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Record W1987760115 · doi:10.6000/1927-5129.2015.11.36

Effect of Taro Starch-Hydrocolloids Mixture as a Functional Ingredient on the Quality of Milk Dessert

2015· article· en· W1987760115 on OpenAlexvenueno aff
Feroz Alam, Anjum Nawab, Tanveer Abbas, Mohib R. Kazimi, Abid Hasnain

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2015
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsSyneresisFood scienceCarboxymethyl celluloseGuar gumStarchIngredientXanthan gumArabicChemistryGum arabicGuarMaterials scienceRheologySodiumOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The study was carried out to investigate the effect of taro starch-hydrocolloids mixture on the physical and sensory properties of milk dessert. Four different hydrocolloid i.e. arabic, Carboxymethyl cellulose (CMC), guar and xanthan gums were mixed with taro starch in different concentration and their potential use in milk dessert as functional ingredients were evaluated. Physical and sensory characteristics were found to be considerably improved in different aspects by adding these functional ingredients. Taro starch-arabic gum blend was observed to be an effective additive to produce creamy texture of milk dessert. Syneresis from dessert was noted to be diminished and sensory characteristics were also found to be improved by adding taro starch-guar gum blend. Similarly, taro starch-xanthan gum blend has effectively been stabilized the texture and sensory properties of milk dessert.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.055
GPT teacher head0.319
Teacher spread0.263 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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