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Record W2140999036 · doi:10.1002/cjce.20163

Optimization of α‐amylase production in solid substrate fermentation

2009· article· en· W2140999036 on OpenAlexvenueno aff
Muhammet Şaban Tanyıldızı, Veyis Selen, Dursun Özer

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsnot available
Fundersnot available
KeywordsCentral composite designResponse surface methodologyFermentationAmylaseYeast extractSubstrate (aquarium)ChemistryFood scienceMathematicsBiological systemChromatographyBiochemistryEnzymeBiology

Abstract

fetched live from OpenAlex

Abstract Response surface methodology (RSM) was used to optimize the medium components of α‐amylase production using solid substrate fermentation (SSF). Hazelnut cake (HC), peptone, yeast extract (YE), and (NH 4 ) 2 SO 4 were selected as independent variables for optimization. Central composite design (CCD) was used in design experiments and analysis results. This procedure limited the number of actual experiments performed while allowing possible interactions between the independent variables. By using CCD, 30 experiments were performed for determining the interaction of independent variables and optimization of fermentation medium. The P ‐value of the coefficient of linear effect of (NH 4 ) 2 SO 4 concentrations, which was obtained as 0.0001 has shown that this parameter has the greatest effect on the production of α‐amylase. Model F ‐value (5.62) implies that the model is significant. The highest α‐amylase activity (4895 IU) was measured when the HC, peptone, YE, and (NH 4 ) 2 SO 4 concentrations in the medium were 22.62, 5.20, 1.62, and 6.81 g L −1 , respectively.

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 score0.209
Threshold uncertainty score0.157

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 head0.206
Teacher spread0.200 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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