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Record W2162939932 · doi:10.5539/jfr.v2n4p11

Effect of High Pressure Processing on Color and Textural Properties of Eggs

2013· article· en· W2162939932 on OpenAlexafffundvenue
Ajaypal Singh, Hosahalli S. Ramaswamy

Bibliographic record

VenueJournal of Food Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsEgg whiteYolkChemistryFactorial experimentFood scienceColor differencePascalizationTexture (cosmology)Animal scienceHigh pressureBiologyMathematics

Abstract

fetched live from OpenAlex

Effect of high pressure processing (HPP) on physicochemical characteristics like color and texture of whole liquid egg (WLE), egg white (EW) and egg yolk (EY) were evaluated. A full factorial design involving several pressure levels (600-900 MPa) and treatment time (0-15 min) was employed for this study and the high pressure treatment were given in a temperature and pressure controlled pilot scale HP unit. HPP caused significant changes in various physic-chemical properties in various egg components. Use of pressure levels > 600 MPa resulted in formation of solid gels for all components of eggs. Pressure induced gels were soft and highly elastic. Hardness and cohesiveness of all egg components were found to increase (p < 0.05) with increase in treatment intensity, and increase in EY was higher than in other egg components. The springiness of WLE increased with pressure and treatment time and were higher than in EW and EY. Color changes as indicated by the total color difference (?E) showed a significant (p < 0.05) increase with an increase in pressure level and treatment time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.044
GPT teacher head0.387
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2013
Admission routes3
Has abstractyes

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