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Record W4313897940 · doi:10.1016/j.jafr.2023.100497

Nutrient, amino acids, phytochemical and antioxidant activities of common Nigeria indigenous soups

2023· article· en· W4313897940 on OpenAlexaff
Ayo Oluwadunsin Olugbuyi, Timilehin David Oluwajuyitan, Ibidapo Nathaniel Adebayod, Ugochukwu Miracle Anosike

Bibliographic record

VenueJournal of Agriculture and Food Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhytochemicalAntioxidantFood scienceTanninChemistryDPPHAmino acidNutrientEssential amino acidTraditional medicineBotanyBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Indigenous vegetables have gained attention due to their dietary fibre, mineral, amino acids, phytochemicals, and antioxidant activities. Bitter leaf (BL); Efinrin leaf (EF); Koro owu or cotton seed leaf (KO); Marugbo leaf (MA); Ewedu leaf (EW) and Ira leaf (IR) are common Nigeria indigenous vegetables. These vegetables were processed into soup and analysed for proximate, mineral, amino acids, phytochemicals, and antioxidant activities. The crude fibre content of the vegetable ranged from 2.70 g/100 g in BL to 8.63 g/100 g in MA. It was observed that MA soup had the highest calcium content (42.89 mg/100 g) while the phytochemicals (mg/g) - tannin, phytate, oxalate, total phenol and total flavonoids values ranged as follows: 0.80–7.30; 2.00–32.76; 0.63–5.77; 1.06–9.12 and 0.61–19.50 respectively. The branch chain amino acid content ranged from 10.75 in MA to 14.67 in BL, total non-essential amino acids (35.32 in BL – 57.97 g/100 g in KO) and total essential amino acid (25.70 in MA – 32.34 g/100 g in KO) respectively. The DPPH ranged between 32.32 and 97.24%. Hence, consumption of these processed Nigerian indigenous vegetables soups may be beneficial in prevention and treatment of diet related diseases.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.178

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 science0.0000.000
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 head0.036
GPT teacher head0.277
Teacher spread0.241 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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