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Wickham, H. (2009), ggplot2: elegant graphics for data analysis, Springer New York. Wickham, H. and Chang, W. (2012), "ggplot2: An implementation of the Grammar of Graphics", . Wilkinson, L., Anand, A. and Grossman, R. (2005), "Graph-theoretic scagnostics", Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on, pp. 157. Wilkinson, L., Wills, D., Rope, D., Norton, A. and Dubbs, R. (2005), The Grammar of Graphics, Springer. Wise, B. M., Gallagher, N. B., Bro, R. and Shaver, J. M. (2003), PLS_Toolbox 3.0 for use with MATLAB, available at: https://noppa.tkk.fi/noppa/kurssi/ke- 90.5100/materiaali/pca___pls.pdf. Wold, H. (1975), "Soft Modeling by Latent Variables; the Nonlinear Iterative Partial Least Squares Approach", Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett, , pp. 520-540. Wold, S., Esbensen, K. and Geladi, P. (1987), "Principal component analysis", Chemometrics and Intelligent Laboratory Systems; Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists, vol. 2, no. 1, pp. 37-52. Wold, S., Hellberg, S., Lundstedt, T., Sjöström, M. and Wold, H. (1987), "PLS modeling with latent variables in two or more dimensions", Proceedings PLSmeeting, Frankfurt am Main, pp. 1. Wold, S. and Sjöström, M. (1998), "Chemometrics, present and future success", Chemometrics and Intelligent Laboratory Systems, vol. 44, no. 1–2, pp. 3-14. Wold, S., Sjöström, M. and Eriksson, L. (2001), "PLS-regression: a basic tool of chemometrics", Chemometrics and Intelligent Laboratory Systems, vol. 58, no. 2, pp. 109-130. Wright, M. H. (2012), Nelder, Mead, and the other Simplex Method, available at: http://www.math.uiuc.edu/documenta/vol-ismp/42_wright-margaret.pdf. Wu, W., Guo, Q., Jong, S. d. and Massart, D. L. (2002), "Randomisation test for the number of dimensions of the group average space in generalised Procrustes analysis", Food Quality and Preference, vol. 13, no. 3, pp. 191. Xu, Y., Zomer, S. and Brereton, R. G. (2006), "Support vector machines: a recent method for classification in chemometrics", Critical Reviews in Analytical Chemistry, vol. 36, no. 3-4, pp. 177-188. Xu, Y. and Brereton, R. G. (2005), "A comparative study of cluster validation indices applied to genotyping data", Chemometrics and Intelligent Laboratory Systems, vol. 78, no. 1–2, pp. 30-40. 219
Xu, Y. and Goodacre, R. (2012), "Multiblock principal component analysis: an efficient tool for analyzing metabolomics data which contain two influential factors", Metabolomics, vol. 8, no. 1, pp. 37-51. Yu, H. (2011), Rmpi: Interface (Wrapper) to MPI (Message-Passing Interface), available at: http://cran.r-project.org/web/packages/Rmpi/index.html (accessed 2011). Zeger, S. L., Irizarry, R. and Peng, R. D. (2006), "On time series analysis of public health and biomedical data", Annu Rev Public Health, vol. 27, pp. 57-79. Zhijie Lin, Jiyi Wu, Qifei Zhang and Hong Zhou (2008), "Research on Web Applications Using Ajax New Technologies", MultiMedia and Information Technology, 2008. MMIT '08. International Conference on, pp. 139. Zimmerman, J. E., Kramer, A. A., McNair, D. S. and Malila, F. M. (2006), "Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients", Crit Care Med, vol. 34, no. 5, pp. 1297-1310. 220
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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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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
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 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: Vera, G., Jansen, R. and Suppi, R.
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