MétaCan
Menu
Back to cohort
Record W2514270228 · doi:10.5539/jfr.v5n5p67

Characterization of Two Chickpea Varieties and the Effect of Cooking on their Physico-chemical and Functional Properties of Flours

2016· article· en· W2514270228 on OpenAlexvenueno aff
Victoria Guadalupe Aguilar‐Raymundo, Jorge Fernando Vélez‐Ruiz

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceRaw materialLightnessAbsorption of waterMoistureChemistryMathematicsBotanyBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Considering the nutritional and functional characteristics of chickpea, flours of two varieties of chickpea (“Blanco Noroeste” and “Costa 2004”) were prepared to know the effect of cooking. Thus the objective of this study was to compare their physicochemical and functional properties in both, raw and cooked flours. Physical properties of the grain, for the two varieties were similar, whereas the physicochemical and functional properties of the flours exhibited differences as a function of the variety and the processing. The chickpea cooked flours showed lower lightness and higher redness and yellowness with respect to raw flours. The proximal composition of cooked flours presented significant differences in fat (5.98% - 6.09%) and moisture contents (0.48% - 0.54%) with respect to raw flours. The particle size distribution determined for the raw and cooked flours samples, indicated a unimodal behavior with a wide distribution. The water absorption capacity and oil capacity showed significant difference among flour varieties. For pasting properties, a higher viscosity was measured for Costa 2004 (380 cP) and Blanco Noroeste (272 cP) raw flours, raw flour exhibited better pasting properties than cooked flours.

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 score0.135
Threshold uncertainty score0.228

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 studies0.0000.001
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 head0.044
GPT teacher head0.238
Teacher spread0.193 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicGenetic and Environmental Crop StudiesFrench-language works237,207