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ARS Home » Southeast Area » Raleigh, North Carolina » Food Science Research » Research » Research Project #430619

Research Project: Intervention Strategies for Controlling Human Pathogens Associated with Fermented and Acidified Vegetables

Location: Food Science Research

Project Number: 6070-41420-008-00-D
Project Type: In-House Appropriated

Start Date: Apr 25, 2016
End Date: Apr 24, 2021

Objective:
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.