Applied Microbiology: Predictive Microbiological Models - Literature review Example

Predictive Microbiology Development of predictive microbiological models Predictive microbiological models are computer based software tools which allow us to estimate the rate of microbial growth or whether a particular microorganism will growth under a set of specific conditions. Evaluation of the effect of processing, transportation and storage on the microbiological safety and quality of food is enabled by analysis based on mathematical modelling. (Heredia, et al., 2009) Predictive models were applied empirically with the earliest report by Esty and Meyer (1992) describing the thermal death of Clostridium botulinum type A spores by a log linear model. This method is still used to estimate the required heat processing of low acid canned foods. Future models include dynamic modelling in which interaction between bacteria and environmental factors and structure of the food. Lag modelling which models will take into account the effect of history and physiological state of the bacteria. Modelling including probability growth will address the probability of the prediction. Modelling single cell kinetics will account for variability at a single cell level. Relating predictive microbiology and molecular microbiology which will utilise knowledge about responses at molecular level for instance gene expression under certain conditions. Predictive models are used in a two-step approach for growth curves and death. In the first step, the growth or death model is established in a constant environment that is the primary model. In the second step how the parameters of the primary model are affected by other factors such as environmental factors are determined. This is the secondary model. Predictive microbiological models are based on laboratory generated data. Kinetic growth models allow assessment of the amount of growth that can occur. Microbiological growth in media are produced with different intrinsic parameters such as pH and salt concentration. The models are inoculated with the microorganisms of interest and a stored at a range of temperatures and the microbial level is assessed over time. Another model is growthor no growth models or time to growth models in which a differentapproach istonote to turbidity rather assess microbial levels.(Pearson & Dutson, 1999) The effects on microbial growth of single controlling factors include temperature, pH, water activity (aw) and acceptance that particular microbes of concern would not grow below certain temperatures. Other factors such as composition of the atmosphere, preservative and food structure. Foods contain water, have a pH value and are treated under known temperatures during storage. These compose key control factors. These factors could be measured and modelled. The model is considered representative if the differences between the calculated and observed responses in food are the same. The other factors will be taken into account if only part of the growth follows the model. The growth rates based in relatively simple models for the same conditions of temperature, pH and water activity are often close to the observationinreal food. (Lund, et al., 1999) Applications of predictive microbiology Predictive microbiology is a very quick, efficient and cost effective for assessingthe potential for microbial growth. It is used in assessing product shelf-life. It helps to ensure safety. It assists in product development. It helps to identify areas where challenge testing should be undertaken. Predictive microbiology helps to develop risk assessment and Hazard Analysis and Critical Control Points HACCP. This can be by preliminary hazard analysis, identification and establishment of critical control points, corrective measures and evaluation of variables interaction. During product development, a product formulation could be changed several times before being finalised. It’s impossible to evaluate the shelf life of all possible formulations therefore limiting the amount of tests saves time and money.Prediction models help in evaluation of the impact of microbial spoilage in a food product and effect of processing on food quality and safety.Microbial survival can be predicted for changes without the need for extensive laboratory analysis. (Brul, et al. 2007) In changes in product handling post production, predictive microbiology helps in estimating storage temperature and effect of elevated temperature. It is usefulinbatch specific problem formulation. Predictive microbiology is useful in calculating the time for growth to begin (lag time) for given conditions/formulations for pathogenic organisms. It is used in prediction of the time to reach levels that cause food poisoning in toxin producing micro-organisms.Therefor it is can estimate the likelihood of pathogen survival during storage. Another vital application is predict if microorganisms can reach level above specifications.