Drug residue in shrimp imports a focus of FDA AI program
A U.S. Food and Drug Administration pilot program is using artificial intelligence to screen for shrimp imports contaminated by antibiotics.
The FDA recently kicked off the third phase of its artificial intelligence (AI) imported seafood pilot program, which uses AI and machine learning to strengthen its import screening process and to ensure foods entering the U.S are safe.
“We have begun to focus on areas of increased risk, such as shrimp contaminated by aquaculture drugs, for foreign inspections. This includes increased importer inspections, higher rates of sampling and examination, and use of non-traditional tools, such as third-party audits, specific to this commodity,” the FDA said in a Constituent Update.
This pilot builds upon the two previous phases of the program operated under the New Era of Smarter Food Safety Blueprint, a program that seeks to reduce the number of foodborne illnesses by leveraging technology to create a safer, more digital, traceable food system.
“The pilot focuses on imported seafood because more than 90 percent of the U.S. seafood supply comes from other countries and, in the past, FDA has seen food safety concerns for various imported seafood products along different points in the supply chain," the FDA said.
The third phase, which began 15 August, 2022, is designed to improve the agency’s ability to quickly identify imported seafood products that may be contaminated by illness-causing pathogens, decomposition, the presence of unapproved antibiotic residues, or other hazards.
“The knowledge gained from the pilot will enable the FDA to expand the use of machine learning in the screening of other FDA-regulated products and will inform future risk-based surveillance in products that present the greatest risk to consumers. The ultimate goal is to better protect consumers from unsafe foods by advancing the FDA’s ability to identify potential hazards,” the agency said.
The third phase will help the FDA determine the feasibility of deploying in-house AI and machine learning models using data it collects when reviewing millions of import entries annually.
The FDA is also looking at its seafood project portfolio to expand the use of machine learning to better protect consumers. Machine learning is a type of AI that can help identify connections and patterns that people, or the FDA’s screening system, cannot see. These patterns are applied to incoming supply chains to help predict the likelihood that an import shipment is potentially harmful and not compliant with FDA regulations, according to the agency.
“Machine learning gives the agency the ability to analyze data from various sources to help inform FDA decisions and target our resource at the borders,” the agency said. “There have been enhancements made that determine how machine learning algorithms can best complement field operations and improve the agency’s ability to identify products posing a threat quickly and efficiently.”
The FDA will use data gained from the related shrimp pilot project into the third phase of the AI imported seafood pilot program, “allowing for a more robust and larger targeted sampling,” FDA said.
In 2019, the agency launched the first phase of the pilot, which demonstrated “potential for a machine learning-driven approach to expedite the review of lower-risk seafood shipments, while identifying those of higher risk for violations or refusals,” the FDA said.
The second phase, conducted in the field, was designed to integrate machine learning into existing import data systems to inform decisions about sampling by entry reviewers while gaining more experience with training of the machine learning model. The operational pilot was launched at all 328 U.S. ports of entry from February 2021 through July 2021 and proved successful, according to the FDA. The real-time model was able to analyze an import entry and return a sample recommendation within seconds.
The third phase is expected to be completed in late fiscal year 2023.
Photo courtesy of U.S. Food and Drug Administration