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Record W1489190020 · doi:10.1111/1750-3841.12864

Influences of Operating Parameters on the Formation of Furan During Heating Based on Models of Polyunsaturated Fatty Acids

2015· article· en· W1489190020 on OpenAlexaff
Mingyue Shen, Qian Liu, Yujie Jiang, Shaoping Nie, Yanan Zhang, Jianhua Xie, Sunan Wang, Fan Zhu, Mingyong Xie

Bibliographic record

VenueJournal of Food Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsNiagara College
FundersKey Technologies Research and Development Program
KeywordsFuranChemistryPolyunsaturated fatty acidGas chromatographyOrganic chemistryFatty acidChromatographyFood science

Abstract

fetched live from OpenAlex

Furan, a possible carcinogen, is commonly produced by thermal processing in a number of heated foods. The existence of furan levels in foods has attracted considerable attention worldwide. Recent research of furan in food has focused on the possible influences of operating parameters on the furan formation during heat processing. The aim of our study was to investigate the impacts of multiple factors (pH, temperature, heating time, ferric, and glutamic acid) on furan formation using linolenic and linoleic acids-based model systems in which furan was analyzed by headspace gas chromatography-mass spectrometry (HS-GC-MS). The results revealed that the content of furan increased rapidly when the heating temperature was elevated, with the highest levels of furan in neutral buffer solutions, the furan levels were also found to be related to heating time in all model systems. Ferric promoted furan formation from polyunsaturated fatty acids, conversely glutamic acid with an optimum concentration suppressed the furan formation. The minimal level of furan in foods during thermal treatment could be achieved via adding furan formation suppressors, and/or avoidance of furan forming promoter.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.107

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.104
GPT teacher head0.283
Teacher spread0.180 · 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 designBench or experimental
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

Citations41
Published2015
Admission routes1
Has abstractyes

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