MétaCan
Menu
Back to cohort
Record W4414531119 · doi:10.1080/10807039.2025.2557592

Distribution and potential health risks of perfluoroalkyl substances (PFAS) in dairy and egg products in Guangzhou

2025· article· en· W4414531119 on OpenAlexaff
Lili Huang, Hongfeng Zhang, Yuting Qin, Yuhua Zhang, Yan Li, Zhijun Bai, Yanyan Wang, Florence Mhungu, Weiwei Zhang

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsHorizon Health Network
Fundersnot available
KeywordsDistribution (mathematics)Milk productsHuman healthAnimal health

Abstract

fetched live from OpenAlex

Poly- and perfluoroalkyl substances (PFAS) are widely studied for their potential toxicity and environmental prevalence. In Guangzhou, South China, PFAS research has focused mainly on environmental samples and aquatic products, with limited data on contamination in commercial foods. This study measured PFAS levels in 104 food products sold in Guangzhou—59 dairy and 45 egg products—using isotope dilution LC-MS/MS. Dietary exposure and health risks were assessed for three age groups: 3–6 years, 7–19 years, and adults. Perfluorooctane sulfonate (PFOS; 6.7%, ND–1.51 ng/g) and perfluorooctanoic acid (PFOA; 3.3%, ND–1.72 ng/g) were detected only in hen eggs. Perfluorononanoic acid (PFNA) appeared in 79.3% of pasteurized milk (ND–0.83 ng/mL) and 100% of fermented milk (0.16–0.49 ng/mL). Perfluorobutanesulfonic acid (PFBS) was the dominant compound in egg products, found in 83.3% of hen eggs (ND–0.83 ng/g) and 93.3% of quail eggs (ND–0.43 ng/g). Health risk assessments indicate no risk from consuming these dairy and egg products for the general population, though children showed higher exposure to PFDS from fermented milk (hazard quotient 0.849). This first age-specific risk assessment of PFAS in local foods provides essential data to guide region-specific dietary safety standards.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.397
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueHuman and Ecological Risk Assessment An International JournalSame topicPer- and polyfluoroalkyl substances researchFrench-language works237,207