Modeling and predicting spoilage of cooked, cured meat products by multivariate analysis (original) (raw)
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Journal of Food Science, 2006
ABSTRACT: In the present study, the spoilage flora of a sliced cooked cured meat product was studied to determine the specific spoilage organism (SSO). The physicochemical changes of the product during its storage in a temperature range of 0 to 12 °C were also studied. Among the primary models used to model the temperature effect on SSO growth, the modified Gompertz described better the experimental data than modified logistic and Baranyi. The derived growth kinetic parameters, such as maximum specific growth rate (μmax) and lag phase duration (LPD), were modeled by using the square root and Arrhenius equation (secondary models). The latter described better the data of μmax and LPD; therefore, this model was chosen for correlating temperature with kinetic parameters. The selection of the best model (primary or secondary) was based on some statistical indices (the root mean square error of residuals of the model, the coefficient of multiple determination, the F-test, the goodness of fit, the bias, and accuracy factor). The validation of the developed model was carried out under constant and dynamic temperature storage conditions. To validate its usefulness to similar products, another sliced cooked cured meat product stored under constant temperature conditions was also used. The log shelf life model was used for shelf life predictions based on the evident (visual defects) or the incipient spoilage (attainment of a certain spoilage level by SSO and/or chemical spoilage index). The possibility for shelf life predictions constitutes a valuable information source for the quality assurance systems of meat industries.
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Consumption of raw or undercooked meat is responsible for 2.3 million foodborne illnesses yearly in Europe alone. The greater part of this illness is associated with beef meat, which is used in many traditional dishes across the world. Beneath the low microbiological quality of beef lies (pathogenic) bacterial contamination during primary production as well as inadequate hygiene operations along the farm-to-fork chain. Therefore, this study seeks to understand the microbiological quality pathway of minced beef processed for fast-food restaurants over three years using an artificial neural network (ANN) system. This simultaneous approach provided adequate precision for the prediction of a microbiological profile of minced beef meat as one of the easily spoiled products with a short shelf life. For the first time, an ANN model was developed to predict the microbiological profile of beef minced meat in fast-food restaurants according to meat and storage temperatures, butcher identifica...
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The aim of the present study was to investigate the evolution of the volatile compounds of aerobically stored sterile pork meat as a consequence of the metabolic activities of inoculated specific spoilage microorganisms. Thus, Pseudomonas fragi, Pseudomonas putida, Lactobacillus sakei and Leuconostoc mesenteroides were inoculated in monocultures, dual cultures and a cocktail culture of all strains on sterile pork meat stored aerobically at 4 and 10 °C. Microbiological and sensory analyses, as well as pH measurements, were performed, along with headspace solid-phase microextraction gas chromatography/mass spectroscopy (headspace SPME–GC/MS) analysis. Data analytics were used to correlate the volatile compounds with the spoilage potential of each stain using multivariate data analysis. The results for the sensory discrimination showed that the volatiles that dominated in spoiled samples consisted mostly of alcohols, ketones and two esters (butyl acetate and ethyl acetate), while at fr...
International Journal of Food Microbiology, 1998
To predict microbial growth during chill storage of a traditional Greek raw sausage, a numerical model was developed and validated. In our novel approach, the specific growth rate of each microbial population was calculated on the basis of the main microbial populations grown in the sausage. In addition, the specific destructive effect of the sausage ecosystem was introduced to evaluate microbial growth. The model was integrated by the Runge-Kutta method and the parameter values were optimised by the least squares method. Fitting of the model to the experimental data derived from four sausage batches stored aerobically at 3 and 12°C successfully described the microbial growth kinetics in the sausage niche. Finally, the parameter values estimated by the fitting of the model on the data set from each batch were used to predict microbial growth in the other batches at both storage temperatures.