Browsing by Author "Argyri, Anthoula A."

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    A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints
    (Elsevier Science B.V., Amsterdam, 2011-06-30T00:00:00Z) Panagou, Efstathios Z.; Mohareb, Fady R.; Argyri, Anthoula A.; Bessant, Conrad M.; Nychas, George-John E.
    A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10, 15, and 20°C) using the dataset presented by Argyri etal. (2010). The performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. The purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. The results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. The PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. The ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. The performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN.
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    Quantifying meat spoilage with an array of biochemical indicators
    (Cranfield University, 2010) Argyri, Anthoula A.; Magan, Naresh; Nychas, George-John E.
    Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. It is crucial to validate and establish new rapid methods for the accurate detection of microbial spoilage of meats. In the current thesis, the microbial association of meat was monitored in parallel with the chemical changes, pH measurements and sensory analysis. Several chemical analytical techniques were applied to explore their dynamics on quantifying spoilage indicators and evaluate the shelf life of meat products. The applied analytical methods used were Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, image analysis, high performance liquid chromatography (HPLC) and gas chromatography/mass spectroscopy (GC/MS). The first component of the study was designed to evaluate the potential of FTIR spectroscopy as a rapid, reagent-less and non-destructive analytical technique in estimating the freshness and shelf life of beef. For this reason, minced beef samples survey from the Greek market), beef fillet samples stored aerobically (0, 5, 10, 15 and 20ºC) and minced beef samples stored aerobically, under modified atmosphere packaging (MAP) and active packaging (0, 5, 10, and 15ºC), were analysed with FTIR. The statistical analysis from the survey revealed that the impact of the market type, the packaging type, the day and the season of purchase had a significant effect on the microbial association of mince. Furthermore, the Principal Components Analysis (PCA) and Factorial Discriminant Analysis (FDA), applied to the FTIR spectral data, showed discrimination of the samples based on freshness, packaging type, the day and season of purchase. The validated overall classification accuracies VCA) were 61.7% for the freshness, 79.2% for the packaging 80.5% for the season and 61.7% for the day of purchase. The shelf life of beef fillets and minced beef was evaluated and correlated with FTIR spectral data. This analysis revealed discrimination of the samples regarding their freshness (VCA 81.6% for the fillets, 76.34% for the mince), their storage temperature (VCA 55.3% and 88.1% for the fillets and mince, respectively) and the packaging type (VCA 92.5% for the mince). Moreover, estimations of the different microbial populations using Partial Least Squares Regression (PLS-R) were demonstrated (e.g. Total viable counts-TVC: RMSE 1.34 for the beef fillets and 0.72 for the mince). Cont/d.
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    ItemOpen Access
    Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks
    (Elsevier, 2009) Argyri, Anthoula A.; Panagou, Efstathios Z.; Tarantilis, P. A.; Polysiou, M.; Nychas, George-John E.
    A machine learning strategy in the form of a multilayer perceptron (MLP) neural network was employed to correlate Fourier transform infrared (FTIR) spectral data with beef spoilage during aerobic storage at chill and abuse temperatures. Fresh beef fillets were packaged under aerobic conditions and left to spoil at 0, 5, 10, 15, and 20 °C for up to 350 hours. FTIR spectra were collected directly from the surface of meat samples, whereas total viable counts of bacteria were obtained with standard plating methods. Sensory evaluation was performed during storage and samples were attributed into three quality classes namely fresh, semi-fresh, and spoiled. A neural network was designed to classify beef samples to one of the three quality classes based on the biochemical profile provided by the FTIR spectra, and in parallel to predict the microbial load (as total viable counts) on meat surface. The results obtained demonstrated that the developed neural network was able to classify with high accuracy the beef samples in the corresponding quality class using their FTIR spectra. The network was able to classify correctly 22 out of 24 fresh samples (91.7%), 32 out of 34 spoiled samples (94.1%), and 13 out of 16 semi-fresh samples (81.2%). No fresh sample was misclassified as spoiled and vice versa. The performance of the network in the prediction of microbial counts was based on graphical plots and statistical indices (bias and accuracy factors, standard error of prediction, mean relative and mean absolute percentage residuals). Results demonstrated good correlation of microbial load on beef surface with spectral data. The results of this work indicated that the biochemical fingerprints during beef spoilage obtained by FTIR spectroscopy in combination with the appropriate machine learning strategy have significant potential for rapid assessment of meat spoilage.