CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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The chief aim of visualisation is to make the data accessible. The assessment of a good visualisation tool is based on a three-fold relationship among the designer, the reader and the data, as presented in the schematic diagram of Figure 6-1. According to Illinsky et al. (2011), in order for a visualisation graphic to be successful, first of all it needs to be informative. In addition, the designer has to convey the information in the most persuasive and efficient (possible) way, by including only precise information while keeping the visual “noise” at a minimum (Steele et al., 2010). Finally, the graphs must be aesthetically pleasing; according to Illinsky et al. (2011), visualisation “leverages the incredible capabilities and bandwidth of the humans’ visual system to move a huge amount of information into the brain very quickly”. The overall level of success of a visualisation tool is highly dependent on the balance in between these relationships. Designers often fail to achieve a balance between functionality and aesthetics, hence leading into extremely complex, abstruse, biased and/or misleading results. In this thesis, the graphs that represent the results of the multivariate analysis pipeline have been meticulously designed to satisfy all the afore-mentioned attributes; equal importance has been given to the implementation of the analysis pipeline as well as the development of efficacious comprehensive and reproducible graphs. Figure 6-1 The “designer-reader-data trinity” of data visualisation The visualisation outcome chiefly depends on which of these relationships is dominating over the others. Above all, the visualisation techniques have to be informative in addition to efficient. Furthermore, a visual aid should convey information to the readers in the most persuasive and aesthetically pleasing way. The figure has been extracted from Illinsky et al. (2011). 171
6.2.2 Generating static graphs As described in the previous chapters, the bulk of the work was implemented using the R project for statistical computing (see Section 2.2.6). Even though R does not qualify as a scripting language or web technology, it is a powerful and widely popular graphics tool for the construction of “publication-quality diagrams and plots” (Murrell, 2005). In this project, R has been extensively used as a means of constructing high-quality graphics that enhance the interpretability of the analysis results acquired by the multivariate analysis pipeline. Since R consists of a plethora of built-in and add-on packages of great variability, it is often extremely difficult to succeed in attaining optimal functionality; therefore, this section attempts to narrow down the enormity of the range of functions down to those of most relevance to the applications covered in this thesis. 6.2.2.1 R packages and functions The plot() function, as provided by the built-in graphics package (R Development Core Team, 2012), is the core function for plotting R objects, and constitutes the backbone for all other graphics packages and plotting functions. In this work, the function was used to construct simple two-dimensional scatter plots. The corresponding three-dimensional scatterplots are available through the popular scatterplot3d (Ligges and Mächler, 2003) package and the homonymous function. A major innovation in the R graphics field has been achieved with the introduction of the ggplot2 package (Wickham, 2009; Wickham and Chang, 2012). Nowadays, the ggplot2 package is widely recognised among the R users as the “best graphics package for R” (http://www.inside-r.org/packages/ggplot2/reviews/simply-bestgraphics-package-r) due to its clean and subtle aesthetics, ease of use and intuitive syntax. The package has been developed based on the Grammar of Graphics, written by Wilkinson (Wilkinson, 2005; Wilkinson et al., 2005). Each new plot consists of several independent reusable components, built in a layered grammar (Figure 6-2), where each of these features provides an additional functionality. Thus, ggplot2 is 172
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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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Figure 4-9 Class prediction rates o
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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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Page 137 and 138: Figure 5-5 displays the PCA scores
Page 139 and 140: (a) GPA (b) CPCA Figure 5-6 The con
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Page 143 and 144: Figure 5-8 Class prediction rates o
Page 145 and 146: (a) RBF SVMs (b) PLS-DA Figure 5-9
Page 147 and 148: RBF SVMs Datasets Original %CC Mean
Page 149 and 150: Figure 5-12 Execution times of the
Page 151 and 152: (a) FTIR data (b) Raman data Figure
Page 153 and 154: The classification results for the
Page 155 and 156: Figure 5-16 Class prediction rates
Page 157 and 158: (a) RBF SVMs (b) PLS-DA Figure 5-17
Page 161 and 162: Figure 5-20 Execution times of the
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 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: 6 Development of improved visualisa
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 and 204: In addition to aesthetics, faceting
Page 205 and 206: Datasets FTIR HPLC e-nose PLS-DA (L
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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