CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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7.2.3 Feature extraction Currently, feature extraction was implemented in the analysis pipeline only as part of PCA. However, a more thorough investigation of feature extraction algorithms may help increase the generalisation accuracy and apply further dimensionality reduction of the multivariate datasets. In any feature extraction algorithm, the variables are ranked based on their contribution to the classifier, and the variables that present high ranks are considered optimal. The implementation of feature extraction may be straightforward for relatively simple classification models, however the difficulty increases for the kernel-based SVMs, where the high-dimensional feature space is quite complex. A widely applied feature extraction method that may prove to be fruitful in the case of SVMs is the SVM-RFE algorithm (Guyon et al. 2002; the algorithm is based on the iterative backwards sequential selection method using weights as the variable criterion (Xu et al., 2006). 201
REFERENCES Almeida, J. A. S., Barbosa, L. M. S., Pais, A. A. C. C. and Formosinho, S. J. (2007), "Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering", Chemometrics and Intelligent Laboratory Systems, vol. 87, no. 2, pp. 208-217. Alvarez-Ordoñez, A. and Prieto, M. (2012), “Technical and Methodological Aspects of Fourier Transform Infrared Spectroscopy in Food Microbiology Research”, in Fourier Transform Infrared Spectroscopy in Food Microbiology, Springer, pp. 1-18. Ammor, M. S., Argyri, A. and Nychas, G. E. (2009), "Rapid monitoring of the spoilage of minced beef stored under conventionally and active packaging conditions using Fourier transform infrared spectroscopy in tandem with chemometrics", Meat Science, vol. 81, no. 3, pp. 507-514. Andrade, J. M., Gómez-Carracedo, M. P., Krzanowski, W. and Kubista, M. (2004), "Procrustes rotation in analytical chemistry, a tutorial", Chemometrics and Intelligent Laboratory Systems, vol. 72, no. 2, pp. 123-132. Argyri, A. A. (2010), Quantifying meat spoilage with an array of biochemical indicators (Doctor of Philosophy thesis), Cranfield University, Cranfield Health. Argyri, A. A., Doulgeraki, A. I., Blana, V. A., Panagou, E. Z. and Nychas, G. -. E. (2011), "Potential of a simple HPLC-based approach for the identification of the spoilage status of minced beef stored at various temperatures and packaging systems", International journal of food microbiology, vol. 150, no. 1, pp. 25-33. Argyri, A. A., Jarvis, R. M., Wedge, D., Xu, Y., Panagou, E. Z., Goodacre, R. and Nychas, G. -. E. (2013), "A comparison of Raman and FT-IR spectroscopy for the prediction of meat spoilage", Food Control, vol. 29, no. 2, pp. 461-470. Argyri, A. A., Panagou, E. Z., Tarantilis, P. A., Polysiou, M. and Nychas, G. -. E. (2010), "Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks", Sensors and Actuators B: Chemical, vol. 145, no. 1, pp. 146-154. Arthur, D. and Vassilvitskii, S. (2007), "K-Means++: the Advantages of Careful Seeding", pp. 1035. Baralis, E. and Fiori, A. (2008), "Exploring heterogeneous biological data sources", pp. 647. Barker, M. and Rayens, W. (2003), "Partial least squares for discrimination", Journal of Chemometrics, vol. 17, no. 3, pp. 166-173. 202
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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
Page 165 and 166: (a) GPA (b) CPCA Figure 5-22 The co
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Page 169 and 170: Figure 5-24 Classification Results
Page 171 and 172: Figure 5-25 Class prediction rates
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Page 175 and 176: Figure 5-28 Distribution plots of t
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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 and 212: large number of individual models i
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Page 215: In addition, the difficulty to corr
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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