CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Figure 6-9 Sweave example for the dynamic construction of a PDF report directly from R The figure illustrates the simplicity of the Sweave format; all R functions are fused together with the LATEX commands into a single *.Rnw file. In this example, Sweave generates a dynamic pdf report containing a ggplot2 boxplot for the comparison of permutation results for different classification ensembles. The relevant data are read from a comma-delimited Excel file. If the input data were to be changed by providing a different file, the actual R script could still be used without alterations in order to reproduce the final figure; thus, great reproducibility is accomplished. 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 The figure displays the constructed dendrogram when HCA is applied on the HPLC dataset of case study 1. Once more, only the 32 common samples are used as a visual aid. In this example, “Euclidean distance” was applied as the distance algorithm and “centroid method” as the linkage algorithm. The samples have been automatically coloured by the iWebPlots package; different colour representations are used to differentiate the distinct classification groups (red colour is used for fresh, orange for semi-fresh and green for spoiled samples) In addition, the package provides full interactivity of the dendrogram’s leaves; the users can click or rollover the coloured boxes and/or labels, and view additional information about the sample under study. This interactive web-based plot can be embedded in any web interface and/or web project. 192
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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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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 and 202: c) graphics package d) ggplot2 pack
Page 203 and 204: In addition to aesthetics, faceting
Page 205: Datasets FTIR HPLC e-nose PLS-DA (L
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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