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Record W3097643012 · doi:10.5772/intechopen.105996

Potential of Indigenous Vegetable-Fortified Food Products for Improved Human Nutrition and Health in West Africa

2023· book-chapter· en· W3097643012 on OpenAlexafffund
A.A. Famuwagun, O.C. Adebooye, T.V. Odunlade, Kehinde A. Taiwo, Durodoluwa Joseph Oyedele, Rotimi E. Aluko

Bibliographic record

VenueSustainable development · 2023
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsUniversity of Manitoba
FundersInternational Development Research Centre
KeywordsAmaranthLeafy vegetablesFood sciencePastryFortified FoodBiotechnologyFortificationBiology

Abstract

fetched live from OpenAlex

The study developed and tested the acceptability of key innovative value-added products, including vegetable-fortified bread and pastry products (cookies and chinchin), using each of these three indigenous vegetables, namely Telfairia occidentalis Hook. F. (fluted pumpkin), Amaranthus virdis L. (local amaranth), and Solanum macrocarpon L. (eggplant). The bread was fortified with each of the individual dried leafy vegetables at levels of 1%, 3%, and 5%. Ogi was also fortified with the equivalent of the dried Telfaria occidentalis in the wet form at 1%, 3%, and 5%, while pastry was fortified with the only extract obtained from amaranth leafy vegetable at 1%, 3%, and 5%. Results from the MicroVeg project showed that bread fortified with 3% (w/w) dried vegetable leaf powders had higher nutritional properties. Pastry formulations that included 3% dried vegetable leaf powders had slightly lower consumer acceptability in terms of taste and color; however, due to the associated potential health benefits, consumers were willing to buy the vegetable-fortified products as replacements for the regular products. This chapter discusses the full details of the nutrition and value addition aspects of MicroVeg with empirical examples of the interventions and the potential contributions to dietary diversity and enhanced family nutrition.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.031
GPT teacher head0.220
Teacher spread0.189 · 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 designNot applicable
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

Citations1
Published2023
Admission routes2
Has abstractyes

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