MétaCan
Menu
Back to cohort
Record W2380523726

Effect of Cryopreservation on Functional Properties of Soy Protein Isolates

2014· article· en· W2380523726 on OpenAlexaff
Hu Xu

Bibliographic record

VenueFood Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsScience North
Fundersnot available
KeywordsEmulsionWater holding capacitySoy proteinCryopreservationChemistryFood scienceMaterials scienceBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

The changes in functional properties of soybean protein isolates(SPI) before and after freezing were investigated to reveal the effect of cryopreservation conditions, namely sample concentration, freezing temperature, freezing time on functional properties of SPI including water-holding capacity, oil-holding capacity, emulsifying property and texture property. The results showed that reduced SPI concentration could lead to an initial increase and then a final decrease in water-holding capacity, oil-holding capacity and emulsifying property. When SPI concentration was 1:12, the waterholding capacity, oil-holding capacity, emulsion stability, hardness and elasticity achieved maximum levels. When freezing temperature was-18 ℃, the water-holding capacity, oil-holding capacity, emulsion stability, hardness and elasticity reached maximum levels; however, when freezing temperature was-20 ℃, the best emulsion stability was observed. With the prolongation of freezing time, the emulsion stability of SPI gradually decreased, reaching the highest level when freezing time was 3 days; after 2 days of freezing, the highest water-holding capacity was achieved. Compared with unfrozen samples, the functional properties of frozen SPI were obviously weakened, indicating poorer water-holding capacity, oilholding capacity, emulsifying property and texture property.

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)0.001
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 score0.008
Threshold uncertainty score0.110

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.211
Teacher spread0.178 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueFood ScienceSame topicProteins in Food SystemsFrench-language works237,207