Browsing by Author "Nychas, George-John E."
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Item Open Access Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools(Elsevier, 2020-06-03) Manthou, Evanthia; Lago, Sergio-Llaneza; Dagres, Evaggelos; Lianou, Alexandra; Tsakanikas, Panagiοtis; Panagou, Efstathios Z.; Anastasiadi, Maria; Mohareb, Fady; Nychas, George-John E.Recently, rapid, non-invasive analytical methods relying on vibrational spectroscopy and hyper/multispectral imaging, are increasingly gaining popularity in food science. Although such instruments offer a promising alternative to the conventional methods, the analysis of generated data demands complex multidisciplinary approaches based on data analytics tools utilization. Therefore, the objective of this work was to (i) assess the predictive power of different analytical platforms (sensors) coupled with machine learning algorithms in evaluating quality of ready-to-eat (RTE) pineapple (Ananas comosus) and (ii) explore the potentials of The Unscrambler software and the online machine-learning ranking platform, SorfML, in developing the predictive models required by such instruments to assess quality indices. Pineapple samples were stored at 4, 8, 12 °C and dynamic temperatures and were subjected to microbiological (total mesophilic microbial populations, TVC) and sensory analysis (colour, odour, texture) with parallel acquisition of spectral data. Fourier-transform infrared, fluorescence (FLUO) and visible sensors, as well as Videometer instrument were used. For TVC, almost all the combinations of sensors and Partial-least squares regression (PLSR) algorithm from both analytics tools reached values of root mean square error of prediction (RMSE) up to 0.63 log CFU/g, as well as the highest coefficient of determination values (R2). Moreover, Linear Support Vector Machine (SVM Linear) combined with each one of the sensors reached similar performance. For odour, FLUO sensor achieved the highest overall performance, when combined with Partial-least squares discriminant analysis (PLSDA) in both platforms with accuracy close to 85%, but also with values of sensitivity and specificity above 85%. The SVM Linear and MSI combination also achieved similar performance. On the other hand, all models developed for colour and texture showed poor prediction performance. Overall, the use of both analytics tools, resulted in similar trends concerning the feasibility of the different analytical platforms and algorithms on quality evaluation of RTE pineapple.Item Open Access Behavior of Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella Typhimurium in teewurst, a raw spreadable sausage(Elsevier, 2009-04-15) Dourou, Dimitra; Porto-Fett, Anna C. S.; Shoyer, Brad; Call, Jeffrey E.; Nychas, George-John E.; Illg, Ernst K.; Luchansky, John B.The fate of Listeria monocytogenes, Salmonella Typhimurium, or Escherichia coli O157:H7 were separately monitored both in and on teewurst, a traditional raw and spreadable sausage of Germanic origin. Multi-strain cocktails of each pathogen (ca. 5.0 log CFU/g) were used to separately inoculate teewurst that was subsequently stored at 1.5, 4, 10, and 21 °C. When inoculated into commercially-prepared batter just prior to stuffing, in general, the higher the storage temperature, the greater the lethality. Depending on the storage temperature, pathogen levels in the batter decreased by 2.3 to 3.4, ca. 3.8, and 2.2 to 3.6 log CFU/g for E. coli O157:H7, S. Typhimurium, and L. monocytogenes, respectively, during storage for 30 days. When inoculated onto both the top and bottom faces of sliced commercially-prepared finished product, the results for all four temperatures showed a decrease of 0.9 to 1.4, 1.4 to 1.8, and 2.2 to 3.0 log CFU/g for E. coli O157:H7, S. Typhimurium, and L. monocytogenes, respectively, over the course of 21 days. With the possible exceptions for salt and carbohydrate levels, chemical analyses of teewurst purchased from five commercial manufacturers revealed only subtle differences in proximate composition for this product type. Our data establish that teewurst does not provide a favourable environment for the survival of E. coli O157:H7, S. Typhimurium, or L. monocytogenes inoculated either into or onto the product.Item Open Access 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.Item Open Access Detection of meat adulteration using spectroscopy-based sensors(MDPI, 2021-04-15) Fengou, Lemonia-Christina; Lianou, Alexandra; Tsakanikas, Panagiοtis; Mohareb, Fady; Nychas, George-John E.Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.Item Open Access Growth Of Salmonella Enteritidis And Salmonella Typhimurium In The Presence Of Quorum Sensing Signalling Compounds Produced By Spoilage And Pathogenic Bacteria(Elsevier Science B.V., Amsterdam, 2014-06-10) Dourou, Dimitra; Ammor, Mohammed Salim; Skandamis, Panagiotis N.; Nychas, George-John E.The effect of acylated homoserine lactones (AHLs) and autoinducer-2 (AI-2) signalling compounds present in the cell-free culture supernatants (CFS), of Pseudomonas aeruginosa, Yersinia enterocolitica-like GTE 112, Serratia proteamaculans 00612, Y. enterocolitica CITY650 and Y. enterocolitica CITY844, on the growth of two Salmonella Enteritidis and two S. Typhimurium strains was assessed though monitoring of changes in conductance of the medium. Detection times (Tdet), area and slope of conductance curves were recorded. Except for P. aeruginosa 108928, which was not found to produce AI-2, all other strains produced both AHLs and AI-2. Thereafter, aliquots (20% in the final volume) of these CFS were transferred into NZ Amine broth inoculated with ca. 103CFU/ml of stationary phase cultures of each Salmonella strain. While the CFS of P. aeruginosa induced a shorter detection time, i.e. acceleration of the metabolic activity, the CFS of the other microorganisms increased the detection time of Salmonella strains compared to control samples (i.e. without CFS). Results indicate that the growth of Salmonella may be affected by the presence of Quorum sensing (QS) signalling compounds and/or other novel signals existing in CFS, produced by other bacterial species and confirm the complexity of bacterial communication.Item Open Access Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling(Elsevier, 2015-04-20) Mohareb, Fady R.; Iriondo, Maite; Doulgeraki, Agapi I.; van Hoek, Angela; Aarts, Henk; Cauchi, Michael; Nychas, George-John E.While current food science research mainly focuses on microbial changes in food products that lead to foodborne illnesses, meat spoilage remains as an unsolved problem for the meat industry. This can result in important economic losses, food waste and loss of consumer confidence in the meat market. Gram-negative bacteria involved in meat spoilage are aerobes or facultative anaerobes. These represent the group with the greatest meat spoilage potential, where Pseudomonas tend to dominate the microbial consortium under refrigeration and aerobic conditions. Identifying stress response genes under different environmental conditions can help researchers gain an understanding of how Pseudomonas adapts to current packaging and storage conditions. We examined the gene expression profile of Pseudomonas putida KT2440, which plays an important role in the spoilage of meat products. Gene expression profiles were evaluated to select the most differentially expressed genes at different temperatures (30 °C and 10 °C) and decreasing glucose concentrations, in order to identify key genes actively involved with the spoilage process. A total of 739 and 1269 were found to be differentially expressed at 30 °C and 10 °C respectively; of which 430 and 568 genes were overexpressed, and 309 and 701 genes were repressed at 30 °C and 10 °C respectively.Item Open Access Lactic acid bacteria population dynamics during minced beef storage under aerobic or modified atmosphere packaging conditions(Elsevier, 2010-12) Doulgeraki, Agapi I.; Paramithiotis, Spiros; Kagkli, Dafni-Maria; Nychas, George-John E.A total of 266 lactic acid bacteria (LAB) have been isolated from minced beef stored at 0, 5, 10 and 15 °C aerobically and under modified atmosphere packaging consisting of 40% CO2–30% O2–30% N2 in the presence MAP (+) and absence MAP (−) of oregano essential oil. Sequencing of their 16S rRNA gene along with presence of the katA gene demonstrated dominance of the LAB microbiota by Leuconostoc spp. during aerobic storage at 5, 10 and 15 °C, as well as during MAP (−) and MAP (+) storage at 10 and 15 °C; Lactobacillus sakei prevailed during aerobic storage at 0 °C, as well as at MAP (−) and MAP (+) storage at 0 and 5 °C. The sporadic presence of other species such as Leuconostoc mesenteroides, Weisella viridescens, Lactobacillus casei and Lactobacillus curvatus has also been determined. Pulsed-Field Gel Electrophoresis of high molecular weight genomic DNA revealed the dynamics of the isolated LAB strains. Prevalence of Leuconostoc spp. was attributed to one strain