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Record W4399878206 · doi:10.3136/fstr.fstr-d-24-00067

The kinetic analysis of γ-aminobutyric acid (GABA) production in buckwheat after high hydrostatic pressure

2024· article· en· W4399878206 on OpenAlexaff
Genki Onozawa, Daitaro Ishikawa, Hiroyuki Tanji, Tomoyuki Fujii

Bibliographic record

VenueFood Science and Technology Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsConestoga Meat Packers (Canada)
Fundersnot available
KeywordsAminobutyric acidHydrostatic pressureChemistryKinetic energygamma-Aminobutyric acidHydrostatic equilibriumFood scienceBiochemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

We aimed to investigate the effect of high hydrostatic pressure (HHP) on the production of amino acids, including GABA, in buckwheat during preservation. Buckwheat was soaked in 0–0.5 % glutamic acid solution and exposed to HHP treatment at 200 or 400 MPa. The concentrations of amino acids, except glutamic acid, increased with HHP treatment and preservation. GABA production tended to be higher in a 0.3 g/mL Glu solution under 400 MPa. The relationship between initial glutamate concentration and GABA production rate was bell-shaped, with a maximum approximately 50 µmol/g for the 200-MPa and untreated samples. The Km, Ki, and Vmax were calculated from the Michaelis–Menten equation with substrate inhibition. The Km changed after 400-MPa HHP treatment, whereas Vmax and Ki increased in a pressure-dependent manner. Combining Glu and HHP treatment can produce buckwheat with enhanced GABA production.

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 score0.003
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)0.0010.000

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 head0.028
GPT teacher head0.308
Teacher spread0.279 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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