MétaCan
Menu
Back to cohort
Record W2998277092 · doi:10.4236/abc.2019.96015

Enzymatic Hydrolysis of Hairtail Surimi in an Ultra-High Pressure Bioreactor

2019· article· en· W2998277092 on OpenAlexaff
Deqing Yang, Rong Liu, Yongsheng Wang, OU Min-rui, Junjie Gu

Bibliographic record

VenueAdvances in Biological Chemistry · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsPapainChemistryHydrolysisAmino acidProteaseTrypsinChromatographyEnzymatic hydrolysisEnzymeProteasesBioreactorDigestion (alchemy)BiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Amino acids have been extracted from Hairtail surimi using enzymes in an ultra-high pressure bioreactor. The extraction efficiency of different enzymes including papain, trypsin, and proteases (acid, neutral, alkaline) also has been evaluated, and it has been discovered that neutral protease behaved the best. The amino acids were analyzed using automatic amino acid analyzer, and the enzymatic digestion conditions were optimized. For neutral protease, the optimal condition was 50℃, 250 MPa, pH 7.0. Material to liquid ratio of enzyme is 6%. More than 29 amino acids were detected after 24 hours of hydrolysis; the enzymatic hydrolysis rate can reach 83.29%. The results show that enzymatic digestion under ultra-high-pressure provides a very promising approach to extract amino acids from Hairtail surimi.

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 score0.001
Threshold uncertainty score0.002

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)0.0000.000

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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Biological ChemistrySame topicMicrobial Inactivation MethodsFrench-language works237,207