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Record W2113855399 · doi:10.5539/jfr.v4n2p159

Effect of Germination on Functional Properties and Degree of Starch Gelatinization of Sorghum Flour

2015· article· en· W2113855399 on OpenAlexvenueno aff
Ocheme Boniface Ocheme, Olajide Emmanuel Adedeji, Lawal Garba, U. M. Zakari

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsGerminationSorghumAbsorption of waterStarchChemistryFood scienceEmulsionStarch gelatinizationAgronomyHorticultureBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

<p>Sorghum grains were germinated for 24, 48 and 72 hours with a view to determining the effect of germination on some functional properties and degree of starch gelatinization of the flour. Flour from non-germinated grains served as control. In order to measure the effect of germination on degree of starch gelatinization, the flours were processed into cookies. Germination of sorghum grains for 48 hours and above significantly (p<0.05) decreased both loose and packed bulk densities from 0.59 g/ml and 0.77 g/ml to 0.56 g/ml and 0.70 g/ml respectively. The water absorption capacity of the sample germinated for 72 hours was 1.38 g/g which was significantly (p<0.05) higher than the other samples. The oil absorption capacity of the samples germinated for 48 and 72 hours (1.16 and 1.18 g/g respectively) were significantly (p<0.05) higher than those of the control sample and 24 hour germination (1.03 and 1.04g/g respectively). Germination also significantly (p<0.05) increased the swelling power (22-23.2 ml/g), foaming capacity (14-16.2%) and emulsion capacity (58.6-65.5%). The degree of starch gelatinization increased with increasing germination time but decreased with increasing temperature. Generally, germination had a beneficial effect on the functional properties measured. Flour obtained from sorghum grains germinated for 72 hours had the best results.</p>

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)0.003
metaresearch head score (Gemma)0.001
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 score0.133
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.253
GPT teacher head0.372
Teacher spread0.119 · 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

Citations42
Published2015
Admission routes1
Has abstractyes

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