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Record W2160007279 · doi:10.1002/jsfa.7156

Techno‐functional characterization of salad dressing emulsions supplemented with pea, lentil and chickpea flours

2015· article· en· W2160007279 on OpenAlexafffund
Zhen Ma, Joyce I. Boye, Kevin Swallow, Linda Malcolmson, Benjamin K. Simpson

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsMcGill UniversityCanadian International Grains InstituteAgriculture Food and Rural DevelopmentAgriculture and Agri-Food Canada
FundersFundamental Research Funds for the Central UniversitiesAgriculture and Agri-Food CanadaCentral University Basic Research Fund of China
KeywordsFood scienceRheologyYolkSnack foodMaterials scienceChemistryComposite material

Abstract

fetched live from OpenAlex

BACKGROUND: Salad dressings supplemented with pulse flours are novel products. A three-factor face-centered central composite design (CCD) was used to determine the effect of pulse flour concentration (3.5%, 7%, 10.5% w/w), egg yolk concentration (3%, 5%, 7% w/w) and oil concentration (20%, 35%, 50% w/w) on the rheological and color characteristics of salad dressings supplemented with pulse flours. RESULTS: The consistency coefficient m, plateau modulus G(N)(0), recoverable strain Q(t) and color values were all affected by the concentrations of pulse flours used. Scanning electron microscopy showed that dressings with lower oil and egg yolk contents had a less densely packed network compared with dressings with higher oil and egg yolk contents. Sensory results were most promising for salad dressings supplemented with the whole green lentil, yellow pea with low flour content, and chickpea with high oil content. CONCLUSION: This study should be useful for designing novel types of salad dressings to meet market requirements as well as helping to increase pulse consumption.

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 score0.278
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.199
Teacher spread0.179 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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