Sara Bratager
FST Soapbox

The Future of Food Safety Is Data Driven

By Sara Bratager
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Sara Bratager

As the global food supply chain becomes increasingly complex, the food industry must integrate data-driven solutions by expanding the adoption of technologies that enable data collection, exchange and analysis.

“Better food safety begins and ends with better data,” remarked FDA Deputy Commissioner Frank Yiannas during a speech delivered on World Food Safety Day 2022 that emphasized the immense power of data in our food system. Digitized traceability data is critical not only for efficient recalls but also for root cause analysis of foodborne illness events. Product movement, performance and environmental data sets—when aggregated and analyzed—have the power to generate valuable trend insights and inform continuous improvement initiatives in food safety.

Embracing the opportunities provided by better data, the FDA has incorporated data sharing, data quality and data analysis themes into each of the core elements of the New Era for Smarter Food Safety Blueprint. Companies across the food industry mirror that focus, integrating data-based initiatives in their organizational goals. Following are some the latest and emerging technologies entering the food safety and traceability space to support industry efforts to harness the power of data.

IoT Devices Facilitate Data Collection

Though data collection efforts often rely heavily upon human labor, the use of Internet-connected devices to collect food safety and traceability data is expanding throughout the food and beverage industry.

Sensors at the harvest level can be used to monitor climate conditions in the field, automatically alerting farmers to weather events that may impact the quality and safety of food crops. Processing facilities use sensors to monitor the temperature of ingredients and raw materials through the production process, while logistics providers are using IoT technology for cold-chain monitoring.

Radiofrequency identification (RFID) scanners can be used to track the movements of tagged food products, supporting end-to-end food traceability efforts throughout the supply chain. The range of sensors, cameras, scanners and other IoT devices empower food industry actors to access and collect more comprehensive datasets than those collected with human labor.

Data gathered by these devices can be used to manage food safety deviations in real time, quickly recall unsafe products and create valuable predictive models.

Emerging Technical Standards Promote Data Communication

Traceability begins with data collection, but it does not end there. With complex, multi-party supply chains that stretch across our global food system, data communication is critical for end-to-end traceability.

Data standards and communications protocols facilitate seamless data exchange between trading partners. Published in July 2022, GS1’s EPCIS 2.0 standard provides businesses with a standardized way of capturing and sharing traceability data. This presents a common language to capture the what, where, when, why and how of supply chain events. Digital systems that elect to speak the same “language” enable interoperable communication, simplifying the flow of data from one end of the supply chain to the other. These systems can help to reduce the incidence and severity of outbreak occurrence through quicker, more accurate recalls and investigation.

AI and Machine Learning for Improved Data Analysis

With large pools of data at their fingertips, many organizations are looking to AI to analyze and make use of their food safety data.

During the March 2022 FDA TechTalk podcast, Maria Velissariou, VP of global corporate research and development and chief science officer for Mars, Inc., discussed the company’s use of AI in management of aflatoxin: a toxin that’s prevalence is likely to increase with climate change. Meteorological, geospatial and temporal data are analyzed to create AI-based models that predict the generation of aflatoxin in food crops. This model aims to provide farmers with the tools and information needed to prevent toxin formation in the field.

Regulatory agencies are also taking advantage of novel data analysis technology. Armed with two years of seafood import data, the FDA used machine learning to develop and pilot a predictive model for the identification of non-compliant seafood shipments. The program aimed to improve the agency’s ability to target seafood products that may pose a food safety risk, allowing for more efficient use of limited product testing and investigation resources. FDA plans to apply key learnings from the pilot to explore predictive models with other regulated food products.

As the global food supply chain becomes increasingly complex, the food industry must integrate data-driven solutions by expanding the adoption of technologies that enable data collection, exchange and analysis. We’ve already seen the power of food safety and traceability data in creating predictive and preventative models that benefit public health. Now, moving forward, stakeholders from across the industry must share their findings and work collaboratively to continually raise the standard of food safety practices worldwide.

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Sara Bratager

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