Phthalate Esters in Foods: Sources, Occurrence, and Analytical Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Phthalates are a group of diesters of ortho-phthalic acid (dialkyl or alkyl aryl esters of 1,2-benzenedicarboxylic acid). Higher-molecular-weight phthalates, such as di-2-ethylhexyl phthalate (DEHP), are primarily used as plasticizers to soften polyvinyl chloride (PVC) products, while the lower-molecular-weight phthalates, such as diethyl phthalate (DEP), di-n-butyl phthalate (DBP), and butyl benzyl phthalate (BBzP), are widely used as solvents to hold color and scent in various consumer and personal care products. Phthalates have become ubiquitous environmental contaminants due to volatilization and leaching from their widespread applications, and thus contamination of the environment has become another important source for phthalates in foods in addition to migration from packaging materials. Human exposure to phthalates has been an increased concern due to the findings from toxicology studies in animals. DEHP, one of the important and widely used phthalates, is a rodent liver carcinogen. DEHP, DBP, BBzP, and several phthalate metabolites, such as monobutyl phthalate, monobenzyl phthalate, and mono-(2-ethylhexyl) phthalate, are teratogenic in animals. Since foods are the major source of exposure to phthalates, information on levels of phthalates in foods is important for human exposure assessment. The objective of this review is to identify the knowledge gaps for future investigations by reviewing levels of a wide range of phthalates in a variety of foods, such as bottled water, soft drinks, infant formula, human milk, total diet foods, and others, migration of phthalates from various food-packaging materials, and traditional and new methodologies for the determination of phthalates in foods.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it