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Figure 5-31 Execution times of the permutation tests on the datasets of case study 4 The figure displays the execution times of 100 permutation tests using PLS-DA and RBF SVMs for each standalone and integrated dataset of case study 4. The execution times are based on a fully optimised analysis pipeline featuring parallel programming (master/slave architecture) over eight processors (see Section 5.2.4) as well as fast approximation algorithms for the optimisation of the classifiers’ hyperparameters via bootstrapping. The execution times have been rounded towards the nearest integer. 165
5.4 Comparison of the individual case studies In this chapter, the suite of machine learning and validation tools developed in Chapter 4 was applied to three further independent case studies. This Section provides an overall comparison of the performance of the three implemented classification ensembles (PLS-DA, linear and nonlinear SVM ensembles) among the different case studies, when applied on standalone and integrated datasets. In addition, this Section aims to reveal any common underlying patterns, similarities and/or differences between the different case studies and the classifiers. The overall trend across all four case studies is presented in the graph of Figure 5-32. For the FTIR data, all case studies besides the “Survey of minced beef” demonstrate the exact same pattern; clearly, linear classifiers (PLS-DA and linear SVMs) favour the standalone FTIR data both prior and after PCA. On the contrary, the overall performance of kernel-based (RBF) SVMs appears to be inferior to the other two linear classification ensembles. As thoroughly discussed in Section 4.3.2, Xu et al. (2006) support that relatively simplistic chemometric algorithms such as PLS-DA are more efficient when applied on traditional analytical techniques such as spectroscopy, where the data are linear and well understood. Therefore, the nonlinear projection into a high-dimensional feature space via a kernel is found unsuitable in this instance. Finally, for the standalone FTIR data that have been subjected to PCA, the accuracies of nonlinear SVMs approximate those of the linear models. In addition, HPLC proved to be the most diagnostic instrumental technique since it consistently produces the best recorded classification accuracies. In all case studies that include HPLC, the percentages of correctly classified samples ( ) are significantly higher when SVMs are applied on the datasets as opposed to PLS-DA; the HPLC data demonstrate outstanding results especially in the cases of RBF SVMs. Furthermore, the HPLC datasets that have been subjected to PCA also follow a similar trend, with exception of case study 1. 166
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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
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 161 and 162: Figure 5-20 Execution times of the
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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: Figure 5-30 Boxplots representing t
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
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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
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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
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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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