CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Figure 2-1 Mean FTIR spectra for case study 1 in the fingerprint region (1500-1000 cm -1 ) The plot depicts the mean FTIR spectra for each distinct class of shelf life beef fillets (stored in air at 0, 5, 10, 15 and 20°C). 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). 2.2.1.4 High Throughput Liquid Chromatography (HPLC) The analysis was performed using a Jasco (Japan) HPLC equipped with a Model PU-980 Intelligent pump, a Model LG-980-02 ternary gradient unit pump and a MD-910 multi-wavelength detector (Argyri, 2010). The software used for the collection and the processing of the spectra in order to give concentrations of key metabolites was the Jasco Chrompass Chromatography Data system v1.7.403.1. Spectral data were collected from 200 to 600nm; however, chromatogram integration was performed at 210nm and the purity of the peaks was examined through the software using all spectral ranges. Solutions of oxalic, citric, malic, lactic, acetic, formic, tartaric, succinic and propionic acids were used as reference substances, analysed using the same programme, and their spectra were compared with the samples for the identification of the peaks (Argyri, 2010). In total, 52 samples were analysed in duplicate. Based on the provided sensory scores, the HPLC samples consist of 20 fresh (F), 14 semi-fresh (SF) and 18 spoiled (S) samples. 35
DF (Hz) 2.2.1.5 Electronic Nose (e-nose) The volatile profile of beef samples was determined using an electronic nose – the “Libra Nose” – by Technobiochip (Napoli, Italy) (http://www.technobiochip.com/). The instrument consists of an array of eight electronic chemical sensors (EU Patent [EP1505095]), as is depicted in Figure 2-2. In brief, 5g of sample were placed inside a 100 ml volume glass jar; the samples were left at room temperature (20C) to enhance desorption of volatile and semi-volatile compounds from the meat into the gas phase. The headspace was pumped over the sensors of the electronic nose and the generated signal was continuously and in real time recorded to a personal computer. In total, measurements of 36 samples with their replicates using eight sensors were provided; however, due to instability issues, one of the eight sensors had to be removed hence leading to a total of seven sensors. Based on the provided sensory scores, the 36 samples of e-nose consist of 10 fresh (F), 7 semi-fresh (SF) and 19 spoiled (S) samples. 500 -500 0 1 153 305 457 609 761 913 1065 1217 1369 1521 1673 1825 1977 2129 2281 2433 2585 2737 2889 3041 3193 -1000 -1500 -2000 -2500 Cycles Figure 2-2 Sampling with Libra e-nose The picture illustrates the responses of the eight sensors of the Libra electronic nose during sampling. The figure was provided by Argyri et al. (personal contact). 36
Page 1 and 2: CRANFIELD UNIVERSITY Eleni Anthippi
Page 3 and 4: ABSTRACT Muscle foods such as meat,
Page 5 and 6: TABLE OF CONTENTS ABSTRACT ........
Page 7 and 8: 6.2.4 The iWebPlots package .......
Page 9 and 10: Figure 3-12 Speedup produced by the
Page 11 and 12: Figure 6-9 Sweave example for the d
Page 13 and 14: TABLE OF EQUATIONS Equation 1 Mean-
Page 15 and 16: RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
Page 17 and 18: Figure 1-1 Evolution from molecular
Page 19 and 20: 1.1.2.1 Fourier Transform Infrared
Page 21 and 22: 1.1.3 Microbial Spoilage in Meat Sy
Page 23 and 24: The multivariate techniques applied
Page 25 and 26: 1.4 Multivariate Analysis: Unsuperv
Page 27 and 28: 1.4.2 Cluster Analysis Cluster anal
Page 29 and 30: 1.5 Multivariate Analysis: Supervis
Page 31 and 32: In any linearly separable binary da
Page 33 and 34: Where ( ) are the Lagrange multipli
Page 35 and 36: Every kernel is characterised by a
Page 37 and 38: The “one-against-all” approach
Page 39 and 40: Furthermore, metrics such as the bi
Page 41 and 42: 1.6.3 Leave-One-Out Cross-Validatio
Page 43 and 44: In such cases, the model becomes qu
Page 45 and 46: 1.8 Aims and objectives The overall
Page 47 and 48: 2 Development of the multivariate a
Page 49: Sensory panel scores were of the hi
Page 53 and 54: Figure 2-3 Data intersection The fi
Page 55 and 56: 3. Validation Techniques In the cas
Page 57 and 58: Figure 2-4 The process of construct
Page 59 and 60: 2.3 Results and Discussion 2.3.1 Pr
Page 61 and 62: (b) HPLC data (c) e-nose data Figur
Page 63 and 64: In the case of FTIR, linear classif
Page 65 and 66: 2.3.2.2 Class Prediction Accuracies
Page 67 and 68: Figure 2-7 Class prediction rates o
Page 69 and 70: The topology of the three-dimension
Page 71 and 72: Figure 2-9 Three-dimensional error
Page 73 and 74: Furthermore, a thorough comparison
Page 75 and 76: In the master/slave architecture, a
Page 77 and 78: 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
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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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Figure 4-9 Class prediction rates o
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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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Figure 5-8 Class prediction rates o
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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
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(a) GPA (b) CPCA Figure 5-22 The co
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The classification results of the f
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Figure 5-24 Classification Results
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Figure 5-25 Class prediction rates
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The permutation results for the dat
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Figure 5-28 Distribution plots of t
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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.
Page 235:
Xu, Y. and Goodacre, R. (2012), "Mu
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