ILSI Europe Develops Framework to Help Food Industry Rank Microbial Risks of Dried Ingredients
A new scientific review has presented a qualitative, risk-based framework to help food manufacturers, foodservice operators, suppliers, and buyers evaluate microbiological hazards associated with dried fruits, vegetables, herbs, and spices and identify appropriate pathogen mitigation measures.
Published in Comprehensive Reviews in Food Science and Food Safety, the review was conducted by an International Life Sciences Institute (ILSI) Europe’s Microbiological Food Safety Task Force expert group. The researchers noted that, although low water activity (aw) inhibits microbial growth in dried ingredients, bacterial pathogens can survive for extended periods. Dried ingredients are also often added to ready-to-eat (RTE) foods after a validated pathogen-reduction step, meaning no additional lethality treatment occurs before consumption.
The review examined outbreaks and pathogen prevalence associated with dried ingredients, pathogen survival during drying and storage, and the effectiveness of conventional and emerging decontamination technologies. Pathogens implicated in outbreaks linked to dried herbs and spices have included Bacillus cereus, Clostridium perfringens, pathogenic Escherichia coli, Salmonella enterica, and norovirus. Outbreaks associated with dried fruits and vegetables have primarily involved Salmonella and foodborne viruses, including hepatitis A and norovirus.
The researchers emphasized that available prevalence data have significant limitations. Standardized studies are lacking for many dried commodities, and surveillance data may be influenced by targeted investigations conducted in response to outbreaks. Therefore, an absence of recalls, notifications, or pathogen detections should not necessarily be interpreted as evidence that an ingredient presents no microbiological hazard.
Risk-Based Framework Prioritizes Validated Pathogen Reduction
The proposed framework assigns weighted scores across seven areas: historical evidence of contamination; agricultural system contamination risk; crop exposure to soil and irrigation water; pre-drying storage, handling, and processing; the location of drying; validated pathogen-reduction steps; and controls against cross-contamination after lethality treatment.
Validated pathogen-reduction measures receive the greatest weight in the framework. Pre-drying treatments, drying, post-drying treatments, and controlled storage can each be considered if they have been validated to achieve pathogen reductions. The framework generally considers a reduction of at least 5 log colony forming units per gram (CFU/g) of the most resistant pathogen or appropriate surrogate to represent a full lethality treatment, although commodity-specific risk assessments or regulatory requirements may establish different targets.
The resulting overall score ranges from 7–115. Scores of 30 or lower indicate that pathogen risk-reduction measures in the ingredient supply chain are considered adequate without an additional intervention by the customer or consumer. Scores of 31–55 indicate that additional actions are recommended, while ingredients scoring 56 or higher are not recommended for use without an additional pathogen-reduction step by the customer or consumer.
Looking for quick answers on food safety topics?
Try Ask FSM, our new smart AI search tool.
Ask FSM →
The researchers also cautioned that finished-product pathogen testing cannot substitute for validated controls. Because natural contamination levels and practical sampling sizes may be low, failure to detect a pathogen does not demonstrate that an entire ingredient lot is free from contamination or that a control step was effective.
Overall, the framework was intended as a simplified, directional tool rather than a comprehensive quantitative risk assessment. The researchers said it could support sourcing, process development, hazard analysis, microbiological risk assessment, and communication between suppliers and customers. They recommended making conservative assumptions when information is unavailable or data quality is uncertain.
