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Record W2137584076 · doi:10.1021/jf070518n

Carbohydrate and Mineral Removal during the Production of Low-Phytate Soy Protein Isolate by Combined Electroacidification and High Shear Tangential Flow Ultrafiltration

2007· article· en· W2137584076 on OpenAlexaff
Jana Skorepova, Christine Moresoli

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDiafiltrationChemistryUltrafiltration (renal)Cross-flow filtrationChromatographySoy proteinPermeationFoulingFiltration (mathematics)Membrane foulingMembraneMembrane technologyMicrofiltrationFood scienceBiochemistry

Abstract

fetched live from OpenAlex

In this work, soy protein isolates were produced by a combination of electroacidification and high shear tangential flow hollow fiber ultrafiltration with a 100 kDa membrane under constant pressure. The filtration performance was evaluated by comparing the filtration time and the final product composition for an electroacidified (pH 6) and a non-electroacidified (pH 9) soy protein extract. The removal of carbohydrates during the filtration was always consistent with the theoretical predictions (based on free permeability assumption) for both the electroacidified and the non-electroacidified feeds. A higher removal of calcium, magnesium, and phytic acid was achieved during the filtration of the electroacidified feed compared to the non-electroacidified feed. However, the electroacidification pretreatment had a negative impact on the permeate flux and resulted in more significant membrane fouling with correspondingly longer filtration times. A discontinuous diafiltration enhanced the removal of carbohydrates and minerals, thus yielding a product with higher protein content but was unable to improve the permeate flux for the electroacidified feed.

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 score0.085
Threshold uncertainty score0.192

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 head0.006
GPT teacher head0.172
Teacher spread0.167 · 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

Citations25
Published2007
Admission routes1
Has abstractyes

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