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classification models since they produced consistently higher overall accuracies ( ) than PLS-DA. Among the analytical techniques, HPLC proved to be the most diagnostic technique for the assessment of meat freshness, with classification accuracies around 80%. On the contrary, e-nose did not demonstrate any discriminative information, and its results proved to be statistically non-significant. Thus, we can conclude that the provided HPLC data contained abundances of several specific chemical compounds associated with and denoting spoilage. Conversely, the FTIR, Raman and e-nose data were the measurements of raw sensors with no prior feature selection or mapping to specific compounds. At a per-class level, the semifresh samples were consistently difficult to classify, whether they constituted the majority or minority class. Furthermore, in the majority of cases, CPCA produced higher overall and per-class accuracies ( ) than GPA. For case study 4, the obtained by CPCA managed to exceed not only the outcome of GPA but also the overall accuracy recorded by standalone HPLC, equal to 80%. This proves that the comprehensive fusion of valuable information from complementary analytical techniques may indeed result in greater performance. This observation proves that the fusion of the most discriminative information from complementary analytical techniques may indeed result in greater classification performance. In this research, in addition to the implementation of the analysis pipeline, great importance was also given in the development of powerful visualisation techniques as a means of enhancing the interpretability of the obtained results. Chapter 6 highlights the necessity for designing informative yet also aesthetically pleasing graphs. The graphical methods and web technologies presented in the chapter were used to construct the graphs throughout the thesis, demonstrate the outcome of the various analyses. The generated graphs, ranging from high-quality static images to reproducible reports and interactive web-based plots, proved to be more efficient as means of data representation than other traditional visualisation methods. 197
The generated suite of tools, as presented throughout this thesis, has covered with equal emphasis a wide range of chemometric fields, including data aggregation, integration, analysis and visualisation. The software tools show the most promise in terms of performance, flexibility and simplicity. Exceptional attention and precision has been given to the application of rigorous validation and evaluation techniques as a means of generating as accurate, robust and unbiased models as possible; as stated in Brereton (2006) and Westerhuis et al. (2008), even though proper validation of machine learning models has received special attention over the past decade, it is most often lacking in the recent applications. Furthermore, a novelty of this research was the application of advanced optimisation techniques, which resulted in a striking speedup of the end-to-end analyses without however compromising the integrity of the validation and evaluation process; the minimisation of the computational cost and complexity was so impressive, it reached the point where all end-to-end analyses within the pipeline were executed within a few hours on a personal computer, obviating any need of a server or supercomputer. In addition, the analysis pipeline was built in the context of reproducible research in an extremely simple, straightforward and user-friendly way. The functionality of the implemented statistical tools can be further fused into a unified form of a single R package. The package can be uploaded on CRAN, the official R repository, where it will be freely available to others users. Having confirmed the generic nature and applicability of the developed tools by testing them on new real-world case studies, the package can be equally efficient when applied by other scientists to areas of inquiry far wider and more diverse than the present study. Finally, the package provides a great degree of extendibility, since it allows its users to conduct completely personalised analyses in addition to modifying and expanding its functionality. In summary, the project aims and objectives set out in Chapter 1 have been successfully met. In addition, the project has generated ideas for further work, which are explored in the following Section. 198
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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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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 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 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: large number of individual models i
Page 215 and 216: In addition, the difficulty to corr
Page 217 and 218: REFERENCES Almeida, J. A. S., Barbo
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Page 221 and 222: Clarke, B., Fokoué, E. and Zhang,
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Page 225 and 226: Gower, J. C. (1975), "Generalized p
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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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