisation The figure illustrates the significant differences between traditional visualisation approaches such as the static tables and the proposed visualisation tools. Even though the static table is informative, it is relatively difficult to perform a direct comparison across the different entries. On the contrary, the application of ggplot2 provides an efficient and straightforward way to simultaneously compare several parameters under study (instrumental techniques and classification/validation models, distinct classes, overall accuracies). 190
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CRANFIELD UNIVERSITY Eleni Anthippi
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ABSTRACT Muscle foods such as meat,
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TABLE OF CONTENTS ABSTRACT ........
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6.2.4 The iWebPlots package .......
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Figure 3-12 Speedup produced by the
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Figure 6-9 Sweave example for the d
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TABLE OF EQUATIONS Equation 1 Mean-
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RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
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Figure 1-1 Evolution from molecular
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1.1.2.1 Fourier Transform Infrared
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1.1.3 Microbial Spoilage in Meat Sy
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The multivariate techniques applied
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1.4 Multivariate Analysis: Unsuperv
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1.4.2 Cluster Analysis Cluster anal
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1.5 Multivariate Analysis: Supervis
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In any linearly separable binary da
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Where ( ) are the Lagrange multipli
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Every kernel is characterised by a
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The “one-against-all” approach
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Furthermore, metrics such as the bi
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1.6.3 Leave-One-Out Cross-Validatio
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In such cases, the model becomes qu
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1.8 Aims and objectives The overall
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2 Development of the multivariate a
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Sensory panel scores were of the hi
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DF (Hz) 2.2.1.5 Electronic Nose (e-
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Figure 2-3 Data intersection The fi
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3. Validation Techniques In the cas
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Figure 2-4 The process of construct
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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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RBF SVMs Datasets Original %CC Mean
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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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Page 155 and 156: Figure 5-16 Class prediction rates
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Page 159 and 160: RBF SVMs Datasets Original %CC Mean
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Page 163 and 164: (a) FTIR data (b) HPLC data 148
Page 165 and 166: (a) GPA (b) CPCA Figure 5-22 The co
Page 167 and 168: The classification results of the f
Page 169 and 170: Figure 5-24 Classification Results
Page 171 and 172: Figure 5-25 Class prediction rates
Page 173 and 174: The permutation results for the dat
Page 175 and 176: Figure 5-28 Distribution plots of t
Page 177 and 178: RBF SVMs Datasets Original %CC Mean
Page 179 and 180: Figure 5-30 Boxplots representing t
Page 181 and 182: 5.4 Comparison of the individual ca
Page 183 and 184: Figure 5-32 Investigating the commo
Page 185 and 186: 6 Development of improved visualisa
Page 187 and 188: 6.2.2 Generating static graphs As d
Page 189 and 190: Figure 6-2 Construction process of
Page 191 and 192: 6.2.2.2 Generating dynamic reports
Page 193 and 194: activity, while simultaneously the
Page 195 and 196: or disappeared (Wang et al., 2008).
Page 197 and 198: ectangles, circles and/or irregular
Page 199 and 200: In addition to the static figures g
Page 201 and 202: c) graphics package d) ggplot2 pack
Page 203: In addition to aesthetics, faceting
Page 207 and 208: Figure 6-10 Interactive dendrogram
Page 209 and 210: 6.4 Conclusion The work reported in
Page 211 and 212: large number of individual models i
Page 213 and 214: The generated suite of tools, as pr
Page 215 and 216: In addition, the difficulty to corr
Page 217 and 218: REFERENCES Almeida, J. A. S., Barbo
Page 219 and 220: Processing., Proceedings of the 12t
Page 221 and 222: Clarke, B., Fokoué, E. and Zhang,
Page 223 and 224: spectroscopy and machine learning",
Page 225 and 226: 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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