MétaCan
Menu
Back to cohort
Record W2996080560 · doi:10.3390/foods8120685

Chickpea Cultivar Selection to Produce Aquafaba with Superior Emulsion Properties

2019· article· en· W2996080560 on OpenAlexafffund
Yue He, Youn Young Shim, Rana Mustafa, Venkatesh Meda, Martin J. T. Reaney

Bibliographic record

VenueFoods · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsGenome PrairieUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsEmulsionCultivarChemistryFood scienceHorticultureAgronomyBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Aquafaba (AQ), a viscous by-product solution produced during cooking chickpea or other legumes in water, is increasingly being used as an egg replacement due to its ability to form foams and emulsions. The objectives of our work were to select a chickpea cultivar that produces AQ with superior emulsion properties, and to investigate the impact of chickpea seed physicochemical properties and hydration kinetics on the properties of AQ-based emulsions. AQ from a Kabuli type chickpea cultivar (CDC Leader) had the greatest emulsion capacity (1.10 ± 0.04 m2/g) and stability (71.9 ± 0.8%). There were no correlations observed between AQ emulsion properties and chickpea seed proximate compositions. Meanwhile, AQ emulsion properties were negatively correlated with AQ yield and moisture content, indicating that AQ with higher dry-matter content displayed better emulsion properties. In conclusion, the emulsification properties of aquafaba are greatly influenced by the chickpea genotype, and AQ from the CDC Leader chickpea produced the most stable food oil emulsions.

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)0.000
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 score0.199
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.193
Teacher spread0.176 · 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

Citations58
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFoodsSame topicProteins in Food SystemsFrench-language works237,207