only. On the other hand, packaging conditions affected Lb. sakei strain spoilage dynamics.Item Open Access Monitoring the succession of bacterial communities during storage of raw meat(Cranfield University, 2010) Doulgeraki, Agapi I.; Aldred, David; Magan, Naresh; Nychas, George-John E.Fresh meat is exposed to various factors which cause microbiological contamination during handling, processing, packaging and storage. Furthermore, the storage conditions applied may affect the microbial association of the product and consequently the spoilage process. Therefore, the purpose and importance of this study was to identify areas that should be addressed to monitor the succession of bacterial communities during storage of raw meat. The improvement of the microbiological quality and safety of meat was also studied. Cont/d.Item Open Access Novel approaches for food safety management and communication(Elsevier, 2016-06-25) Nychas, George-John E.; Panagou, Efstathios Z.; Mohareb, Fady R.The current safety and quality controls in the food chain are lacking or inadequately applied and fail to prevent microbial and/or chemical contamination of food products, which leads to reduced confidence among consumers. On the other hand to meet market demands food business operators (producers, retailers, resellers) and regulators need to develop and apply structured quality and safety assurance systems based on thorough risk analysis and prevention, through monitoring, recording and controlling of critical parameters covering the entire product's life cycle. However the production, supply, and processing sectors of the food chain are fragmented and this lack of cohesion results in a failure to adopt new and innovative technologies, products and processes. The potential of using information technologies, for example, data storage, communication, cloud, in tandem with data science, for example, data mining, pattern recognition, uncertainty modelling, artificial intelligence, etc., through the whole food chain including processing within the food industry, retailers and even consumers, will provide stakeholders with novel tools regarding the implementation of a more efficient food safety management system.Item Open Access Pathogen responses in foods : underestimated ecophysiological factors(Cranfield University, 2009-01) Dourou, Dimitra; Aldred, David; Nychas, George-John E.Accurate prediction of the fate of microbial foodborne pathogens in foods is of great concern for anyone involved in the food chain. Factors that may influence microbial responses in foods and food environments, such as food structure and composition, microbial interactions and mode of growth were identified and assessed in the present study. The fate of Listeria monocytogenes, Salmonella Typhimurium and Escherichia coli O157:H7 was monitored both in and on teewurst, a raw spreadable sausage, at different storage temperatures. Regardless of the storage temperature and inoculation type, pathogen numbers decreased during storage. The increase of endogenous lactic acid bacteria and the concomitant reduction of pH mostly accounted for this reduction. The inactivation of all three pathogens inoculated into batter or onto slices varied considerably among trials possibly due to variations in the initial batch-to-batch levels of lactic acid microflora and the associated microbial interactions. The effect of structure, composition and microbial interactions on the growth kinetics of L. monocytogenes was evaluated in different growth substrates, including broth, agar, sterile meat blocks, naturally contaminated meat blocks and minced meat. The growth responses of L. monocytogenes were significantly different in the different growth media and food products tested. These differences were more pronounced at low temperatures. The validation of a model based on data from broth against the observed growth of the pathogen in the rest of the tested media showed that broth models may result in significant prediction errors. The potential for mono- or multi-strain cultures of Escherichia coli O157:H7 to attach and form biofilm in combinations of food-contact surfaces, growth substrates and storage temperatures was examined. The susceptibility of biofilms to sanitizers was also evaluated. Attachment and biofilm formation was strain dependent. The presence of food residues (liquid or solid) facilitated the attachment/transfer of E. coli O157:H7 on food-contact surfaces. At moderately cold temperatures culture broth was more