Tonderai Madamba, Rosana G. Moreira, Alejandro Castillo, Zahra Mohebbi
{"title":"新鲜鳄梨干包装生产线上模拟单核增生李斯特菌交叉污染:食品安全的杂交方法","authors":"Tonderai Madamba, Rosana G. Moreira, Alejandro Castillo, Zahra Mohebbi","doi":"10.1111/jfpe.70223","DOIUrl":null,"url":null,"abstract":"<p>This study addresses the urgent need for simulation-based tools that evaluate corrective actions against microbial cross-contamination in food packing facilities. It presents a novel Food Safety Agent-Based Simulation (FS-ABS) that integrates predictive microbiology with discrete event simulation (DES) to track and predict <i>Listeria monocytogenes</i> behavior along a post-harvest Hass avocado dry processing line. The model combines the operational efficiency of DES with the biological realism of agent-based modeling, enabling a comprehensive understanding of how <i>L. monocytogenes</i> spreads in complex processing environments. The objectives of this study were threefold: (1) to track and predict microbial cross-contamination in the avocado dry processing line; (2) to integrate microbiological interactions with supply chain logistics; and (3) to enhance food safety by providing actionable insights for cross-contamination risk mitigation. Developed in NetLogo, the simulation treats avocados as mobile agents moving across a grid-based facility, each carrying attributes like contamination levels and transfer probabilities. This approach models both logistical flow and microbial dynamics, offering insights into how contamination propagates and how interventions might contain it. Validation with real-world data showed that cross-contamination remains limited at low initial loads (0.1–1.5 log CFU) but increases sharply beyond a 3.0 log CFU threshold—highlighting a tipping point where control measures become ineffective. Scenario testing revealed that increasing sampling frequency and sample size improves contamination detection and reduces variability. The model emphasizes the importance of early intervention, frequent sanitation, and strategic sampling in mitigating contamination risks. This hybrid tool provides a robust decision-support framework for improving operational integrity and food safety in avocado packing facilities.</p>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 10","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfpe.70223","citationCount":"0","resultStr":"{\"title\":\"Simulating Listeria monocytogenes Cross-Contamination of Fresh Avocado in a Dry Pack Line: A Hybrid Approach to Food Safety\",\"authors\":\"Tonderai Madamba, Rosana G. 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The objectives of this study were threefold: (1) to track and predict microbial cross-contamination in the avocado dry processing line; (2) to integrate microbiological interactions with supply chain logistics; and (3) to enhance food safety by providing actionable insights for cross-contamination risk mitigation. Developed in NetLogo, the simulation treats avocados as mobile agents moving across a grid-based facility, each carrying attributes like contamination levels and transfer probabilities. This approach models both logistical flow and microbial dynamics, offering insights into how contamination propagates and how interventions might contain it. Validation with real-world data showed that cross-contamination remains limited at low initial loads (0.1–1.5 log CFU) but increases sharply beyond a 3.0 log CFU threshold—highlighting a tipping point where control measures become ineffective. Scenario testing revealed that increasing sampling frequency and sample size improves contamination detection and reduces variability. The model emphasizes the importance of early intervention, frequent sanitation, and strategic sampling in mitigating contamination risks. 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Simulating Listeria monocytogenes Cross-Contamination of Fresh Avocado in a Dry Pack Line: A Hybrid Approach to Food Safety
This study addresses the urgent need for simulation-based tools that evaluate corrective actions against microbial cross-contamination in food packing facilities. It presents a novel Food Safety Agent-Based Simulation (FS-ABS) that integrates predictive microbiology with discrete event simulation (DES) to track and predict Listeria monocytogenes behavior along a post-harvest Hass avocado dry processing line. The model combines the operational efficiency of DES with the biological realism of agent-based modeling, enabling a comprehensive understanding of how L. monocytogenes spreads in complex processing environments. The objectives of this study were threefold: (1) to track and predict microbial cross-contamination in the avocado dry processing line; (2) to integrate microbiological interactions with supply chain logistics; and (3) to enhance food safety by providing actionable insights for cross-contamination risk mitigation. Developed in NetLogo, the simulation treats avocados as mobile agents moving across a grid-based facility, each carrying attributes like contamination levels and transfer probabilities. This approach models both logistical flow and microbial dynamics, offering insights into how contamination propagates and how interventions might contain it. Validation with real-world data showed that cross-contamination remains limited at low initial loads (0.1–1.5 log CFU) but increases sharply beyond a 3.0 log CFU threshold—highlighting a tipping point where control measures become ineffective. Scenario testing revealed that increasing sampling frequency and sample size improves contamination detection and reduces variability. The model emphasizes the importance of early intervention, frequent sanitation, and strategic sampling in mitigating contamination risks. This hybrid tool provides a robust decision-support framework for improving operational integrity and food safety in avocado packing facilities.
期刊介绍:
This international research journal focuses on the engineering aspects of post-production handling, storage, processing, packaging, and distribution of food. Read by researchers, food and chemical engineers, and industry experts, this is the only international journal specifically devoted to the engineering aspects of food processing. Co-Editors M. Elena Castell-Perez and Rosana Moreira, both of Texas A&M University, welcome papers covering the best original research on applications of engineering principles and concepts to food and food processes.