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Record W2117906585 · doi:10.1002/bip.22344

Influence of amylopectin structure and degree of phosphorylation on the molecular composition of potato starch lintners

2013· article· en· W2117906585 on OpenAlexaff
Jeanette Wikman, Andreas Blennow, A. Buléon, Jean‐Luc Putaux, Serge Pérez, Koushik Seetharaman, Eric Bertoft

Bibliographic record

VenueBiopolymers · 2013
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAmylopectinChemistryCrystallinityStarchDehydrationPhosphateTransmission electron microscopyHydrolysisComposition (language)CrystallographyMelting temperatureBiochemistryChemical engineeringAmylose

Abstract

fetched live from OpenAlex

Morphology, molecular structure, and thermal properties of potato starch granules with low to high phosphate content were studied as an effect of mild acid hydrolysis (lintnerization) to 80% solubilization at two temperatures (25 and 45°C). Light microscopy showed that the lintners contained apparently intact granules, which disintegrated into fragments upon dehydration. Transmission electron microscopy of rehydrated lintners revealed lacy networks of smaller subunits. The molecular composition of the lintners suggested that they largely consisted of remnants of crystalline lamellae. When lintnerization was performed at 45°C, the lintners contained more of branched dextrins compared to 25°C in both low and intermediate phosphate-containing samples. High-phosphate-containing starch was, however, unaffected by temperature and this was probably due to an altered amylopectin structure rather than the phosphate content. After lintnerization, the melting endotherms were broad with decreased onset and increased peak melting temperatures. The relative crystallinity was lower in lintners prepared at 45°C. A hypothesis that combines the kinetics of lintnerization with the molecular and thermal characteristics of the lintners is presented.

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 score0.019
Threshold uncertainty score0.231

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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