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Record W2499033639 · doi:10.20286/nova-jmbs-050202

Some Functional Properties of Kafirin Extracted from a- High- Tannin Sorghum Cultivar

2016· article· en· W2499033639 on OpenAlexvenueno aff
Abd Elmoneim O. Elkhalifa, Sarah A. Ahmed, Sohair M. Ahmed

Bibliographic record

VenueNova Journal of Medical and Biological Sciences · 2016
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsSorghumTanninChemistryFood sciencePorosityAgronomyBiology

Abstract

fetched live from OpenAlex

Kafirin was extracted form a whole high tannins sorghum flour (Fetarita). Different functional properties of produced protein were studied and sorghum flour was used as a control. Results showed that the extracted kafirin has a significant low loose and packed bulk densities compared to the control sorghum flour. The porosity of the control (0.374 g/ml) significantly higher than the extracted kafirin (0.196 g/ml). The Carr index for the kafirin was 20.4%, while it was 39.2% for the sorghum flour Alkaline water retention, as well as water and oil holding capacity of the extracted protein were highly significant than the control, and the results showed that kafirin was highly viscous with a water holding capacity of 2.01 ml H2O/g protein, oil holding capacity of 2.10 ml oil/g protein. There is no significant differences between the emulsifying activity of the kafirin and the control, while the kafirin had high emulsifying stability than the control by 11%. No foam capacity and stability were observed for the control, while the kafirin showed 2.75% foam capacity with no foam stability reported. Results shows that the control sorghum flour took longer time (84.9 sec) to completely be wet in cold water than the extracted kafirin (25.0 sec). SEM results of the kafirin supports the development in functional properties.Keywords: Sorghum, Kafirin, Functional properties; SEM.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.137
GPT teacher head0.294
Teacher spread0.157 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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