Adaptive Sampling for Food Safety: A Smarter Way to Reduce Risk and Recalls

Food production environments are complex, constantly evolving ecosystems — alive with motion, materials and microbial risk. From Listeria monocytogenes and Salmonella to molds and spoilage yeasts, the challenges posed by microorganisms in the factory setting are both tenacious and nuanced. As food and beverage manufacturers continue their efforts to reduce recalls and further safeguard both consumer health and their brands, they are increasingly turning to data-driven solutions that allow for earlier detection, more efficient monitoring and stronger preventive strategies.

One of the most promising innovations in this space is adaptive sampling for food safety, a modeling-based approach that applies risk intelligence to the microbiological monitoring process — think environmental monitoring, in-process testing or sanitation. Rather than treating all sample points as equal, adaptive sampling empowers manufacturers to focus on what matters most: where risk is highest and where action will make the greatest impact.

SMARTER DECISIONS THROUGH BETTER DATA.

Traditional environmental monitoring often takes a fixed-frequency, fixed-location approach. While compliant, this method can miss early signs of contamination or waste valuable resources by oversampling in low-risk zones. Adaptive sampling shifts this paradigm, using forward-looking risk models to guide where, when and how frequently to sample. This results in more optimized use of resources, higher confidence in results and faster time to detect emerging risks.

UNDERSTANDING RISK.

In food safety and quality, risk isn’t just about severity — it’s also about likelihood. How likely is it that an environmental sample will test out of specification? And how confident are we in that assessment, based on how often and where we sample?

To bring clarity to these questions, adaptive sampling for food safety applies three core concepts:

• Estimated risk: Based on sample history, facility layout and known migration pathways.
• Uncertainty of the estimate: How reliable is the data? Sampling once a month yields less certainty than sampling weekly.
• Value at risk: A calculation that combines estimated risk and uncertainty to express how bad things could get on a typical day.

This framework allows teams to adjust their sampling plans in real time, intensifying focus in high-risk zones while minimizing low-value effort elsewhere. Ultimately, this approach raises confidence in monitoring results while conserving time, labor and testing resources.

ROOT-CAUSE ANALYSIS, FASTER AND SMARTER.

Microbial problems in a factory rarely stay localized. They move, often through people, products, equipment and air. Understanding migration mechanics is critical to control risk and prevent recurring contamination. This can be done through stochastic means, but also through genomics, a technology that can trace strain-level movement of pathogens through a facility, helping teams uncover hidden transmission routes and identify root causes more quickly. By integrating genomic data into facility-wide risk models, manufacturers gain the ability to observe and intervene with unprecedented precision.

With adaptive sampling, teams can explore contamination events through a new lens. Data models informed by past incidents, genomic fingerprints and facility-specific behaviors allow manufacturers to:

• Decompose how and where microorganisms spread.
• Improve exploratory sampling during investigations.
• Accelerate time to detection during emerging issues.
• Strengthen intervention strategies in known hotspots.

Instead of chasing symptoms after the fact, facilities gain the ability to preempt risk — stopping transmission chains before they reach the product zone.

FOUR-STEP FRAMEWORK.

Implementing adaptive sampling starts with understanding the factory as a living system. Here’s how to approach it:

1. Map the facility and hygiene zones. Begin by defining critical zones — raw vs. ready-to-eat, high vs. low care, wet vs. dry areas — and overlay existing cleaning protocols.

2. Analyze movement and traffic. Document how people, product, tools and waste move through the space. Identify atypical traffic patterns that could contribute to cross-contamination.

3. Profile sample points. Examine historical data from each sample location. Not all points carry equal risk — some show consistently low positives; others may spike only under certain conditions.

4. Adapt based on risk and change. Use a blend of observed results, migration modeling and expert input to update sampling priorities. High and low-risk areas should both be sampled, but potentially, not equally or uniformly. The goal is coverage that reflects true operational risk.

HOW MUCH DATA DO YOU NEED?

One of the key advantages of adaptive sampling for food safety is its accessibility. While two years of historical data provides the strongest foundation for model training, meaningful insights can begin with as little as six months of sampling history. This makes adaptive strategies attainable for a wide range of facilities, regardless of where they are on their data maturity journey.

Importantly, adaptive sampling isn’t limited to large multinational manufacturers. Smaller operations and mid-sized processors can also benefit, especially when supported by user-friendly tools and expert implementation partners.

PREVENTION STARTS WITH PRECISION.

Ultimately, adaptive sampling for food safety isn’t about collecting more data. It’s about collecting the right data at the right time in the right place. When combined with genomics and facility-specific risk modeling, it enables manufacturers to go beyond compliance and toward true control of their microbial environment.

By embracing smarter sampling strategies, food safety and quality teams can move beyond detection to uncovering root causes and preventing potential contamination or spoilage. Ultimately, this translates into stronger internal confidence in quality decision-making and more optimized testing budgets that do not sacrifice rigor.

As the food safety and quality landscape evolves, adaptive sampling offers a forward-looking path — one that transforms environmental monitoring from a cost center into a strategic advantage.

Hannes Pouseele, Ph.D., is the global architect for data and genomics solutions at bioMérieux. He was instrumental in implementing the bioinformatics behind the transition of PulseNet, the U.S. foodborne disease surveillance network, from PFGE to whole genome sequencing. His current areas of focus are building preparedness tools for safety and quality risks in raw material supply, developing micro risk management tools for the food manufacturing environment and creating hybrid diagnostic solutions for molecular traceability, root cause analysis and factory health.