Logistic modeling to predict the minimum inhibitory concentration (MIC) of olive leaf extract (OLE) against Listeria monocytogenes (original) (raw)
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Foodborne Pathogens and Disease, 2012
In this study, we studied the effects of some plant hydrosols obtained from bay leaf, black cumin, rosemary, sage, and thyme in reducing Listeria monocytogenes on the surface of fresh-cut apple cubes. Adaptive neurofuzzy inference system (ANFIS), artificial neural network (ANN), and multiple linear regression (MLR) models were used for describing the behavior of L. monocytogenes against the hydrosol treatments. Approximately 1-1.5 log CFU/g decreases in L. monocytogenes counts were observed after individual hydrosol treatments for 20 min. By extending the treatment time to 60 min, thyme, sage, or rosemary hydrosols eliminated L. monocytogenes, whereas black cumin and bay leaf hydrosols did not lead to additional reductions. In addition to antibacterial measurements, the abilities of ANFIS, ANN, and MLR models were compared with respect to estimation of the survival of L. monocytogenes. The root mean square error, mean absolute error, and determination coefficient statistics were used as comparison criteria. The comparison results indicated that the ANFIS model performed the best for estimating the effects of the plant hydrosols on L. monocytogenes counts. The ANN model was also effective; the MLR model was found to be poor at estimating L. monocytogenes numbers.
The Inhibitory Effect of Plant Extracts on Growth of the Foodborne Pathogen, Listeria monocytogenes
Antibiotics
Listeria monocytogenes is a foodborne pathogen responsible for about 1600 illnesses each year in the United States (US) and about 2500 confirmed invasive human cases in European Union (EU) countries. Several technologies and antimicrobials are applied to control the presence of L. monocytogenes in food. Among these, the use of natural antimicrobials is preferred by consumers. This is due to their ability to inhibit the growth of foodborne pathogens but not prompt negative safety concerns. Among natural antimicrobials, plant extracts are used to inactivate L. monocytogenes. However, there is a large amount of these types of extracts, and their active compounds remain unexplored. The aim of this study was to evaluate the antibacterial activity against L. monocytogenes of about 800 plant extracts derived from plants native to different countries worldwide. The minimal inhibitory concentrations (MICs) were determined, and scanning electron microscopy (SEM) was used to verify how the pla...
International Journal of Food Microbiology
Growth, growth boundary and inactivation models have been extensively developed in predictive microbiology and are commonly applied in food research nowadays. Few studies though report the development of models which encompass all three areas together. A tiered modelling approach, based on the Gamma hypothesis, is proposed here to predict the behaviour of Listeria. Datasets of Listeria spp. behaviour in laboratory media, meat, dairy, seafood products and vegetables were collected from literature, unpublished sources and from the databases ComBase and Sym'Previus. The explanatory factors were temperature, pH, water activity, lactic and sorbic acids. For the growth part, 697 growth kinetic datasets were fitted. The estimated growth rates and 2021 additional growth primary datasets were used to fit the secondary growth models. In a second step, the fitted model was used to predict the growth/no-growth boundary. For the inactivation modelling phase, 535 inactivation curves were used. Gamma models with and without interactions between the explanatory factors were used for the growth and boundary models. The correct prediction percentage (predicted growth when growth is observed + predicted inactivation when inactivation is observed) varied from 62% to 81% for the models without interactions, and from 85% to 87% for the models with interactions. The median error for the predicted population size was less than 0.34 log 10 (CFU/mL) for all models. The kinetics of inactivation were fitted with modified Weibull primary models and the estimated bacterial resistance was then modelled as a function of the explanatory factors. The error for the predicted microbial population size was less than 0.71 log 10 (CFU/mL) with a median value of less than 0.21 for all foods. The model enables the quantification of the increase or decrease in the bacterial population for a given formulation or storage condition. It might also be used to optimise a food formulation or storage condition in the case of a targeted increase or decrease of the bacterial population.
