Location: Food Science and Market Quality and Handling Research Unit
2017 Annual Report
Objectives
1. Determining the safety of low and alternative salt fermentations, produced nationally and internationally.
2. Develop predictive models for 5-log reduction times for pathogenic Escherichia coli in fermented and acidified vegetable products.
3. Enhance buffer capacity models for predicting pH changes in acidified foods with low acid ingredients.
Approach
The experimental approaches that will be use to achieve the objectives will include mathematical modeling, molecular ecology studies, and biochemical analysis of fermentation brines. Specifically, for Objective 1, to determine the effects of salts on pathogen reduction in fermentations, growth and death of bacterial pathogen cocktails (strain mixtures) will be measured in fermentations by conventional bacterial plating methods using automated plating equipment. Log reduction times for pathogens will be calculated using linear or nonlinear (Weibull) models. Biochemical analysis for salts, organic acids and sugars, will be done by titration (for salts), and high performance liquid chromatography (for acids and sugars). A matrix of salt types and concentrations will be tested to determine how salt effects pathogen die-off. For Objective 2, mathematical modeling approaches to determine the reduction in pathogen populations during fermentation will utilize non-linear systems of ordinary differential equations (rate equations) using Matlab computer software. In addition computer simulation models will be developed using the C++ programming language. Data for these models will be obtained from the experiments in Objective 1. Model results will be compared to data generated under a variety of conditions to determine if the models accurately describe the data. To accomplish Objective 3, predicting pH of buffered acidified foods with low acid additives, mathematical models will be based on published ionic equilibria equations for buffered acid and base solutions. Novel methods for numerical solutions to these equations will be implemented with Matlab software. An automated titrator will be used to confirm predicted buffer capacity curve data. To fit data to the models, several optimization algorithms will be used from the Matlab Optimization Toolkit, or independently programmed in Matlab or C++. The knowledge gained will be used to help processors and regulatory agencies assess and assure the safety of acidified and fermented food products.
Progress Report
The effects of sodium chloride (NaCl) or calcium chloride concentrations on the growth and death of lactic acid bacteria (LAB, from vegetable fermentations) and pathogenic strains of Escherichia (E.) coli (STEC) were investigated. STEC strains were targeted in these studies because they are the most acid resistant pathogens in fermented and acidified foods. The data indicate that contrary to expectations, the growth of STEC strains was not significantly affected by even very high salt concentrations (to 6% NaCl, typical of commercial fermentations) in a vegetable brine, compared to a control treatment with no salt. However, the growth of LAB, which ferment vegetables in salt brines, was found to increase as salt concentrations (sodium or calcium) were increased. Data on the die-off of STEC or LAB with varying salt concentrations were also generated. In general it was determined that killing rates for LAB and STEC were influenced by the salts tested. However, calcium salt did not affect killing rates compared to NaCl for the concentrations typical of commercial cucumber fermentations. This is significant because calcium fermentation technology is being developed to help alleviate waste NaCl disposal problems at commercial pickle plants.
A computer simulation model of bacterial cell growth for LAB and E. coli (STEC, defined above) under conditions typical of vegetable fermentations have been developed. Data for model validation was generated with three different salt conditions, representative of cabbage fermentations (2% NaCl), commercial cucumber fermentations (6% NaCl) and commercial cucumber fermentations brined with calcium chloride (1.1% calcium chloride). While further data is still needed to fully validate the model, the values of some model parameters, such as the rate as which sugar is used by growing cultures, and the amount of sugar needed for cells to divide have been determined. These results are important, since the outcome of competitive growth of STEC and LAB depends on sugar utilization, which in turn leads to the acid production that inhibits STEC and other pathogenic bacteria. Continued work will include the comparison of simulation results with competitive growth experiments. Once successfully validated, these models may be used to predict the die off of pathogens in competitive growth with lactic acid bacteria in vegetable fermentations.
A buffer capacity model has been developed that can predict how pH changes occur with the addition of organic acids or bases to fermented or acidified vegetable brines (or dressings). Data for the model was generated using titration curves done with a strong acid (hydrochloric acid, HCl) or base (sodium hydroxide, NaOH). The model has been used to predict the pH of mixtures of organic and inorganic acids and bases. An important accomplishment was devising a means to fit the buffer capacity curve data by using a secondary model (based on trigonometric functions) that allows easy prediction of pH with the primary buffer capacity model. While further studies with foods, including fermentation brines are needed, the existing data demonstrate that pH prediction with complex mixtures of known or unknown components can be accurately done. The model may be used (possibly with further development) to determine the pH of mixtures of acidic foods with low acid ingredients (having high pH) in acidified foods. Data will be generated to determine if the pH resulting from a mixture of the food ingredients matches predictions. The model will be used to help the Food and Drug Administration (FDA) determine how to regulate the addition of small amounts of low acid (high pH) ingredients into fermented and acidified foods, without affecting safety.
A buffer capacity model has been developed that can predict how pH would change with the addition of organic acids or bases to fermented or acidified vegetable brines (or dressings) by analyzing data from titration curves done with a strong acid (HCl) or base (NaOH). The model has now been used to predict the pH of mixtures of organic and inorganic acids and bases. An important accomplishment that allowed this result was devising a means to fit the buffer capacity curve data to a secondary model (based on trigonometric functions) that allows easy prediction of pH with the primary buffer capacity model. While further studies with foods, including fermentation brines, are needed, the existing data demonstrates that pH prediction with complex mixtures of known or unknown components can be accurately done. The model may be used (possibly with further development) to determine the pH of mixtures of acidic foods with low acid ingredients (having high pH) in acidified foods. Data will be generated to determine if the pH resulting from a mixture of the food ingredients matches predictions. These data will help the FDA determine how to regulate the addition of small amounts of low acid ingredients into fermented and acidified foods.
Accomplishments
1. Identification of regulatory genes affecting acid resistance of pathogenic E. coli strains (STEC) in acidified vegetables. ARS scientists in Raleigh, North, Carolina, examined disease causing bacterial strains (STEC) to determine how differences in acid resistance could be explained by differences in gene regulation. From previous research, we knew that there was a wide range of acid resistance levels among these bacteria. Our research focused on determining mechanism(s) by which these important food pathogens are able to survive in acid conditions representative of acidic food products. We found that a regulatory mechanism involving small RNA molecules inside the cells apparently links expression of acid resistance genes to genes involved in producing a fibrous network that can aid STEC cells in attaching to surfaces. This regulatory network is very complex. We have found that it includes genes that are involved in the cellular response to nutrient limitation by using different kinds of growth media (with varying amounts of nutrients). The knowledge gained increases our understanding of how cells respond to acid conditions, and why some STEC strains survive better than others in acidic foods. Results may be used to help devise strategies for reducing the threat of these pathogens in acidic foods.
Review Publications
Baranzoni, G., Fratamico, P.M., Reichenberger, E.R., Kim, G., Breidt, F., Kay, K., Oh, D. 2016. Complete genome sequences of Escherichia coli O157:H7 strains SRCC 1675 and 28RC that vary in acid resistance. Genome Announcements. 4:4. doi: 10.1128/genomeA.00743-16.
Kim, G., Fratamico, P.M., Breidt, F., Oh, D. 2016. Survival and expression of acid resistance genes in Shiga toxin-producing Escherichia coli acid adapted in pineapple juice and exposed to synthetic gastric fluid. Journal of Applied Microbiology. doi: 10.1111/jam.13223.
