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Record W2903731752 · doi:10.5851/kosfa.2018.e53

Effect of Allium hookeri Root on Physicochemical, Lipid, and Protein Oxidation of Longissimus Dorsi Muscle Meatball

2018· article· en· W2903731752 on OpenAlexaff
Dong-Kyu Yoon, Ji‐Han Kim, Won Yong Cho, Da-Som Ji, Ha-Jung Lee, Jung‐Ho Kim, Chi-Ho Lee

Bibliographic record

VenueKorean Journal for Food Science of Animal Resources · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Quality and Safety Studies
Canadian institutionsUniversity of Saskatchewan
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsTBARSChemistryFood scienceLipid oxidationThiobarbituric acidLongissimus dorsiAntioxidantAscorbic acidDiallyl disulfideBiochemistryLipid peroxidation

Abstract

fetched live from OpenAlex

The antioxidant effects of Allium hookeri root (AHR) were investigated by evaluating lipid and protein oxidation in meatballs during refrigerated storage at 4±1℃. AHR was mixed at concentrations of 0.5% (w/w, T2) and 1% (w/w, T3) with minced longissimus dorsi muscle. Meatballs containing AHR (T2 and T3) were compared to those containing 0.05% (w/w) ascorbic acid (T1) as a reference and without antioxidant as a control. The 2-thiobarbituric acid reactive substances (TBARS) value, disulfide bond formation, carbonyl contents, and volatile basic nitrogen (VBN) value of T2 were lower than those of the control during storage (p<0.05). The pH values of T2 and T3 were higher than that of the control (p<0.05). Texture profile analysis of T2 revealed a lower value compared to the control (p<0.05). Therefore, the VBN value, TBARS value, disulfide bond formation, and carbonyl content in meatball containing AHR were lower than those of the control meatball. These results indicate that AHR improves the quality of meat products and functions as an antioxidant.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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