Significant changes in lifestyle and dietary choices have led to consumer demand for quality, nutritious, fresh, and convenient on-the-go foods. In recent years, this has accelerated the consumption of ready-to-eat fresh produce (RTEFP), mainly leafy green vegetables and fruits. However, the horticultural produce chain is complex, with various production steps that potentially make RTEFP susceptible to foodborne pathogen contamination, thereby threatening human health. Risk factors may contribute to contamination during pre-harvest, harvesting, post-harvest preparation and handling practices, within storage, distribution and transport chains and at the retail level or in consumer kitchens. A spreadsheet-based qualitative risk assessment tool is developed in this study, which embodies established principles of food safety risk assessment to identify significant risk factors and transmission sources of pathogenic microorganisms for different RTEFP crops. The model features two risk assessments- pre-harvest and post-harvest, based on multiple-choice questions about various routes and mechanisms of produce contamination throughout the farm-to-table continuum. The assessments weigh risk factors according to a number of scenarios: equal weighting of risk factors, expert opinion weighting of risk factors, and user-defined weighting scenario. The final risk score is calculated based on a probability-impact matrix. It can be used to predict the risk associated with the crop's agricultural practices. Two case studies are presented (representing two different production scenarios) to demonstrate the applicability of the tool. The model is intended to support stakeholders in the RTEFP industry by identifying key risk factors and transmission sources of foodborne pathogens through horticultural crops. The tool can also be used to assess source-specific mitigation measures and help in identifying knowledge gaps in produce safety management in the horticultural sector. • A spreadsheet tool was developed for qualitative food safety risk assessment. • The model conducts pre- and post-harvest risk assessments via questionnaires. • Risk factors are weighted equally, by expert opinion, or by user definition. • Final risk scores are derived using a structured probability–impact matrix. • Two case studies illustrate the tool’s application to different crop systems.
Bhatia et al. (Wed,) studied this question.