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Record W2160819607 · doi:10.1002/jsfa.2799

Acceptability, storage stability and costing of α‐amylase‐treated maize–beans–groundnuts–bambaranuts complementary blend

2007· article· en· W2160819607 on OpenAlexfundno aff
Victor Owino, Moses Sinkala, Beatrice Amadi, Andrew Tomkins, Suzanne Filteau

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2007
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersNutrition Third WorldInstitut national de la recherche scientifique
KeywordsFood scienceAmylaseChemistryMathematicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract The effects of α‐amylase treatment on physical properties, acceptability to mothers, and cost of roasted and extruded maize–beans–groundnuts–bambaranuts complementary porridge recipes were assessed prior to their industrial production. Storage stability of the extruded α‐amylase‐treated fortified blend was assessed at 2 weeks and 6 months by sensory evaluation, peroxide value, water activity and microbiological load. The use of α‐amylase at 0.04% w/w enhanced porridge acceptability and resulted in 88% and 122% increase in flour concentration for roasted and extrusion‐cooked porridge flour, respectively, while maintaining porridge viscosity at 1000‐fold lower than that of traditionally used porridges. The extrusion cooked blend was stable for 6 months. α‐Amylase application increased the unit cost of the developed blend by only 1.4%. The total cost was less than US $ 2 kg−1, half the minimum price of commercially available complementary foods. Further work on marketing and the efficacy of this inexpensive food on growth of infants is warranted. Copyright © 2007 Society of Chemical Industry

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.096
GPT teacher head0.393
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2007
Admission routes1
Has abstractyes

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