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Physical and Mechanical Properties of High‐amylose Rice and Pea Starch Films as Affected by Relative Humidity and Plasticizer

2004· article· en· W2030763008 on OpenAlexafffund
Ghadeer F. Mehyar, Jung H. Han

Bibliographic record

VenueJournal of Food Science · 2004
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsAmylopectinAmyloseStarchOxygen permeabilityUltimate tensile strengthSolubilityRelative humidityPlasticizerElongationChemistryGlycerolMaterials scienceChemical engineeringOxygenPolymer chemistryComposite materialFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT: The tensile properties, water vapor permeability, oxygen permeability at different relative humidities (RH), and water solubility of edible films made of high‐amylose rice starch (RS) or pea starch (PS) were measured and compared with the most commonly used edible films. Photomicrography of starch films shows amylopectin‐rich gels and amylose‐rich granules. The addition of glycerol into starch films made amylose‐rich granules swollen and continuously dispersed between amylopectin‐rich gels. Tensile strength of RS and PS films decreased when RH increased from 51% to 90%, whereas elongation‐at‐break (E) of both films increased when RH increased. Water vapor permeabilities of both films were similar, resulting in 130 to 150 g mm/m 2 /d /kPa. Oxygen permeability of RS and PS were very low (< 0.5 cm 3 μm/m 2 /d/kPa) below 40% RH, and 1.2 to 1.4 at 45% RH. Water solubility of PS film was 32.0%, which is lower than that of RS film (44.4%). Overall high‐amylose rice and pea starch films possess an excellent oxygen barrier property with extremely high stretchability.

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)0.000
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
Metaresearch0.0000.000
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)0.0010.000

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 head0.014
GPT teacher head0.247
Teacher spread0.232 · 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

Citations127
Published2004
Admission routes2
Has abstractyes

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