CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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a) KernSmooth package b) ggplot2 package Figure 6-7 Scatterplots with density estimation using the KernSmooth and ggplot2 packages Two-dimensional smoothed colour density scatterplots display the outcome of PCA when applied on standalone mean-centered FTIR data. Colour representation was used once more to identify the three distinct classes. Kernel density estimation techniques were applied to highlight the formation of any possible clusters. The scatter plots were initially enhanced with smoothed colour density representation using the KernSmooth R package (Wand et al., 2011). However, the density contour plots drawn by the ggplot2 package are relatively more intuitive and easier to comprehend than the basic plot. 187
In addition to aesthetics, faceting is the second most important functionality provided by the ggplot2 package. Facets are tables of graphics that represent subsets of data, commonly depicting the same type of graph. Faceting is an extremely powerful tool that enables direct and straightforward comparisons using several different parameters simultaneously. Figure 6-8 highlights the necessity for the application of specialised feature-rich libraries, using as an example the per-class percentages of correctly classifies samples from Section 2.3.2.2; in this instance, the two different graphical techniques that have been employed display exactly the same information. The first approach displays information using a static table of data. Even though this approach is extremely informative and efficient, it is relatively difficult for the reader to perform a direct visual comparison among the different cases, and extract any trends and/or underlying relationships. On the contrary, the powerful ggplot2 library makes use of different visualisation attributes, such as shapes, colour palettes and facets to grant the users with the ability to compare all different cases simultaneously. This functionality can be further extended to integrate several individual facets, which may consist of several different types of plots, together into a unified view. This approach, enables the simultaneous comparison of several different graphs in addition to parameters, and hence the comparison of several different aspects of the same study (see Figure 5-32). All the aspects that have been described thus far can be further exploited with the application of Sweave for the construction of dynamically generated reports. Figure 6-9 demonstrates the simplicity upon which the Sweave framework is built. The R scripts are wrapped within a small set of LATEX commands, which generate a pdf report in a dynamic and automatic fashion. The same Sweave document such as the one of Figure 6-9 can be successfully applied unaltered to several other input files in order to generate the exact same plot but on different data. Therefore, this approach verifies that reproducible documentation as well as graphs and figures can be produced upon demand. 188
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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
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 159 and 160: RBF SVMs Datasets Original %CC Mean
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 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: c) graphics package d) ggplot2 pack
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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