Medycyna Weterynaryjna, 2016
Response surface methodology was used to optimize conditions (e.g., Thyme oil concentration [0.0–0.57%] and storage temperature [0.0-14.14ºC]) for inhibiting the growth of L. monocytogenes (log cfu/g) in ground meat. Additionally, the effect of the variables; namely, temperature and concentration on µmax (maximum specific growth rate, ln cfu/g/h) values was also evaluated using a proposed combined model. The best fitting second order polynomial models were developed for each response using multiple linear regression analysis with backward elimination regression (BER) procedure. In this paper, multi-response surfaces using desirability function approaches were successfully applied to determine optimum operating conditions. Under these optimum treatment and storage parameters, L. monocytogenes populations at hours 6, 24, 48, 72 and 96 were 6.12, 5.96, 5.88, 5.81 and 5.41 log cfu/g and 0.001 ln cfu/g/h, respectively and μmax value 0.001 ln cfu/g/h. At the end, the proposed combined mod...
Assessment of the Antimicrobial Activity of Olive Leaf Extract Against Foodborne Bacterial Pathogens
Frontiers in microbiology, 2017
Olive leaf extract (OLE) has been used traditionally as a herbal supplement since it contains polyphenolic compounds with beneficial properties ranging from increasing energy levels, lowering blood pressure, and supporting the cardiovascular and immune systems. In addition to the beneficial effects on human health, OLE also has antimicrobial properties. The aim of this work was to investigate the antimicrobial effect of OLE against major foodborne pathogens, including Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella Enteritidis. Our results demonstrated that at a concentration of 62.5 mg/ml, OLE almost completely inhibited the growth of these three pathogens. In addition, OLE also reduced cell motility in L. monocytogenes, which correlated with the absence of flagella as shown by scanning electron microscopy. Moreover, OLE inhibited biofilm formation in L. monocytogenes and S. Enteritidis. Taken together, OLE, as a natural product, has the potential to be used as an ...
Journal of Food Protection, 2005
Organic acid salts including sodium lactate, sodium diacetate, potassium benzoate, potassium sorbate, and their combinations were assessed as potential inhibitors of Listeria monocytogenes growth on frankfurters. Predictive models for L. monocytogenes growth on frankfurters treated with these salts were compared to select a proper L. monocytogenes growth curve model under these conditions. Sigmoidal equations, including logistic and Gompertz equations, are widely used to describe bacterial growth. In this study, the reparameterized Gompertz model provided a better fit to the L. monocytogenes growth data compared with the other models that were included in this study. Rather than a fixed value for the maximum number of organisms, the reparameterized Gompertz model allows this quantity to be estimated from the data to determine the effect, if any, of the treatments on maximum population density. This information is expected to improve practical methodology for hazard characterization ...
Sensitivity Analysis applied to a Listeria monocytogenes exposure assessment model
2009
1 General regression neural network model for growth of Salmonella serotypes on chicken skin for use in risk assessment 2 Design of challenge testing experiments to assess the variability of microbial behaviors in foods 3 Flexible querying of Web data for predictive modeling of risk in food 4 An integrated model for predictive microbiology and simultaneous determination of lag phase duration and exponential growth rate. (not submitted) 5 An artificial neural networks approach for the rapid detection of the microbial spoilage of beef fillets based on Fourier Transform Infrared Spectroscopy data 6 Concept for the implementation of a generic model for remaining shelf life prediction in meat supply chains Technical Session 2 Yeast, Mold and Spoilage Modeling 7 Modeling the effect of temperature and water activity on the growth boundaries of Byssochlamys fulva 8 Distributions of the germination time of Aspergillus flavus, Penicillium expansum and P. chrysogenum conidia depend on storage conditions (not submitted) 9 Modeling the growth/no growth interface of Zygosaccharomyces bailii in a viscoelastic food model system 30 Quantification of inhibition of Listeria monocytogenes by organic acids/pH relevant to semi-hard Dutch cheese (not submitted) 31 Modelling microbial competition in foods. Application to the behaviour of