Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
Wenjing Guo,
Jeffrey Archer,
Morgan Moore,
Sina Shojaee,
Wen Zou,
Weigong Ge,
Linda Benjamin,
Anthony Adeuya,
Russell Fairchild,
Huixiao Hong
Affiliations
Wenjing Guo
National Center for Toxicological Research, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Jeffrey Archer
Office of Regulatory Affairs, Office of Regulatory Science, Arkansas Laboratory, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Morgan Moore
Office of Regulatory Affairs, Office of Regulatory Science, Arkansas Laboratory, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Sina Shojaee
Office of Regulatory Affairs, Office of Regulatory Science, Arkansas Laboratory, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Wen Zou
National Center for Toxicological Research, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Weigong Ge
National Center for Toxicological Research, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Linda Benjamin
Center for Veterinary Medicine, U.S. Food & Drug Administration, 7500 Standish Place, Rockville, MD 20855, USA
Anthony Adeuya
Center for Food Safety and Applied Nutrition, U.S. Food & Drug Administration, 5001 Campus Dr, College Park, MD 20740, USA
Russell Fairchild
Office of Regulatory Affairs, Office of Regulatory Science, Arkansas Laboratory, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Huixiao Hong
National Center for Toxicological Research, U.S. Food & Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
Persistent Organic Pollutants (POPs) are a serious food safety concern due to their persistence and toxic effects. To promote food safety and protect human health, it is important to understand the sources of POPs and how to minimize human exposure to these contaminants. The POPs Program within the U.S. Food and Drug Administration (FDA), manually evaluates congener patterns of POPs-contaminated samples and sometimes compares the finding to other previously analyzed samples with similar patterns. This manual comparison is time consuming and solely depends on human expertise. To improve the efficiency of this evaluation, we developed software to assist in identifying potential sources of POPs contamination by detecting similarities between the congener patterns of a contaminated sample and potential environmental source samples. Similarity scores were computed and used to rank potential source samples. The software has been tested on a diverse set of incurred samples by comparing results from the software with those from human experts. We demonstrated that the software provides results consistent with human expert observation. This software also provided the advantage of reliably evaluating an increased sample lot which increased overall efficiency.
