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Enregistrement W2923395739 · doi:10.1016/j.envint.2019.03.009

A review on organophosphate Ester (OPE) flame retardants and plasticizers in foodstuffs: Levels, distribution, human dietary exposure, and future directions

2019· review· en· W2923395739 sur OpenAlexaff
Jianhua Li, Luming Zhao, Robert J. Letcher, Yayun Zhang, Kang Jian, Jinhua Zhang, Guanyong Su

Notice bibliographique

RevueEnvironment International · 2019
Typereview
Langueen
DomaineEnvironmental Science
ThématiqueToxic Organic Pollutants Impact
Établissements canadiensCarleton UniversityEnvironment and Climate Change Canada
Organismes subventionnairesFundamental Research Funds for the Central UniversitiesRecruitment Program of Global ExpertsNanjing University of Science and TechnologyNatural Science Foundation of Jiangsu Province
Mots-clésTriphenyl phosphatePlasticizerFood scienceOrganophosphateChemistryEnvironmental chemistryFish <Actinopterygii>PhosphateContaminationToxicologyFood contaminantReference doseEnvironmental sciencePesticideBiologyRisk assessmentFire retardantOrganic chemistryFisheryEcology

Résumé

récupéré en direct d'OpenAlex

Given the ongoing studies on the adverse effects of organophosphate ester (OPE) flame retardants and plasticizers on human health, there is an increasing scientific interest in the risk of exposure to OPEs via dietary intake. Using peer-reviewed literature published up to 2018, this review surveyed and compiled the available and reported data on the concentrations and distributions of 30 OPEs based on their occurrence in various food samples from around the world. Regardless of sampling locations or food categories, 22 OPEs were detectable in at least one of analyzed sample, and there were clear variations in OPE levels among samples from different locations or food categories. For instance, cereals and fats/oils were the most contaminated by OPEs in China and Belgium, whereas fats/oils and desserts were the main polluted products in Sweden. In contrast, vegetables, fruits, fluid dairy products, and cereals were reported as the primary categories of food polluted by OPEs in Australia. Animal-based food categories such as eggs, fish and meat were the least contaminated, whereas the highest median OPE concentrations were found in meat and fish from the United State. The levels and distribution patterns of OPEs in foodstuffs demonstrated a tremendous difference even when collected from the same country and the same food item. Rice from China had the highest tris(2‑chloroethyl) phosphate (TCEP, mean: 29.8 ng/g dw) levels, whereas 2‑ethylhexyl‑diphenyl phosphate (EHDPP, mean: 4.17 ng/g ww), triphenyl phosphate (TPHP, mean: 26.14 ng/g ww), tris(2-chloroisopropyl) phosphate (TCIPP, mean: 0.87 ng/g ww) and tributyl phosphate (TNBP, median: 0.55 ng/g ww) concentrations were the highest in the same food category from Sweden, Belgium, Australia, and the United States, respectively. These discrepancies may be due to a variety of reasons such as differences in OPE physico-chemical properties, extent of usage, uptake, metabolic pathways, industrial food manufacturing processes, OPE level differences as a function of habitat, and accumulation and degradability of OPEs in different species. It is worth noting that, due to its worldwide usage in food packaging materials, EHDPP was more prominently found in processed food compared to non-processed food. Based on reported OPE levels in various foods, this review conducted a preliminary assessment of human exposure to OPEs through dietary intake, which suggested that the OPE estimated daily intake (EDI) for humans was around 880 ng/kg bw/day (95th percentile). This value was well below the corresponding OPE health reference dose given by the U.S. EPA (≥15,000 ng/kg bw/day). Even so, dietary exposure to OPEs via food intake may be not negligible based on some important factors such as dilution effects, cooking processes, and the contribution of as yet unknown means of OPE exposure. Overall, this review highlights several gaps in our understanding of OPEs in foodstuffs: 1) the investigation of contamination levels of OPEs in foodstuffs should be extended to other regions, especially North America and European countries, where OPEs are widely used and frequently detected in environmental samples, and 2) newly identified OPE derivatives/by-products, e.g., OP diesters and hydroxylated metabolites, which have been reported as end-products of OPE enzymatic metabolism or degradation through aqueous hydrolysis, and which may co-exist with parent OPEs, could also be screened with precursor OPEs in foodstuffs in future studies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0050,005
Études des sciences et des technologies0,0000,000
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,034
Tête enseignante GPT0,290
Écart entre enseignants0,256 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations340
Publié2019
Routes d'admission1
Résumé présentoui

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