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Record W596446507 · doi:10.1007/s13205-015-0312-7

Optimisation of enzymatic hydrolysis of apple pomace for production of biofuel and biorefinery chemicals using commercial enzymes

2015· article· en· W596446507 on OpenAlex

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

Venue3 Biotech · 2015
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
FundersWater Research Commission
KeywordsBiorefineryPomaceEnzymatic hydrolysisChemistryRaw materialHydrolysisBiofuelSugarYield (engineering)Pulp and paper industryFood scienceEthanol fuelBiotechnologyFermentationBiochemistryOrganic chemistryMaterials scienceBiology

Abstract

fetched live from OpenAlex

Apple pomace, a waste product from the apple juice industry is a potential feedstock for biofuel and biorefinery chemical production. Optimisation of hydrolysis conditions for apple pomace hydrolysis using Viscozyme L and Celluclast 1.5L was investigated using 1 L reaction volumes. The effects of temperature, pH, β-glucosidase supplementation and substrate feeding regimes were determined. Hydrolysis at room temperature using an unbuffered system gave optimal performance. Reactors in batch mode resulted in a better performance (4.2 g/L glucose and 16.8 g/L reducing sugar, 75 % yield for both) than fed-batch (3.2 g/L glucose and 14.6 g/L reducing sugar, 65.5 and 73.1 % yield respectively) in 72 h. The addition of Novozyme 188 to the core mixture of Viscozyme L and Celluclast 1.5L resulted in the doubling of glucose released. The main products (yield %) released from apple pomace hydrolysis were galacturonic acid (78 %), glucose (75 %), arabinose (90 %) and galactose (87 %). These products are potential raw materials for biofuel and biorefinery chemical production.

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)0.000
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 score0.011
Threshold uncertainty score0.326

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 head0.038
GPT teacher head0.252
Teacher spread0.214 · 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