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Record W2907386198 · doi:10.5713/ajas.18.0689

Effect of aged garlic powder on physicochemical characteristics, texture profiles, and oxidative stability ofready-to-eat pork patties

2019· article· en· W2907386198 on OpenAlexaff
Ji‐Han Kim, Hyun-Joo Jang, Chi-Ho Lee

Bibliographic record

VenueAsian-Australasian Journal of Animal Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFood scienceGARLIC POWDERChemistryLipid oxidationVacuum packingOxidative phosphorylationCooked meatAntioxidantBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to investigate the effects of aged garlic powder (AGP) on physicochemical characteristics, texture profiles, and oxidative stability of ready-to-eat (RTE) pork patties. METHODS: There were five treatment groups: a control; 1% fresh garlic powder (T1); 0.5%, 1%, and 2% AGP (T2, T3, and T4). Pork patties with vacuum packaging were roasted at 71°C for core temperature, stored at 4°C for 14 d, and then reheated for 1 min using a microwave. RESULTS: The AGP groups showed a lower the level of lipid oxidation and higher thiol contents than the control and T1. The pH value of the control increased whereas that of aged garlic groups decreased after re-heating process. In addition, the redness significantly increased with increasing level of AGP whereas the redness of the control and T1 decreased after re-heating process. T4 added patties improved textural and sensory properties compared to the control. CONCLUSION: The results of this study suggest that AGP addition to RTE pork patties can improve their sensory characteristics and oxidative stability.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.266
Teacher spread0.244 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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