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Record W2768051449 · doi:10.5539/jfr.v6n6p74

Profiling of Phenolic Compounds in Sprouted Common Beans and Bambara Groundnuts

2017· article· en· W2768051449 on OpenAlexvenueno aff
Vincent Nyau, S. Prakash, José A. Rodrigues, Jill M. Farrant

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryCaffeic acidPolyphenolPhytochemicalKaempferolQuercetinCatechinQuinic acidSproutingFood scienceGlucosideBotanyBiochemistryBiologyAntioxidant

Abstract

fetched live from OpenAlex

Consumption of sprouted legumes is a popular trend at household level in Africa at the moment. This study investigated the effect of sprouting on the phenolic phytochemicals of the red bambara groundnuts and red beans. Plant-derived phenolic compounds are important as antioxidants in our diet. Phenolic phytochemical profiling was performed using HPLC-PDA-ESI-MS and Folin Ciocalteu assay. There were noticeable changes in the phenolic profiles due to sprouting of the two legumes. The total polyphenol content increased by 1.3-fold and 3-fold after sprouting in red bambara groundnuts and red beans respectively. The HPLC-PDA-ESI-MS profiling of the methanolic extracts of the sprouts revealed new emerging compounds. In red bambara groundnuts, eleven new compounds emerged. The new compounds identified include caffeic acid hexoside, resveratrol glucoside, a caffeic acid derivative, naringenin and kaempferol glucoside. In red beans, eight new compounds emerged. Catechin glucoside, quercetin-3-O-glucoside, quercetin-3-rutinoside, luteolin hexoside, quercetin glucoside acylated and p-coumaric acid hexoside were new compounds identified. Sprouting therefore enhances the polyphenolic profiles of the two legumes.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.146
GPT teacher head0.360
Teacher spread0.215 · 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 designObservational
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

Citations15
Published2017
Admission routes1
Has abstractyes

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