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5.3 Results and Discussion 5.3.1 Case study 2 In case study 2 (“Shelf life of minced beef stored in air, MAP, and in active packaging at 0, 5, 10 and 15°C”), data have been acquired from two main experimental techniques: FTIR spectroscopy and high performance liquid chromatography (HPLC). The data intersection approach presented in Section 2.3.1 extracted a total of 75 common samples along with their respective sensory scores, which were inserted in the analysis pipeline; these samples consist of 25 fresh (F), 12 semi-fresh (SF) and 38 spoiled (S) samples. In this instance, the spoiled samples constitute the majority class, whereas the semi-fresh samples the minority class. Initially, PCA was applied on standalone pre-processed data for dimensionality reduction purposes in addition to investigating any underlying patterns in the data. The percentages of variance and cumulative variance for each experimental technique are presented in Table 8. In the case of FTIR, the first two PCs account for 99% of the variance. On the contrary, the HPLC data accumulate only 53.23% of the variance for the first three PCs, while at least five PCs are required to capture approximately 70% of the total variance. It is apparent that the percentages of variance and cumulative variance present a similar trend to the datasets of case study 1 (See Table 2) FTIR HPLC PCs %Var %Cum Var %Var %Cum Var PC1 98.26 98.26 25.89 25.89 PC2 0.83 99.09 14.12 40.01 PC3 0.33 99.42 13.22 53.23 PC4 0.21 99.63 9.22 62.45 PC5 0.16 99.79 7.87 70.32 Table 8 PCA proportion and cumulative variance captured for the datasets of case study 2 121
Figure 5-5 displays the PCA scores plots for the two datasets under study. Similar to Section 2.3.1, 95% confidence ellipses for each distinct class were added to the plot in order to highlight the density of the samples within a single class in addition to the formation of any clusters and/or outliers. In the case of FTIR, no distinct clusters can be identified since the different types of samples are notably overlapping; in addition, several outlying samples for each distinct class, which are located outside the confidence ellipses, can be identified in the plot. In addition, the first two PCs for HPLC do not demonstrate any well-defined clusters, however, they do provide a better separation between fresh and spoiled samples; in this instance, the number of overlapping samples is smaller than FTIR. Data integration of the datasets originating from FTIR and HPLC was performed using GPA and CPCA as described in Section 4.2.5. The geometrically transformed consensus of GPA is depicted in Figure 5-6. The outcome of CPCA – the super scores – is plotted along with the output of GPA. As can be concluded from the figure, both integrated datasets provide a relatively good separation between the fresh and spoiled samples with the exception of a miniscule of outliers. However, semi-fresh samples are once more overlapping in-between the other two classes. 122
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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
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 and 122: Figure 4-12 Distribution plots of t
Page 123 and 124: RBF SVMs Datasets Original %CC Mean
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: 5.2.3.3 High Throughput Liquid Chro
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 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
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
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
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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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