Objectives:
Objective 1: Develop a QMRA to model public health risk of the “last mile” considering produce quality parameters (including microbiome patterns) and their impact on pathogen survival and growth.
Objective 2: Perform inoculation studies on key commodities of interest to collect input parameters to improve the QMRA developed.
Objective 3: Validate the QMRA model and its predictions through inoculation studies.
Objective 4: Run scenario analyses to model the impact of different “last mile” scenarios on produce food safety risks.
Abstract: The post-harvest quality of fresh produce is highly variable and is dependent on the rate of physiochemical and microbiological decay. For example, at the post-harvest level, produce may display various degrees of incipient decay (e.g., physical damage) and can have different microbiomes that can impact quality defects. Importantly, “conditions” that relate to the post-harvest quality of fresh produce have the potential to substantially impact the risk of pathogen growth and survival as well as foodborne illness risks, particularly during the “last mile” in the supply chain. In addition, pre-harvest, harvest, and post-harvest management, including post-processing storage and distribution (e.g., time and temperature history), may impact pathogen dynamics and virulence in numerous ways ranging from (i) introduction of damage during processing, which increases accessibility of nutrients, to (ii) selecting for microbiomes that may enhance or otherwise impact pathogen survival and proliferation. Building on existing work (including unpublished produce spoilage models developed by the PI), this project will develop a quantitative microbial risk assessment (QMRA) for produce during the “last mile” of the supply chain (for L. monocytogenes and enterohemorrhagic E. coli [EHEC]), while accounting for novel risk factors such as (i) the post-harvest quality of produce, and (ii) direct and indirect interactions between pathogens and the produce microbiome. In Obj. 1., we will develop a baseline QMRA and conduct sensitivity analysis to identify which QMRA inputs contribute to the greatest uncertainty in predictions (i.e., pathogen prevalence and concentration, likelihood of foodborne illness). These findings will inform Obj. 2, which will include experimental studies to evaluate how pathogen dynamics on produce is impacted by the produce microbiome and leaf damage (two inputs that are expected to have the greatest impact on the QMRA’s uncertainty); the data gathered in Obj. 2 will be utilized to improve the QMRA, which will subsequently be validated in Obj. 3. Validation of the QMRA will allow us to assess prediction accuracy and bias. In Obj. 4, the validated QMRA will be utilized to run “what-if” scenarios; these analyses will allow us to rank risk factors associated with post-harvest defects by their impact on (i) pathogen growth and survival on produce, and (ii) likelihood of causing foodborne illness. Overall, this project will provide both (i) a QMRA as well as (ii) additional experimental and modelling data that will help industry assess the impact of different post-harvest conditions and defects on food safety risks, which will facilitate the development of receiving specifications that can reduce food safety risks.