CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Google Chart Tools (https://developers.google.com/chart/). The users can create dynamic, interactive, feature-rich web-based graphs via R, without uploading their data directly to Google. However, the package does have certain limitations; in order to visualise the graphs in the browser, there is a need for Internet connection in addition to the installation of the Flash plug-in. Another package, iPlots (Urbanek and Theus, 2003) is a powerful R package for the creation of interactive graphics that offers features such as querying, linked highlighting, and colour brushing, among others. Even so, the iPlots package runs as a standalone graphical user interface (GUI) within the R console, and cannot be embedded within any other project or interface. In addition, the core of the package is written in Java, hence a fully working Java installation on the local machine is expected. 6.2.5 Constructing a web interface for demonstrative purposes As part of this project, a web interface has been constructed for demonstrative purposes as the front-end of the multivariate analysis pipeline. This approach attempts to evaluate whether the results of the analyses can be directly embedded within any HTML page and/or web project. As presented in Section 6.2.1, the web interface was chiefly built by using two powerful JavaScript-based technologies; namely, jQuery and AJAX. In this work, AJAX requests are triggered from the web front-end, more specifically from jQuery scripts embedded within static HTML pages, towards specific Perl files located at the server-side. These requests may also carry additional information such as associated data and input parameters. Subsequently, the Perl scripts execute the relevant R scripts, also stored on the web server, after passing these parameters. Once the R script terminates the execution of the scripts, it returns the output of the analyses (calculation results, plots, files, et al.) back to Perl. Finally, Perl sends an asynchronous AJAX response along with the requested results back to the original client-based script. The web interface is partially updated and the output from R is displayed to the user, without the need of reloading the entire page. Due to AJAX and its asynchronous functionality, the exchange of requests, responses and data is performed within seconds. 183
In addition to the static figures generated by R, the iWebPlots package (see Section 6.2.4) was also employed on the server-side for the construction of fully interactive web-based graphs. The graphs were easily incorporated in the main web interface for users to view. The functionality of iWebPlots in this context was successfully tested on the outcome of supervised and unsupervised techniques such as Principal Component Analysis (PCA). Finally, the DataTables plug-in has been used in order to construct a fully interactive data table. The table stores and displays associated metadata for the objects displayed in the plot; for instance, in the case study 1 (shelf life of beef fillets), such information may contain the name, class (sensory scores) and/or instrumental technique for each sample depicted in the graph. These data are exported from R in a JSON format that is received by AJAX and parsed by jQuery. One of the most powerful attributes of DataTables is the provided search mechanism. More specifically, the table can be filtered using either the default search box or by clicking one of the points in the interactive map. This “on-click” event generates an AJAX request, which is posting the selected sample’s name as a parameter against the entries of the data table. As a result, the table contents are filtered and only the matches are displayed. Finally, additional attributes include pagination, sorting as well as colouring of the rows based on a predefined criterion such as the class values (fresh, semi-fresh, spoiled samples). 184
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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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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 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: ectangles, circles and/or irregular
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 and 234: Vera, G., Jansen, R. and Suppi, R.
Page 235: Xu, Y. and Goodacre, R. (2012), "Mu
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