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Record W2135226841 · doi:10.1002/ejlt.201300342

Effect of breading and battering ingredients on performance of frying oils*

2014· article· en· W2135226841 on OpenAlexaff
Kelsey Lazarick, Felix Aladedunye, Roman Przybylski

Bibliographic record

VenueEuropean Journal of Lipid Science and Technology · 2014
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCanolaFood scienceIngredientChemistryPigmentOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of pre‐formed lipid hydroperoxides, breading, and battering ingredients on pigment formation and thermo‐oxidative degradation of oil during institutional frying was evaluated. The food ingredients were fried intermittently in canola oil heated for 8 h daily over 4 consecutive days at 185 ± 5°C. Color component formation, total polar components (TPC), and tocopherols were measured. Glycine‐enriched whey protein fried in 1% oxidized canola oil contributed most significantly to oil darkening with a rate ten times that of the control sample. Using whey protein as a base for battering caused the most significant color changes and thermo‐oxidative deterioration. Glucose and glycine are two minor ingredients that also contribute to color formation in oil. Breading materials were prone to cause a more significant amount of oil deterioration when compared to battering ingredients most likely due to excess loose breading particles falling into the oil during frying. Practical applications: The present study evaluated the effect of some components of food coatings on the stability and pigment formation of the frying oil. The results suggest the need to optimize the protein component of coating materials and ensure that the amounts of loose particles on breaded products are adequately minimized. This information will assist institutional frying operators and other relevant industries in product development and food preparation with the view of optimizing performance of the frying oil.

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)0.003
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 score0.175
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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