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Record W2015581831 · doi:10.1016/j.lwt.2010.08.007

Effect of acid hydrolysis on rheological and thermal characteristics of lentil starch slurry

2010· article· en· W2015581831 on OpenAlexaff
Jasim Ahmed, Rafael Auras

Bibliographic record

VenueLWT · 2010
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsPolymer Source (Canada)
Fundersnot available
KeywordsStarchHydrolysisRheologyStarch gelatinizationCreepSlurryAcid hydrolysisChemistryChemical engineeringIsothermal processKineticsMaterials scienceActivation energyEnthalpyComposite materialOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

A comparative rheological and thermal study was carried out between acid hydrolyzed and unhydrolyzed (control) lentil starch dispersions (25–33.3 g starch per 100 g water) as function of temperature. After acid hydrolysis, the peak gelatinization temperature (Tp) shifted to higher temperature than the corresponding starch without hydrolysis whereas the gelatinization enthalpy remained unaffected by hydrolysis. The starch gelatinization kinetics was evaluated by a non-isothermal technique as function of elastic modulus (G′) and G′ vs. time (t) data up to the gelatinization peak value was considered for rate estimation. A 2nd-order reaction kinetics described well the starch gelatinization process and the process activation energy was ranged between 241 and 434 kJ/mol. Acid hydrolysis strongly affected the rheological properties by lowering gel strength compared to unhydrolyzed starch. The creep analysis further revealed that starch gel was significantly affected by hydrolysis and exhibited less resistant to the stress. A 4-parameters Burgers model well-described creep curves and supported oscillatory rheological data.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations55
Published2010
Admission routes1
Has abstractyes

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