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Record W3181018891

Evaluation of quality characteristics of bread from kodo, little and foxtail millets

2013· article· en· W3181018891 on OpenAlexaff
P Karuppasamy, D. Malathi, P. Banumathi, N. Varadharaju, Koushik Seetharaman

Bibliographic record

VenueInternational Journal of Food and Nutritional Sciences · 2013
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFoxtailFood scienceWheat flourBread makingShelf lifePopulationChemistryMathematicsAgronomyBiology
DOInot available

Abstract

fetched live from OpenAlex

Small millets incorporated breads were standardized by incorporating millet flour viz., kodo millet, little millet and foxtail millet at 10, 20, 30, 40, 50, 60 and 70% levels. The developed bread was evaluated for their sensory attributes and it was highly acceptable at 20% level of small millets incorporation. The developed products were analyzed for their physico-chemical properties. The incorporation of millet flour increased the bread characteristics such as height, weight, specific volume, bulk density, water absorption and decreased the dough extensibility. As the level of substitution increases, the whiteness index increased and the yellowness index decreased for the developed millet bread. The staleness of bread crumb was increased on storage. The texture profiles like springiness, cohesiveness and resilience were decreased. The fibre content of the millet bread was 1.31g, 1.46g and 1.53g for kodo, little and foxtail millet bread respectively and was higher than the control (0.36g) bread. The calcium and iron content of the developed bread was 21.54 and 1.96mg, 19.88 and 3.36mg and 22.21mg and 2.28mg per 100g of the kodo, little and foxtail millet bread respectively. The shelf life of the bread was 7 days under ambient condition in different packaging materials and the microbial population was within the safer limit during the storage period.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.185

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 head0.074
GPT teacher head0.340
Teacher spread0.267 · 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 designObservational
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

Citations15
Published2013
Admission routes1
Has abstractyes

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