Performing Department
(N/A)
Non Technical Summary
As stated by the CDC, foodborne illness is a common, costly--yet preventable--public health problem. CDC estimates that 1 in 6 Americans get sick from contaminated foods or beverages each year, and 3,000 die. The U.S. Department of Agriculture (USDA) estimates that foodborne illnesses cost more than $15.6 billion each year. Other estimates including all costs range as high as $100 billion. Outbreaks associated with consumption specifically of fresh produce between 2003-2013 surpassed outbreaks associated with all other food categories in the same period. This highlights the higher risk of foodborne disease outbreaks associated with fresh produce as over 630 outbreaks from 2003-2014 were traced back to fresh produce contamination. These outbreaks resulted in over 19,000 confirmed illnesses across the US. Although many of these initial contaminating events may have resulted from pre-harvest contamination in the field, the cross-contamination process during harvesting and postharvest processing and handling can significantly enhance the risk of nationwide disease outbreaks. This is validated by a quantitative risk analysis data set published by the FDA based on prior disease outbreaks that illustrates significant contamination risks from equipment surfaces during harvesting and postharvest processing.This project is focused on developing a low-cost, bio-based antimicrobial surface coating that will be used on food contact surfaces of equipment used for harvesting, handling and processing fruits and vegetables. The work is based on discoveries made at the University of California-Davis. The coating product will be applied during the routine sanitation process and will provide continuous decontamination of the food contact surfaces during operations. The proposed solution can be adapted to existing equipment and a diversity of surfaces such as plastics and stainless steel used in harvesting, handling and processing of produce (and other food products). Primary activities will include selecting will involve a) developing and optimizing the coating formulation, b) verifying it has the appropriate physical and chemical properties for the intended use, c) verifying the antimicrobial effectiveness, d) determining if there are any adverse effects on the produce that contacts the coating, and e) modeling overall effectiveness via computer simulations. Success in this effort will significantly reduce the risk of cross-contamination of fruits and vegetables by pathogens via equipment surfaces. This will lead to reduced incidence of foodborne illnesses and alleviate the accompanying human, societal and financial costs.
Animal Health Component
50%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Goals / Objectives
Goal: Develop innovative, flexible, food grade antimicrobial coating compositions that will prevent cross contamination by pathogens via surfaces of produce harvesting and processing equipment, including legacy equimpment, and which can be easily removed and reapplied during normal sanitation processes.Objectiives:Develop and optimize coating formulationValidate chemical and physical stability of coatingsVerify antimicrobial activity of optimized coatings on equipment food contact surfacesQuantify residual antimicrobial on produce surface after contact with coatingDetermine effect of contact with coating on produce quality attributesDetermine risk reduction using coatingsDetermine bacterial inactivation ratesDetermine transfer coefficient of from contact surface to produceModel risk of cross-contamination with and without coating
Project Methods
Aim I: Develop approaches for rapid and uniform deposition of antimicrobial food ingredient-based coatings on conveyor belts and packaging tables commonly used in the fresh produce industry.Development of antimicrobial coating compositions: Carrisan has developed proprietary formulations of antimicrobials using food ingredient based coatings that will be evaluated in this aim.Antimicrobial properties and reduction in cross-contaminationAntimicrobial activity (based on the culture-based colony) antimicrobial coating formulations will be measured against selected bacteria (rapamycin resistant E. coli O157:H7 (ATCC 700728); Generic E. coli K-12; rapamycin resistant L. innocua (ATCC 33090), avirulent Salmonella strain (MHM112), and virulent strains of E. coli O157:H7 and Listeria monocytogenes 10403. Inoculated produce batch (20 kg of baby spinach or 20 kg of peaches) at 103 -10 5 CFU/gm with a combined inoculation of a cocktail selected BSL-1 strains (Listeria, E. coli, and Salmonella) will be processed using the coated surfaces.After processing a batch of contaminated produce on the selected surfaces (conveyor belt and packaging table), the selected surfaces will be swabbed using the standard procedures for verification of surface sanitation. The presence of inoculated strains will be quantified using the selective media.Produce quality evaluation based on shelf life, texture, total phenolic content, and colorShelf life:The shelf life of produce samples from control and coated surfaces will be evaluated using the standard bagging and storage conditions and compared.Total color change:The color of fresh produce will be measured using the Hunter color value (L, a, b) values before and after treatment. The total color difference (ΔE) will be determined to characterize the color changes.Texture: Changes in the samples' texture will be characterized based on the changes in maximum compression force (MCF) measured using the TA-TXPlus Texture Analyzer.Phenolic content: Phenolic content of fresh produce will be measured using Folin & Ciocalteu's method.Measure of success: Development of effective food-grade coating composition that can provide rapid inactivation of contaminating bacteria and maintain antimicrobial effectiveness for 24 hours during the simulated industrial process will be considered a success in this aim.Aim 2: Evaluate risk reduction using the proposed antimicrobial food ingredient-based coatings and validate the cost-effectiveness of the coatingsDetermination of bacterial inactivation rates on antimicrobial coated surfaces:Selected bacterial inoculation (107 cfu/ml) will be pipetted on a coated and uncoated control surfaces, and the number of surviving bacteria as a function of time will be enumerated by plate counting on selective media. Based on these measurements, bacteria's inactivation kinetics on both antimicrobial coated and uncoated controls will be determined.Determine the transfer coefficient from contact surface to fresh produce: To obtain the transfer coefficients, control and the antimicrobial coated surface will be inoculated with the target strains (shiga toxin negative E. coli O157: H7 and Listeria innocua, respectively) at 103 CFU/cm2 and 107 CFU/cm2 and left to dry before processing of the produce. Contact time between contaminated surfaces (conveyor belt and packaging table) and model fresh produce (100 gm samples) will be designed to simulate industry conditions (between 5-30 seconds). The bacterial transfer levels from the contaminated surfaces to fresh produce will be measured based on colony counts using selective plating media for the target strains. This process will be repeated 10 times to obtain a statistical distribution of the transfer coefficient. We anticipate that the transfer coefficient between fresh produce and food contact surface will follow a lognormal distribution. Based on measured transfer coefficients and their standard deviation, the distribution of transfer coefficient can be obtained, which will then be used to develop a Monte Carlo simulation model for bacteria cross-contamination process in batch processing.Monte Carlo model of risk of cross-contamination of fresh produce: Monte Carlo simulation will be conducted using R software by running 10000 times simulations (each simulation represents one event) of the cross-contamination scenario.Statistical analysis:Throughout the study, experiments will be conducted in three independent trials. Measurements will be conducted in three replicates, and reported values will be in the form of averaged values ± standard deviation. Analysis of Variance (ANOVA) will be used to determine statistical significance, and p<0.05 is considered significant.Cost Analysis: We will confirm the accuracy of the initial cost estimates. The cost analysis will include the ingredients, manufacturing, packaging, and equipment, and labor required.Measures of Success: Demonstrating significant risk reduction (more than 95%) for the cross-contamination compared to control while using food-grade compositions with low-cost ingredients would be considered a success.