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Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food

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...

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Autores principales: Guo, Wenjing, Archer, Jeffrey, Moore, Morgan, Shojaee, Sina, Zou, Wen, Ge, Weigong, Benjamin, Linda, Adeuya, Anthony, Fairchild, Russell, Hong, Huixiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865765/
https://www.ncbi.nlm.nih.gov/pubmed/33525602
http://dx.doi.org/10.3390/molecules26030685
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author Guo, Wenjing
Archer, Jeffrey
Moore, Morgan
Shojaee, Sina
Zou, Wen
Ge, Weigong
Benjamin, Linda
Adeuya, Anthony
Fairchild, Russell
Hong, Huixiao
author_facet Guo, Wenjing
Archer, Jeffrey
Moore, Morgan
Shojaee, Sina
Zou, Wen
Ge, Weigong
Benjamin, Linda
Adeuya, Anthony
Fairchild, Russell
Hong, Huixiao
author_sort Guo, Wenjing
collection PubMed
description 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.
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spelling pubmed-78657652021-02-07 Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food Guo, Wenjing Archer, Jeffrey Moore, Morgan Shojaee, Sina Zou, Wen Ge, Weigong Benjamin, Linda Adeuya, Anthony Fairchild, Russell Hong, Huixiao Molecules Article 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. MDPI 2021-01-28 /pmc/articles/PMC7865765/ /pubmed/33525602 http://dx.doi.org/10.3390/molecules26030685 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Wenjing
Archer, Jeffrey
Moore, Morgan
Shojaee, Sina
Zou, Wen
Ge, Weigong
Benjamin, Linda
Adeuya, Anthony
Fairchild, Russell
Hong, Huixiao
Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
title Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
title_full Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
title_fullStr Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
title_full_unstemmed Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
title_short Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food
title_sort software-assisted pattern recognition of persistent organic pollutants in contaminated human and animal food
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865765/
https://www.ncbi.nlm.nih.gov/pubmed/33525602
http://dx.doi.org/10.3390/molecules26030685
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