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Record W2800029436 · doi:10.1111/ijfs.13813

Aquafaba, wastewater from chickpea canning, functions as an egg replacer in sponge cake

2018· article· en· W2800029436 on OpenAlexaff
Rana Mustafa, Yue He, Youn Young Shim, Martin J. T. Reaney

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsGenome PrairieUniversity of Saskatchewan
FundersInstitute of International Education
KeywordsEgg whiteFood scienceSpongeTexture (cosmology)Materials scienceMathematicsBiologyBotany

Abstract

fetched live from OpenAlex

Summary Aquafaba, the viscous liquid resulting from cooking chickpeas in water is typically discarded. However, this solution is now widely used by the vegan community as an egg replacement that adds texture to food products, such as mayonnaise, pudding, ice cream and baked goods. Sponge cake was prepared with either egg white or aquafaba derived from ten different brands of canned chickpea and the texture and colour were compared. Aquafaba obtained from each chickpea can produced foam which differed in both properties and stability. In addition, aquafaba from some brands provided comparable foam volume and stability to that achieved with egg white. The colour and texture of sponge cake made with either egg white or aquafaba were similar and acceptable, but cakes prepared with aquafaba were less springy, and less cohesive than cake that included egg white. Based on our results, it appears that aquafaba has potential to replace egg white in eggless cake recipes.

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 score0.004
Threshold uncertainty score0.008

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations195
Published2018
Admission routes1
Has abstractyes

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