MétaCan
Menu
Back to cohort
Record W2374027067

Research of removal of soybean peptide bitter by enzymolysis fermented by mixed mucedine

2013· article· en· W2374027067 on OpenAlex
Yan Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience and Technology of Food Industry · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsScience North
Fundersnot available
KeywordsAspergillus oryzaeProteaseAspergillus nigerFermentationChemistryFood scienceBitter tasteAspergillusIncubationYield (engineering)ChromatographyEnzymeTasteBiochemistryBotanyBiologyMaterials science
DOInot available

Abstract

fetched live from OpenAlex

The preparation of compound protease solution which fermented by mixed mucedine consist of Aspergillus niger,Aspergillus oryzae and hair mould was researched.On the basis of single factor experiment,technological parameters of preparing compound protease were determined by mixture design method to optimize fermentation conditions as following:inoculum size of mixed mucedine was 2%,the ratio of Aspergillus niger,Aspergillus oryzae and hair mould was 1.59 ∶1 ∶1.95,pH was 5,incubation temperature was 30℃,incubation time was 72 hours.Under the condition of the fermentation process,compound protease solution with high enzyme activity was prepared.Debitterizing experiment was carried out with enzymolysis of soybean peptide by compound protease solution.The experimental results showed that best debitterizing conditions as followings:concentration of protease was 70U/mL,enzymolysis pH was 5,enzymolysis temperature was 60℃,enzymolysis time was 2 hours.Under the condition,the yield of short peptide was higher,the bitter taste value was lower,and effect of debitterizing was better.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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