CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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1.1.2.2 High Throughput Liquid Chromatography (HPLC) High-performance liquid chromatography (HPLC) is a chromatographic technique used to separate a mixture of chemical compounds. It is mainly used in biochemistry and analytical chemistry to identify, quantify as well as purify the individual components in a mixture. The HPLC instrument consists of a solvent reservoir, transfer line with frit, high-pressure pump, sample injection device, column, detector, and data acquisition, usually together with data evaluation (Meyer, 2013) 1.1.2.3 Electronic Nose (e-nose) An electronic nose (e-nose) is an instrument applied for the rapid non-destructive detection and analysis of microbial volatile compounds. The electronic noses attempt to mimic the human organoleptic olfactory interpretation (Persuad and Dodd, 1982). The instrument consists of an array of chemical gas sensors, which are capable of detecting and recognising simplex or complex odours. Electronic noses have shown great promise in the field of food analysis as a means of evaluating freshness and investigating shelf life. E-noses have become increasingly popular due to the fact that they resemble human sensory evaluation, but also since they are rapid, low-cost non-destructive techniques. Even so, the repeatability with electronic noses has been questioned since they present instabilities due to severe instrumental drift (Ellis and Goodacre, 2001). 1.1.2.4 Raman Spectroscopy Raman spectroscopy is also a non-destructive method that can be considered to be complementary to FTIR spectroscopy. Both Raman and FTIR are powerful metabolic fingerprinting methods as they reflect accurately the phenotype of a sample, including changes to its metabolism (Nicolaou et al., 2011). The major advantage of Raman spectroscopy over FTIR is the fact that the contribution from water is very small and thus can be used directly on food without recourse to ATR (Argyri et al., 2013). 5
1.1.3 Microbial Spoilage in Meat Systems biology has gained in importance in food science and the food industry due to an increasing focus on food for better health and the demand for products of consistently high quality (Ellis et al., 2002; Hassani et al., 2010). Out of all foods that are a vital part of human diet, meat has been described as the most perishable of all. Muscle foods such as meat or poultry become unacceptable to the consumer when organoleptic changes occur due to spoilage (Ellis et al., 2002). Spoilage can be defined as “any change in a food product that renders it unacceptable to the consumer from a sensory point of view” (Gram et al., 2002; Ercolini et al., 2006) Meat spoilage may be the result of a plethora of intrinsic and extrinsic factors, the most significant of which is microbial activity (Gram et al., 2002). Even though changes of food substances during storage may be the result of endogenous enzymatic processes within muscle tissue post-mortem, it is generally accepted that detectable organoleptic spoilage is a result of decomposition and the formation of metabolites caused by the growth of microorganisms (Ellis and Goodacre, 2001; Ellis et al., 2002). These organoleptic characteristics usually include the development of off-odours and off-flavours, the formation of slime in addition to any changes in the appearance such as discoloration; thus, consumers consider the meat as being undesirable. Due to its moist highly nutritious surface, meat stored at between -1 and 25°C favours the growth of a wide range of spoilage bacteria. Under aerobic conditions, spoilage organisms that belong primarily to the genus Pseudomonas attach more rapidly to meat surfaces than other spoilage bacteria (Ellis et al., 2002). The organoleptic changes may vary depending on the microbial association contaminating the meat and the conditions under which it is stored. The development of organoleptic spoilage is related to microbial consumption of meat nutrients such as sugars and free amino acids, and the release of undesired volatile metabolites (Ercolini et al., 2006). 6
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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: 1.1.2.1 Fourier Transform Infrared
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 and 50: Sensory panel scores were of the hi
Page 51 and 52: DF (Hz) 2.2.1.5 Electronic Nose (e-
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
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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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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.
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Xu, Y. and Goodacre, R. (2012), "Mu
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