Listeria monocytogenes and lactic acid flora in diced bacon 32 Determination of the kinetic parameters for Campylobacter jejuni under dynamic conditions. (not submitted) 33 Bayesian modelling of Clostridium perfringens growth in food products 34 Introducing a zero-modified negative binomial regression for estimating the effect of chilling on Escherichia coli counts from Irish beef carcasses 35 Use of fish shelf life prediction (FSLP) software for monitoring fresh turbot quality in the logistic chain 36 Probabilistic modeling of Listeria monocytogenes behaviour in diced bacon along the manufacture process chain Technical Session 6 New Applications and Neural Networks-Part 2 37 The effect of singular and duplicate plating on the accuracy of estimating low numbers of microorganisms in food (not submitted) 38 Estimating undetectably low post-pasteurization recontamination levels of milk with pathogens using surrogate microbial variables (not submitted) 39 Detection and identification of Acid-Lactic bacteria in an isolated system with near-infrared spectroscopy and multivariate regression modeling 40 Empirical meta-modelling of Salmonella Typhimurium at the farm level of the pork production chain 41 Application of network science to analyse the proteome of Escherichia coli during the lag phase under acid stress (not submitted) 42 Is the Bigelow z-concept consistent with non-log-linear inactivation models? (not submitted) 43 Memory embedded structures of artificial neural networks: limitations and constraints in predictive modeling in foods Technical Session 7 Application of models to food commodities (e.g. seafood, meat, produce, beverages)-Part 2 44 Development and validation of predictive models for the growth and survival of Vibrio vulnificus in post harvest shellstock oysters 45 Development of predictive models for Listeria monocytogenes in selected refrigerated ready-to-eat foods 46 Modelling the kinetics of Listeria monocytogenes on frankfurters and other ready-to-eat meat products from manufacturing to consumption. 47 Predicting growth and growth boundary of Listeria monocytogenes-an international validation study with processed meat and seafood products 48 Predicting Staphylococcus aureus in the dairy chain 49 Introducing stochasticity in predictive modelling of Salmonella Typhimurium at the farm level of the pork production chain 50 The potential of end-products metabolites on predicting the shelf life of minced beef stored under aerobic and modified atmosphere with or without the effect of essential oils Technical Session 8 Cross-Contamination; Microbial Competition Modeling; Model Performance and Validation-Miscellaneous 51 Risking more by modelling cocktail or strain? 52 Mathematical modeling the cross-contamination of food pathogens on the surface of ready-to-eat meats while slicing 53 Modelling the response of the kinetics of the arginine deaminase pathway of Lactobacillus sakei CTC 494 to acid stress 54 Comparison of two optical density methods and plate counts for growth parameter estimation (not submitted) 55 Relationship between cellular esterase activity and physiological state of stressed Listeria monocytogenes cells Technical Session 9 Risk Assessment 56 Application of risk evaluation techniques to achieve a food safety objective for Listeria monocytogenes and Salmonella spp. in a ready-to-eat meat 57 The use of meta-analytical tools in risk assessment modeling for food safety 58 A preliminary consumer risk assessment model of Salmonella in Irish pork sausages: transport and home refrigeration modules 59 Predictive microbiology models vs. modeling microbial growth within Listeria monocytogenes risk assessments: What gap? What impact? 60 Sensitivity analysis applied to a Listeria monocytogenes exposure assessment model 61 Developing a predictive model for quantifying the risk associated with infactory Listeria monocytogenes recontamination and to identify suitable management options to reduce it. 62 Development of an online predictive modeling resource for food safety risk analysis decision making 63 Accounting for diversity of food borne pathogens. Cardinal growth parameters of the Bacillus cereus genetic groups and consequences for risk assessment 64 A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing 65 Use of time temperature indicators as a risk management tool for Listeria monocytogenes in ready-to-eat foods (not submitted) Technical Session 10 Non-Thermal and Thermal Inactivation 66 Application of QMRA to go beyond safe harbors in thermal processes. Part 1: introduction and framework 67 Application of QMRA to