CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Figure 4-13 Superimposed density plots of the permutation tests on the datasets of case study 1 using PLS-DA and nonlinear (RBF) SVMs The figure provides a visual comparison of the permutation distributions when different classification models are applied on the datasets of case study 1; the distributions for PLS-DA and SVMs are depicted in a semi-transparent blue and red colour respectively. In these plots, the dashed lines represent the mean values of each density curve and are coloured accordingly. By superimposing the density plots, major differences in the shape, spread and location of the distributions can be identified. 107
RBF SVMs Datasets Original %CC Mean Value Median Value Min Value Max Value Upper 95%C.I. FTIR 62% 48 % 48% 45% 55% 51% HPLC 79% 48% 48% 45% 59% 51% E-NOSE 50% 47% 48% 43% 54% 51% FTIR & HPLC (GPA) 75% 47% 47% 44% 54% 50% FTIR & E-NOSE (GPA) 57% 48% 48% 45% 55% 51% HPLC & E-NOSE (GPA) 67% 48% 48% 44% 52% 50% ALL (GPA) 73% 48% 48% 43% 54% 51% FTIR & HPLC (CPCA) 72% 47% 47% 43% 55% 51% FTIR & E-NOSE (CPCA) 54% 48% 48% 44% 56% 51% HPLC&E-NOSE (CPCA) 76% 47% 47% 45% 56% 51% ALL (CPCA) 70% 48% 48% 44% 66% 52% Table 3 Descriptive statistics of the permutation distributions obtained by RBF SVMs (case study 1) The results presented in Table 1 have been rounded towards the nearest integer. 108
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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
Page 71 and 72: Figure 2-9 Three-dimensional error
Page 73 and 74: Furthermore, a thorough comparison
Page 75 and 76: In the master/slave architecture, a
Page 77 and 78: precision of the classification acc
Page 79 and 80: Figure 3-3 The steps of the Nelder-
Page 81 and 82: 3.3 Results and Discussion 3.3.1 Li
Page 83 and 84: 3.3.2 Nonlinear Models In order to
Page 85 and 86: 9) 10) 11) 12) 13) 14) 15) 16) Figu
Page 87 and 88: As an additional visual aid, these
Page 89 and 90: a) Number of iterations b) Number o
Page 91 and 92: Figure 3-10 Comparison of the execu
Page 93 and 94: Figure 3-12 Speedup produced by the
Page 95 and 96: 4 Integration of Heterogeneous Data
Page 97 and 98: 4.2.2 Procrustes Analysis Procruste
Page 99 and 100: symmetric method since the ordering
Page 101 and 102: Figure 4-3 Steps of CPCA for the da
Page 103 and 104: 4.2.5 Data Integration and Analysis
Page 105 and 106: Figure 4-4 Data integration workflo
Page 107 and 108: a) GPA b) CPCA Figure 4-6 The conse
Page 109 and 110: accuracies as the linear models; po
Page 111 and 112: Figure 4-8 Classification Results f
Page 113 and 114: Similar to FTIR, the PLS-DA ensembl
Page 115 and 116: Figure 4-9 Class prediction rates o
Page 117 and 118: 4.3.3 Permutation Tests Even though
Page 119 and 120: Even though the interpretation of t
Page 121: Figure 4-12 Distribution plots of t
Page 125 and 126: Figure 4-14 Boxplots representing t
Page 127 and 128: 5 Application of the multivariate a
Page 129 and 130: Figure 5-1 Mean FTIR spectra for ca
Page 131 and 132: 5.2.2.2 Fourier Transform Infrared
Page 133 and 134: 5.2.2.4 Data Overview For each expe
Page 135 and 136: 5.2.3.3 High Throughput Liquid Chro
Page 137 and 138: Figure 5-5 displays the PCA scores
Page 139 and 140: (a) GPA (b) CPCA Figure 5-6 The con
Page 141 and 142: observation confirms that indeed CP
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 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
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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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RBF SVMs Datasets Original %CC Mean
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Figure 5-30 Boxplots representing t
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5.4 Comparison of the individual ca
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Figure 5-32 Investigating the commo
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6 Development of improved visualisa
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6.2.2 Generating static graphs As d
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Figure 6-2 Construction process of
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6.2.2.2 Generating dynamic reports
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activity, while simultaneously the
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or disappeared (Wang et al., 2008).
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ectangles, circles and/or irregular
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In addition to the static figures g
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c) graphics package d) ggplot2 pack
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In addition to aesthetics, faceting
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Datasets FTIR HPLC e-nose PLS-DA (L
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Figure 6-10 Interactive dendrogram
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6.4 Conclusion The work reported in
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large number of individual models i
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The generated suite of tools, as pr
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In addition, the difficulty to corr
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REFERENCES Almeida, J. A. S., Barbo
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Processing., Proceedings of the 12t
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Clarke, B., Fokoué, E. and Zhang,
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spectroscopy and machine learning",
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Gower, J. C. (1975), "Generalized p
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Jardon, M. (2006), Systems Biology:
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Nicolaou, N., Xu, Y. and Goodacre,
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Shah, A. A., Barthel, D., Lukasiak,
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Vera, G., Jansen, R. and Suppi, R.
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Xu, Y. and Goodacre, R. (2012), "Mu
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