(Perez-Rodriguez & Valero, 2013). Models can be used in training of food safety professionals about rates of microbial growth, survival and inactivation change with different values of intrinsic and extrinsic parameters. (Knechtges, 2011) Advantages of predictive microbiology Temperature, pH and water activity are the most important parameters for controlling microbial growth, followed by antimicrobial constituents and the concentration of certain gases such as CO2Temperature can be maintained consistently but the other parameters may change from time to time from microbial growth and chemical reactions. Predictive microbiology enables the objective evaluation of the microbiological consequences of food processing and handling operations. Introduction of the concept of food safety, allowing the industry and regulatory agencies to develop methodologies that determine the safety and shelf life of foods has been contributed by recognition of micro-organisms and their capacity to spoil foods. Modelling offers advantages in terms of time and cost. Predictive microbiology models offers great potential for assuring the safety of product designs and foods in international trade formulated by different groups in different places. It helps to prevent the unnecessary repetition of experiment. (Brown, 2008) Disadvantages of predictive models Current models have been derived using model broth systems. It is often difficult to apply those models on actual foodsystems especially if the automated methods such as impedance have been used in model development. Most models are designed to handle temperature effects well and some have the ability to predict effect of pH and salt but factors such as spices and preservatives are not incorporated in many models.(Shahidi, et al., 1997). Another limitation of models is thatmodels are specific to the food. The physiological state of thecells affects the time that is neededby the microorganisms to leave the lag phase and enter the exponential phase. In most cases, there is very limited information about the pre inoculation period in modelling. Models have errors in each step of model construction. Homogeneity error arise because some foods are not homogenous. When the model is a simplification and other food effects and microbial ecology effects that are included are difficult to quantify are not included in currently available models result to completeness error. Measurement error arises from inaccuracy in the limitations in the measurement methods used to collect data for the model parameters. Numerical procedure error includes errors arising from procedures used for model fitting and evaluation. The Combase Predictive Model tool to establish the expected growth curve (logCFU/g) of Listeria monocytogens which the initial amount was 0.1 logCFU/g in a food product which had a use by date of 8 days (192 hours). The storage temperature was 4oC. The pH of the product is 7, with a physical state of 2.02e-2, salt concentration of 0.5% and had absence of CO2. The double time (Dbl. Time) is 17.989 hours. Because of a technical problem with the refrigeration system the storage temperature was compromised and happened to be in fact 7oC. Establish the new predicted growth curve. The double time was 9.743 therefore the growth rate was higher compared to as when the temperature was 4o C. The physical state was changed to 1.00e+0 to imply no lag phase. (USDA-ARS, 2014) The level of the initial contamination and type controls bacterial densities in food and by environmental conditions such as temperature, pH and water activity and history of cells. Bacteria may increase in number, remain the same, or decrease in number. Predictive models have been developed for each kind of bacteria. Bacteria exhibit four types of phases in response to environmental conditions. These include lag, growth, maximum population density and death. Growth phase and maximum population density are less variable than lag phase. During lag phase the physiological status of a bacterial cell at any moment is linked to its response to theenvironment. These transitions require bacteria to make adjustments. The lag phase increases with decreasing temperature and with exposure to environmental stresses such as lower water activity and unfavourable pH.Lag phase may also be affected by the initial pathogen density. At lower densities there is a higher probability of selecting individual cells with longer lag times versus higher cell concentrations. These models need to be developed with lower, more realistic contamination levels such as Listeria monocytogenes. Lag time is inversely related to the generation time, that is, the time for a cell to replicate (doubling time). The ratio of lag time to generationtime for a specific bacterial species is a unique distribution of responses. (Fratamico, 2005) Bibliography Brown, M., 2008. Chilled foods : a comprehensive guide. London: Cambridge. Brul, S., van Gerwen, S. & Zwietering, M. H., 2007. Modelling microorganisms in food. Cambridge: CRC Press. Fratamico, P. M., 2005. Foodborne pathogens: microbiology and molecular biology. Wymondham: Caiser Acad. Press. Heredia, N. L., Wesley, I. V. & Garcia, J. S., 2009. Microbiologically Safe Foods. Hoboken: John Wiley & Sons. Knechtges, P. L., 2011. Food Safety: Theory and Practice. s.l.:Jones & Bartlett Publishers. Lund, B. M., Baird-Parker, A. C. & Gould, G. W., 1999. The microbiological safety and quality of food. Gaithersburg: Aspen Publishers. Pearson, A. M. & Dutson, T. R., 1999. HACCP in meat, poultry and fish processing. Gaithersburg, Md: Aspen. Perez-Rodriguez, F. & Valero, A., 2013. Predictive microbiology in foods. New York: Springer. Shahidi, F., Jones, M. Y. & Kitts, D., 1997. Seafood Safety, Processing,and Biotechnology. Pennsylvania: CRC Press. USDA-ARS, 2014. Combase. [Online] Available at: http://modelling.combase.cc/ComBase_Predictor.aspx [Accessed 8 April 2014]. Read More