conducive to subsequent growth. At chill temperatures the presence of natural microflora in liquid residues enhanced further attachment of the pathogen. Biofilms were less susceptible to sanitation treatments as compared to planktonic cells. Biofilm cells surviving sanitation were able to survive and present slight increases at refrigeration and abuse temperatures, respectively, in cross-contaminated ground meat. Acylated homoserine lactones (AHLs) and autoinducer-2 (AI-2) signalling molecules in the cell-free supernatants of Pseudomonas aeruginosa, Yersinia enterocolitica-like, Serratia proteamaculans and a mixture of two Yersinia enterocolitica strains were found to affect the growth kinetics of two Salmonella Enteritidis and S. Typhimurium strains, respectively. P. aeruginosa synthesized quorum sensing signals that accelerated the metabolic activity of Salmonella strains. All other quorated bacteria tested had a negative effect on both initiation of growth and metabolic activity. The effect seems to be strain and QS signal dependent.Item Open Access 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.Item Open Access Quorum sensing : understanding the role of bacteria in meat spoilage(Cranfield University, 2010) Blana, Vasiliki A; Magan, Naresh; Nychas, George-John E.Quorum sensing is a fundamental process to all of microbiology since it is ubiquitous in the bacterial world, where bacterial cells communicate with each other using low molecular weight signal molecules called autoinducers. Despite the fact that quorum sensing regulates numerous bacterial behaviours, very few studies have addressed the role of this phenomenon in foods. The microbial association of beef consists mainly of pseudomonads, Enterobacteriaceae, Brochothrix thermosphacta and lactic acid bacteria as revealed by minced beef samples purchased from retail shops, which fluctuates according to the storage conditions. Certain members of the microbial association, which are considered to produce signal molecules, have been found to be major contributors to meat spoilage. Pseudomonas fragi and Enterobacteriaceae strains, i.e., Hafnia alvei and Serratia liquefaciens are among the most common quorum sensing signal producers recovered from various food environments. Cont/d.Item Open 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.Item Open Access Spoilage assessment of chicken breast fillets by means of Fourier transform Infrared spectroscopy and Multispectral Image Analysis(Elsevier, 2021-02-25) Spyrelli, Evgenia D.; Ozcan, Onur; Mohareb, Fady; Panagou, Efstathios Z.; Nychas, George-John E.The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n=215) were conducted at 0, 5, 10, and 15 oC for up to 480 h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast filletsItem Open Access Table olives volatile fingerprints: Potential of an electronic nose for quality discrimination.(Elsevier, 2008-09-25) Panagou, Efstathios Z.; Sahgal, Natasha; Magan, Naresh; Nychas, George-John E.In the present work, the potential of an electronic nose to differentiate the quality of fermented green table olives based on their volatile profile was investigated. An electronic gas sensor array system comprising a hybrid sensor array of 12 metal oxide and 10 metal ion-based sensors was used to generate a chemical fingerprint (pattern) of the volatile compounds present in olives. Multivariate statistical analysis and artificial neural networks were applied to the generated patterns to achieve various classification tasks. Green olives were initially classified into three major classes (acceptable, unacceptable, marginal) based on a sensory panel. Multivariate statistical approach showed good discrimination between the class of unacceptable samples and the classes of acceptable and marginal samples. However, in the latter two classes there was a certain area of overlapping in which no clear differentiation could be made. The potential to discriminate green olives in the three selected classes was also evaluated using a multilayer perceptron (MLP) neural network as a classifier with an 18–15–8–3 structure. Results showed good performance of the developed network as only two samples were misclassified in a 66-sample training dataset population, whereas only one case was misclassified in a 12-sample test dataset population. The results of this study provide promising perspectives for the use of a low-cost and rapid system for quality differentiation of fermented green olives based on their volatile profile.