CRANFIELD UNIVERSITY Eleni Anthippi Chatzimichali ...

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Fears over microbiological food safety issues have led to the requirement for a rapid and accurate detection system for microbiologically spoiled or contaminated meat. To date, various methods have been proposed to measure and detect bacterial spoilage in meat such as enumeration methods based on microscopy, ATP bioluminescence and the measurement of electrical phenomena as well as detection methods based on immunological procedures (Ellis et al., 2002). However, these techniques are time-consuming, labour-intensive and generate retrospective information; thus, they cannot be used for on- or at-line monitoring (Ellis et al., 2002; Argyri et al., 2011). Polymerase chain reaction (PCR) techniques may also be investigated; however, the main limitation of these techniques is the high equipment cost, the demand for highly trained staff as well as the risk of cross-contamination (Nicolaou et al., 2011). The research for reliable meat-quality sensors has led to the development of a plethora of rapid, non-invasive, and relatively inexpensive methods based on analytical instrumentation techniques such as Fourier transform infrared (FTIR) spectroscopy and electronic nose technology (Argyri et al., 2010; Panagou et al., 2010). The present study aims to develop an automated, reproducible and quantitative approach that defines the spoilage state of a product objectively. The application of analytical techniques such as the ones presented in Section 1.1.2 in conjunction with multivariate statistical techniques and machine learning algorithms may prove to be an effective, extremely fast and accurate method, which could have practical applications to ensure the quality and safety of meat and meat products. 1.2 Multivariate Analyses and Chemometrics The vast amount of biological information generated by the advanced analytical instruments such as the omics fields, demand appropriate multivariate statistical tools for data analysis. Multivariate analysis can be defined as “the simultaneous statistical analysis of a collection of random variables” (Izenman, 2008). According to Vermuza and Filzmoser (2009), chemometrics may be defined as the “extraction of chemically relevant information out of analytical chemical data by mathematical and statistical tools”. 7
The multivariate techniques applied in the field of chemometrics are conventionally divided into two main categories, namely supervised and unsupervised. Unsupervised methods “attempt to disclose naturally occurring groups and structures within the dataset without previous knowledge of any class assignment” (Alvarez-Ordoñez and Prieto, 2012). They chiefly focus on the discovery of patterns, trends, clusters and/or outliers in the data, and they include techniques such as Principal Component Analysis (PCA) and cluster analysis. On the other hand, supervised learning algorithms “make use of a priori knowledge of classes to guide the characterisation or classification process” (Alvarez-Ordoñez and Prieto, 2012); these algorithms generate prediction models for regression, classification, pattern recognition, or machine learning tasks. Characteristic examples of supervised learning involve Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machines (SVMs), among many others. 1.3 Data pre-treatment Nowadays, the extraction of relevant information from highly heterogeneous datasets constitutes a major challenge (van den Berg et al., 2006). It is well established that prior to the application of any type of data analysis, proper data pre-treatment is crucial for the outcome and the interpretability of the results. Data pre-treatment can make the difference between a useful model and no model at all. Therefore, biological data under investigation are often scaled, centered and/or transformed. The application of pre-treatment techniques may prove to be extremely fruitful, especially under circumstances where the variables span over wide and different ranges. In addition, pre-treatment techniques aim to minimise the influence of disturbing factors such as measurement noise. The selection of chemometrics method to be applied, strongly influences the selection of the data pre-treatment methods. Different techniques focus on different aspects of the data. For instance, clustering algorithms focus on revealing similarity and dissimilarity patterns, whereas PCA attempts to explain the maximum variation based on a few meaningful components. Thus, a certain pre-treatment method may enhance the results of one technique and obscure the results of another. 8
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: 1.1.3 Microbial Spoilage in Meat Sy
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
Page 71 and 72: 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.
Page 235:
Xu, Y. and Goodacre, R. (2012), "Mu
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