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Record W2980487356 · doi:10.5539/jfr.v8n6p49

Determination of Gamma-aminobutyric Acid (GABA) Content in Grains and Cruciferous Vegetable Seeds

2019· article· en· W2980487356 on OpenAlexvenueno aff
Fadwa Al-Taher, Boris Nemzer

Bibliographic record

VenueJournal of Food Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsnot available
Fundersnot available
KeywordsCruciferous vegetablesBrassicaFood scienceChemistryHigh-performance liquid chromatographygamma-Aminobutyric acidDetection limitHorticultureBiologyChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Gamma aminobutyric acid (GABA) has great physiological functions, mainly as a major inhibitory neurotransmitter in the brain, which makes it important for the development of functional foods. This study detected GABA in grains and cruciferous vegetable seeds by using HPLC after pre-column on-line derivatization with diode array detection (DAD) and fluorescence detection (FLD). The limit of quantitation was 2.94 and 2.86 µg/mL with DAD and FLD, respectively. GABA recoveries ranged from 98.8 to 111.2% on both detectors. Intra and inter-day precision showed relative standard deviations, generally, less than 10% for both DAD and FLD. GABA was determined in different grains (flaxseeds, white quinoa seeds, and buckwheat) and cruciferous vegetable seeds (broccoli, kale, daikon radish, mustard, cabbage, and brussels sprouts). Organic broccoli seeds contained the highest amount and mustard seeds the least amount of GABA in the Brassica family with none being detected in organic white quinoa and flaxseeds. A statistically significant difference (p < 0.05) exists between the various lots of the broccoli seeds. GABA is important as a natural source in functional foods.

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 score0.867
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.130
GPT teacher head0.327
Teacher spread0.198 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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