3-A SSI 2025 Dr. Ron Schmidt Student Travel Award Winner: Boluwatife Esther Ajayi

3-A SSI awarded eight dedicated food safety students with a Dr. Ron Schmidt Student Travel Award in 2025. These talented students attended the 3-A annual conference, presenting poster sessions on their areas of study. In the coming months, we’ll share their research in the 3-A SSI Blog.

This week, work by Boluwatife Esther Ajayi, a PhD student in the animal science department at Texas A&M University, is featured. Her research centers on food safety microbiology, with a focus on preventing foodborne illnesses to protect public health.

Safer Sausage: Validating a Predictive Model for Pathogen Control in Fermented Dried Meats  

Boluwatife Esther Ajayi

Ready-to-eat (RTE) dry fermented sausages are prized for their shelf stability, sensory appeal, and cultural significance. However, they also represent one of the more complex food safety challenges in the meat industry. The absence of a traditional thermal lethality step like cooking means that safety must be assured through fermentation and drying processes that must be carefully monitored and validated. A part of my Ph.D. research, which I presented at this year’s 3-A SSI  Summit on Hygienic Design, titled “Validation of a Process Criteria Model for Controlling Salmonella enterica and Shiga-toxigenic Escherichia coli in Fermented Dried Sausage,” seeks to address this critical need by creating a data-driven process criteria model for pathogen control. This work focuses on building predictive tools to ensure microbial safety in RTE fermented dried sausages.

The food safety risks associated with fermented dried sausages necessitate the validation of pathogen control. Pathogens such as Salmonella enterica and Shiga-toxigenic Escherichia coli (STEC) have been linked to outbreaks in products that were improperly fermented or dried, especially sausages like pepperoni and salami. While USDA-FSIS guidelines outline expectations for microbial lethality of these pathogens, many small and mid-sized processors face challenges in validating their processes due to cost, time, or lack of technical resources. My research is designed to provide a data-driven solution: a process criteria model that uses environmental and microbiological data to predict pathogen reduction and guide safe sausage production.

The goal of this project is to use these findings to develop a robust process criteria model. This model will serve as a predictive tool, allowing processors to estimate microbial lethality based on measurable process parameters across different product types and environmental conditions.

The research follows a structured, real-world approach to sausage production. We formulated meat batter, inoculated samples with Salmonella enterica and STEC, ground and stuffed it into casings, and then conducted controlled fermentation trials. During fermentation four key variables were closely monitored: time, temperature, relative humidity (RH), and pH. These variables were selected for their known roles in affecting optimum fermentation (quality) and microbial survival (safety). Our aim was to quantify their individual and combined effects on pathogen reduction, and to establish empirical baselines for developing a predictive safety model.

Results showed that within 10 hours of fermentation (preselected time to achieve a pH of 5.0), Salmonella counts decreased by approximately 0.58 log CFU/g, while STEC levels dropped by 0.35 log CFU/g. Although these reductions do not meet regulatory lethality requirements on their own, they provide the empirical data needed to develop a predictive model. By mapping how environmental variables impact pathogen reduction, we are working to create a mathematical model, potentially enhanced with machine learning, that will allow processors to predict lethality outcomes without needing to conduct direct microbial testing. This model could be integrated into Hazard Analysis and Critical Control Point (HACCP) systems, used as part of regulatory validation documentation, and applied across a range of sausage types and production scales. We also observed consistent trends in pH reduction, from around 6.3 to below 5.0, as well as stable environmental conditions. Temperature and humidity were also held within optimal fermentation ranges, confirming environmental stability and process consistency. These trends reinforce the effectiveness of controlled fermentation and confirm its contribution to pathogen suppression.

The next phase of this research involves extending the model to account for the drying phase, where additional pathogen reduction typically occurs and integrating antimicrobial interventions. We also plan to test the model in commercial-like settings to validate and refine its real-world performance and ensure the model’s adaptability and reliability across real-world settings. By incorporating machine learning, we hope to improve the model’s ability to handle variability across different product types, ingredient formulations, and processing environments. The goal of this project is to use these findings to develop a robust process criteria model. This model will serve as a predictive tool, allowing processors to estimate microbial lethality based on measurable process parameters across different product types and environmental conditions. In addition to supporting regulatory compliance, this approach holds promise as a scalable and cost-effective solution for smaller processors who lack access to laboratory-based validation studies.

This work would not be possible without the generous support of the USDA’s National Institute of Food and Agriculture (NIFA), under Grant No. 2023-67017-39044 for the research Validation of microbial pathogen control on dried RTE sausages by novel antimicrobial and mathematical approaches. I’m especially grateful to 3-A SSI for providing the Dr. Ron Schmidt Student Travel Award, which allowed me to present this work and engage with a broader community of food safety professionals, hygienic design professionals and industry leaders. I had the chance to meet professionals, researchers, and students from across the country, discuss challenges and solutions in meat science, and learn how others are applying technology to solve real-world problems. It was also personally meaningful to represent the future of food safety science and share how modeling and microbiology can come together to serve public health.

In conclusion, this research represents a step forward in modernizing microbial validation for fermented meat products. By developing a process criteria model that is both scientifically rigorous and practically applicable, we are not only improving safety outcomes, but we are also helping ensure that producers, regardless of size or resources, can confidently deliver safe, high-quality products to consumers. As the meat industry continues to evolve, innovation in hygienic design, fermentation control, and data modeling will be key to ensuring safe, high-quality, and economically viable products. I’m proud to contribute to this mission and look forward to the continued development of tools that make food safety both rigorous and practical from the fermentation chamber to the consumer’s plate.

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