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Characterisation of pore properties of deep‐fat‐fried chicken nuggets breading coating using mercury intrusion porosimetry technique

2010· article· en· W1530362258 on OpenAlexaff
Akinbode A. Adedeji, Michael Ngadi

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMcGill University
Fundersnot available
KeywordsPorosimetryPorosityMaterials scienceMercury intrusion porosimetryVolume (thermodynamics)MineralogyMoistureBulk densityComposite materialChemistryPorous mediumGeology

Abstract

fetched live from OpenAlex

Summary The objective of this study was to characterise the pore properties of deep‐fat‐fried chicken nuggets coating under different processing conditions namely frying temperatures (170, 180 and 190 °C) and time (0–240 s) using porosimetry technique. Porosity range obtained was between 39.93 and 68.99%. Porosity of the freeze‐dried samples decreased with frying time. The main effect of temperature on porosity was significant ( P < 0.05). Porosity showed a high positive and negative correlation with moisture and fat contents, and the correlation coefficients ranged between 0.88 and 0.96 and 0.78 and 0.8, respectively. Bulk density increased with frying time, while apparent density was relatively the same. Pore distribution showed bimodality. There was no significant effect of temperature on pore size distribution. Over 70% of the pore volume is made up of pores greater than 1 μm. Pore volume ranged between 0.54 and 1.5 cm 3 g −1 , and it decreased with frying time. Mean pore diameter was between 0.006 and 389 μm, while with frying time, it ranged between 0.25 and 8.32 μm. Total pore area was between 2.53 and 16.53 m 2 g −1 . Hysteresis phenomenon showed that some of the pores were not perfectly cylindrical in shape.

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)0.001
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 score0.029
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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

Citations18
Published2010
Admission routes1
Has abstractyes

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