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Record W4286852937 · doi:10.3791/56305-v

Composition and Properties of Aquafaba: Water Recovered from Commercially Canned Chickpeas

2018· article· en· W4286852937 on OpenAlexaff
Youn Young Shim, Rana Mustafa, Jianheng Shen, Kornsulee Ratanapariyanuch, Martin J. T. Reaney

Bibliographic record

VenueJournal of Visualized Experiments · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsGenome PrairieUniversity of Saskatchewan
Fundersnot available
KeywordsUltrafiltration (renal)Composition (language)BrineFood scienceChemistryViscosityChromatographyMaterials scienceChemical engineeringComputer scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Chickpea and other pulses are commonly sold as canned products packed in a thick solution or a brine. This solution has recently been shown to produce stable foams and emulsions, and can act as a thickener. Recently interest in this product has been enhanced through the internet where it is proposed that this solution, now called aquafaba by a growing community, can be used a replacement for egg and milk protein. As aquafaba is both new and being developed by an internet based community little is known of its composition or properties. Aquafaba was recovered from 10 commercial canned chickpea products and correlations among aquafaba composition, density, viscosity and foaming properties were investigated. Proton NMR was used to characterize aquafaba composition before and after ultrafiltration through membranes with different molecular weight cut offs (MWCOs of 3, 10, or 50 kDa). A protocol for electrophoresis, and peptide mass fingerprinting is also presented. Those methods provided valuable information regarding components responsible for aquafaba functional properties. This information will allow the development of practices to produce standard commercial aquafaba products and may help consumers select products of superior or consistent utility.

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 score0.001
Threshold uncertainty score0.184

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 head0.061
GPT teacher head0.345
Teacher spread0.285 · 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
Published2018
Admission routes1
Has abstractyes

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