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Record W2990594161 · doi:10.1002/bbb.2053

Low‐cost recycling production of pectinase to increase the yield and quality of Muzao jujube juice by <i>Aspergillus niger</i>

2019· article· en· W2990594161 on OpenAlexaff
Chun Yang, Jiahong Wang, Chonlong Chio, Xuantong Chen, Ling Zhang, Jiangning Zhang, Zhihong Feng, Jiming Han, Weiying Ding, Wensheng Qin

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides and Plant Cell Walls
Canadian institutionsLakehead University
Fundersnot available
KeywordsPectinaseAspergillus nigerFood scienceYield (engineering)Pulp and paper industryChemistryBiotechnologyEnzymeBiologyEngineeringBiochemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract A low‐cost recycling system for the production of pectinase was built to improve the yield and quality of Muzao date juice and to reduce production costs. An Aspergillus niger strain, Gyx086, was selected from 20 A. niger strains on potato agar plates containing Muzao date residues, and the strain was used to produce an enzyme cocktail with high pectinase activity using Muzao date residues as the medium. The high pectinase activity level was therefore used in the production of Muzao date juice. Under optimized enzymatic hydrolysis conditions, a date juice yield of 92.88% was obtained, which was 11% higher than the control, and was not significantly different from a commercial preparation. Further storage trials indicated that the clarity, viscosity, storage stability, and soluble solid content could also be greatly improved by enzymatic treatment. These results suggested that the cheap experimental preparation could effectively replace the commercial preparation used for the production of date juice. This is the first systematic report detailing the production of Muzao date juice using a cheap and effective pectinase cocktail, which could promote the development of the date juice industry. © 2019 Society of Chemical Industry and John Wiley &amp; Sons, Ltd

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)0.000
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 score0.051
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.231
Teacher spread0.202 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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