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Record W2515605835 · doi:10.4236/aces.2016.64033

Relationship between Oil Uptake and Moisture Loss during Deep Fat Frying of Deboned Chicken Breast Meat

2016· article· en· W2515605835 on OpenAlexafffund
Lamin S. Kassama, Michael Ngadi

Bibliographic record

VenueAdvances in Chemical Engineering and Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMoistureChicken breastPetroleum etherFood scienceChemistryExtraction (chemistry)SolventChromatographyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The relationship between moisture loss and oil uptake at different combination of Frying Oil Temperatures (FOT) and time during deep-fat frying of chicken meat was investigated in this study. Chicken meat samples were diced and fried at different FOT (170°C, 180°C, and 190°C) in an industrial fryer for periods varying from 5 to 900 s. Fat analysis was accomplished in a soxhlet extraction apparatus with petroleum ether solvent. Prior to fat analysis samples were freeze dried and the moisture analysis was based on the standard AOAC standard method. The results show that a cook value of 415 s was found to give the most ideal sensory characteristics. The relationship between moisture loss and oil uptake during the initial phase of frying (°C and 190°C, respectively, and the rate (1.35 g/s) at FOT 170°C was significantly (P < 0.05) lower.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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