Computer-Based Contamination Monitoring for Protein Safety
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Solution Overview
Problem
Current methods for detecting harmful pathogens in protein food sources, such as beef, are limited as they only measure contamination levels at a single point in the supply chain after aggregation, failing to account for variations throughout the chain, which can lead to unsafe protein for consumption.
Innovation Solution
A computer-based method that receives contamination level and external indicator data, accesses prior data and interventions from a database, selects subsets with similar characteristics, determines effective interventions based on prior results, and adjusts interventions accordingly to enhance protein food safety across the supply chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If contamination levels are measured at a single point in the supply chain after aggregation, then measurement cost and complexity are reduced, but measurement precision and reliability of food safety assessment deteriorate
Solution Approach 1:
The supply chain is divided into multiple monitoring points (feedlot, hide wash, pre-evisceration carcass wash, post-split carcass wash, post-chill carcass wash, subprimal spray cabinet, trim point) where contamination levels are measured independently. This segmentation allows comprehensive tracking of pathogen levels throughout the entire supply chain rather than relying on a single aggregated measurement, thereby improving measurement precision while maintaining manageable system complexity through modular monitoring stations.
2Productivity
If random sampling is used at the trim point, then measurement cost and time are reduced, but reliability of food safety determination deteriorates
Solution Approach 1:
Contamination levels are measured at multiple points throughout the supply chain before the final trim point assessment. By performing preliminary measurements at feedlot, hide wash, pre-evisceration carcass wash, post-split carcass wash, and post-chill carcass wash stages, the system accumulates contamination data progressively. This preliminary action ensures that when the trim point assessment is performed, it is based on comprehensive prior data rather than random sampling alone, thereby improving reliability while maintaining productivity through efficient data aggregation.
Solution Approach 2:
The system uses contamination level measurements from previous points in the supply chain as feedback to inform and adjust the assessment at subsequent points. Each measurement point provides feedback data that contributes to the overall reliability determination, allowing the system to build a comprehensive safety profile rather than relying solely on random sampling at the final trim point. This feedback mechanism ensures that productivity is maintained while reliability is enhanced through cumulative evidence.
3Reliability
If contamination levels are monitored at multiple points throughout the supply chain, then food safety reliability is improved, but device complexity and measurement cost increase
Solution Approach 1:
The monitoring system uses universal measurement methods and standardized protocols at each supply chain point that can be applied consistently across feedlot, hide wash, pre-evisceration carcass wash, post-split carcass wash, post-chill carcass wash, subprimal spray cabinet, and trim point. This multi-functionality allows the same basic measurement technology to serve multiple monitoring purposes throughout the supply chain, improving reliability through comprehensive coverage while controlling device complexity by reusing standardized components and procedures rather than requiring unique complex systems at each location.
Data Source
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AI summary
Methods, systems, and devices for increasing protein food safety are provided. According to one embodiment, a method in a computer system for increasing protein food safety includes steps: (a) receiving contamination level data; (b) accessing from a database stored data comprising prior contamination level data, prior interventions associated with the prior contamination level data, and prior actual results associated with the prior contamination level data; (c) selecting a subset of the prior contamination level data, the prior interventions, and the prior actual results, where the prior contamination level data is similar to the contamination level data; (d) determining if an effective intervention is set forth in the subset based at least partially on the prior actual results in the subset; and (e) if an effective intervention is not set forth in the subset, causing an intervention to be output that is increased relative to the intervention in the subset.