go beyond safe harbors in thermal processes. Part 2: quantification and examples 68 Quantification of the effect of culturing temperature on the salt-induced heat resistance of mesophilic and psychrotolerant Bacillus strains (not submitted) 69 Estimating probability of undetected failure of pasteurization process control using Fault Tree Analysis (not submitted) 70 microbiology approach for thermal inactivation of Hepatitis A Virus in acidified berries 71 The Enhanced Quasi-chemical Kinetics Model for the Inactivation of Bacillus amyloliquefaciens by High Pressure Processing (HPP) (not submitted) 72 The effect of pre-acid shock in the induced heat resistance of Escherichia coli K12 at lethal temperatures 73 Modeling the combined effect of osmotic dehydration, nisin and modified atmosphere packaging on the shelf life of chilled gilthead seabream fillets (11) 74 Modelling the inactivation of Listeria monocytogenes and enzymes in mussel using high pressure processing (not submitted) 75 Application of kinetic models to describe heat inactivation of selected New Zealand isolates of Campylobacter jejuni (not submitted) Technical Session 11 Applications of Predictive Modeling in Food Industry 76 Testing the Gamma hypothesis for two different hurdles, pH and undissociated acid concentration, using Bacillus cereus F4810/72 (not submitted) 77 Development and use of Microbilogical spoilage models by the food industry (21) 78 Biological time temperature indicators as quality indicators of refrigerated products (34) 79 The importance of growth/no growth models for specific spoilage organisms within the food industry 80 SSSP version 3.1 from 2009: new freeware to predict growth of Listeria monocytogenes for a wide range of environmental conditions 81 Evaluation of the microbial growth for different transport conditions of warm raw pork carcasses 82 Monte Carlo simulation for the prediction of vitamin C and shelf-life of pasteurised orange juice (not submitted) Posters 83 Predictive modelling of Escherichia coli O157:H7 cross contamination during slaughter operations. 84 Evaluation of primary models to predict microbial growth by plate count and absorbance methods 85 A predictive model for the effect of temperature and water activity on the growth of pseudomonads in osmotically pretreated gilthead seabream fillets 86 Quantification of the effect of factors involved in challenge-test assays on the growth rate estimation of Listeria monocytogenes 87 Modeling chlorine resistance of Penicillium expansum in aqueous solutions (non submitted) 88 Predicting the growth of Salmonella enterica in fresh cilantro (not submitted) 89 Dynamic modelling of Listeria monocytogenes growth in vacuum packed cold smoked salmon. (not submitted) 90 Dynamic models for growth of Salmonella in ground beef and chicken at temperatures applicable to the cooking of meat. 91 Mathematical modeling for predicting the growth of Listeria monocytogenes during ripening and storage of Camembert type cheese (not submitted) 92 Using ComBase Predictor and Pathogen Modeling Program as support tools in outbreak investigation: an example from Denmark 93 Influence of sporulation conditions upon the heat resistance of Bacillius coagulans ATCC 7050 (not submitted) 94 Variability analysis of microbial inactivation after heat treatments and the survivor lag phase (not submitted) 95 Model for Listeria monocytogenes inactivation on dry cured ham by high hydrostatic pressure processing 96 Simulation of human exposure to mycotoxins in dairy milk 97 Predicting the lag phase of Listeria monocytogenes in fluctuating environmental conditions (not submitted) 98 Validation of predictive models for the growth and survival of total Vibrio parahaemolyticus in post harvest shellstock Asian oysters 99 Sampling plan optimisation: application to French diced bacon industry and Listeria monocytogenes
Revista De La Sociedad Venezolana De Microbiologia, 2014
The antibacterial activity of lime (Citrus x aurantifolia) essential oil (EO) against the foodborne pathogen Listeria monocytogenes in tyndallised apple juice was studied at two temperatures. The EO concentration required to produce a significant increase in the lag phase of bacterial growth was determined. The addition of 200 µL of lime EO per 100 mL of apple juice completely inhibited the growth of L. monocytogenes at 5 ºC and at 37 ºC. This concentration of EO extended the lag time at least 292.7% compared to juice without EO. This is especially important considering that L. monocytogenes was able to grow in the juice at low temperatures in the absence of EOs.