CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Datasets FTIR HPLC Fresh (F) 66 26 Semi-Fresh (SF) 26 12 Spoiled (S) 95 38 Total #Samples 187 76 Total #Variables 1869 16 Total #Variables (fingerprint region) 259 – Table 5 The sizes and data composition of standalone datasets from case study 2 prior to analysis 5.2.2 Case study 3: “Survey of minced beef” 5.2.2.1 Sample Preparation A detailed explanation of the experimental techniques and the methodology of case study 3 can be found in Argyri (2010, 2011, 2013). In brief, fresh minced beef (pH 5.5) was purchased from a meat market in Athens (Greece) and transported under refrigeration to the laboratory within 30 minutes, where it was stored at 1°C for 1-2 hours. Two portions of 75 g were placed onto Styrofoam trays, where one was used for chemical and microbiological analysis, and the other for sensory assessment. Two packaging conditions were applied on the meat samples of case study 3: air and modified atmospheres (MAP) (40% CO 2 30% O 2 30% N 2 ). Subsequently, the samples were stored aerobically and under MAP at 5°C until spoilage was pronounced (intense discoloration and presence of off-odours). In case study 3, two analytical techniques were employed; namely, FTIR and Raman spectroscopy. 115
5.2.2.2 Fourier Transform Infrared Spectroscopy (FTIR) Similar to case study 1, the FTIR dataset was generated from measurements based on a thin slice of the aerobic upper surface of the beef fillets that was excised and placed in intimate contact with the crystal (Argyri, 2010; Panagou et al., 2010; Argyri et al., 2013). The spectrometer was programmed to collect spectra in the mid-IR range between 4000 and 400cm -1 . Out of the initial 1,739 variables, only the spectra that reveal the metabolic fingerprint of spoilage, between the ranges of 1500 and 1000cm -1 , were extracted and used for classification purposes. Thus, the final FTIR dataset consists of 150 samples of minced beef with their replicates and 259 variables. Based on the provided sensory scores, the 150 samples of FTIR consist of 28 fresh (F), 52 semi-fresh (SF) and 70 spoiled (S) samples. Figure 5-2 shows the mean FTIR spectra of the samples of minced beef per each distinct class in the fingerprint region between 1500 and 1000 cm -1 . Figure 5-2 Mean FTIR spectra for case study 3 in the fingerprint region (1500-1000 cm -1 ) The plot depicts the mean FTIR spectra of minced beef samples (stored aerobically and under MAP at 5°C) per each distinct class. The spectral region between 1500 and 1000cm -1 reveals the metabolic fingerprint of spoilage. Colour representation is used to identify the three classes as determined by the relevant sensory scores: fresh (red colour), semi-fresh (orange colour) and spoiled (green colour). 116
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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
Page 79 and 80: Figure 3-3 The steps of the Nelder-
Page 81 and 82: 3.3 Results and Discussion 3.3.1 Li
Page 83 and 84: 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: Figure 5-1 Mean FTIR spectra for ca
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 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
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5.4 Comparison of the individual ca
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Figure 5-32 Investigating the commo
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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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