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Record W1495556933 · doi:10.15376/biores.6.2.1707-1718

Impact of furfural on the sugar analysis of prehydrolysis liquor of kraft-based dissolving pulp production process using the HPAEC technique

2011· article· en· W1495556933 on OpenAlexafffund
Abrar Saeed, Pedram Fatehi, Yonghao Ni, Adriaan van Heiningen

Bibliographic record

VenueBioResources · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsFurfuralDissolving pulpSugarChemistryPulp and paper industryKraft paperHydrolysisSugar productionPulp (tooth)ChromatographyCelluloseOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

High performance anion-exchange chromatography with pulse ampero-metric detector (HPAEC-PAD) is a reliable method to systematically determine the sugar contents in pulp and paper waste streams, including bleaching and extraction liquors. We used the same method to determine the sugar content of industrially produced pre-hydrolysis liquor (PHL) from a kraft-based dissolving pulp production process. The analysis showed that the traditional method cannot be applied for sugar analysis, and an improvement on the method was required. In fact, the presence of furfural in the PHL sample was the reason for the required modification. It was noted that the removal of furfural via evaporation could improve the reliability of the HPAEC technique for sugar assessments. If the concentration of furfural was higher than 0.045% (wt.) in the PHL, the error introduced in the sugar analysis was profound. Also, the industrially produced PHL contained more furfural than the laboratory produced PHL under the same hydrolysis conditions. Consequently, the concentration of furfural in the PHL should be taken into account for sugar analysis using the HPAEC technique.

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 score0.245
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.058
GPT teacher head0.279
Teacher spread0.221 · 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

Citations9
Published2011
Admission routes2
Has abstractyes

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