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Record W51030892 · doi:10.1177/156482650702800103

Improvement of the Nutritional Quality of a Traditional Complementary Porridge Made of Fermented Yellow Maize ( <i>Zea Mays):</i> Effect of Maize–Legume Combinations and Traditional Processing Methods

2007· article· en· W51030892 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood and Nutrition Bulletin · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPeanut Plant Research Studies
Canadian institutionsUniversité LavalMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
FundersWorld Health Organization
KeywordsLegumeGerminationRoastingFood scienceFermentationNutrientArachisBiologyZea maysAgronomyBiotechnologyChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Blends with a cereal-legume ratio of 70:30 have been introduced in many communities for use in the preparation of complementary foods with augmented protein quality. These foods should meet World Health Organization estimated energy and nutrient needs from complementary foods. OBJECTIVE: To increase energy and nutrient densities and nutrient availability in a traditional complementary porridge. METHODS: Yellow maize was processed by lactic acid fermentation. Peanuts (Arachis hypogea) and beans (Phaseolus vulgaris) were processed by germination, roasting, dehulling, and a combination of germination and roasting. Blends were prepared from processed peanuts and beans and cooked into porridges with viscosities less than 3,000 cp. Traditional porridge was the control and consisted of fermented yellow maize only. The porridges were analyzed for their physicochemical and nutritional properties. RESULTS: Blends increased energy and nutrient densities in porridges compared with the control (p < .05). The maize-peanuts combination yielded porridges with higher energy densities and improved nutritional quality compared with the maize-beans combinations. In vitro availability of iron did not change (p > .05) with formulation of the blends except for porridges made from maize and germinated peanuts, but there was a significant increase in zinc in vitro availability, whereas a decrease was observed for calcium in vitro availability. The energy densities of maize-peanuts porridges were sufficient to cover energy required from complementary foods for infants aged 6 to 11 months receiving four meals of complementary foods per day and an average amount of energy from breastmilk. CONCLUSIONS: Maize-legume blends can efficiently improve the nutritional quality of traditional porridge. Peanuts are the best legume complements.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.247

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 head0.059
GPT teacher head0.306
Teacher spread0.248 · 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