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Record W1536367715 · doi:10.1111/1750-3841.12716

Mechanical and Microstructural Properties of “Wet” Alginate and Composite Films Containing Various Carbohydrates

2014· article· en· W1536367715 on OpenAlexaff
B. Allison Harper, Shai Barbut, A.K. Smith, Massimo F. Marcone

Bibliographic record

VenueJournal of Food Science · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPectinCelluloseUltimate tensile strengthStarchComposite numberMaterials scienceGellan gumCellulose fiberChemical engineeringPolysaccharideComposite materialCarrageenanFiberChemistryFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Composite "wet" alginate films were manufactured from alginate-carbohydrate solutions containing 5% alginate and 0.25% pectin, carrageenan (kappa or iota), potato starch (modified or unmodified), gellan gum, or cellulose (extracted or commercial). The "wet" alginate films were used as a model to understand co-extruded alginate sausage casings that are currently being used by several sausage manufacturers. The mechanical, optical, and microstructural properties of the calcium cross-linked composite films were explored. In addition, the water holding capacity and textural profile analysis properties of the alginate-carbohydrate gels were studied. The results indicate that the mechanical properties of "wet" alginate films/casings can be modified by adding various carbohydrates to them. Alginate films with pectin, carrageenan, and modified potato starch had significantly (P < 0.05) greater elongation values than pure alginate films. The alginate-pectin films also had greater (P < 0.05) tensile strengths than the pure alginate films. Alginate films with extracted cellulose, commercial cellulose, and modified potato starch had lower (P < 0.05) puncture force, distance, and work values than the alginate control films. Transmission electron microscopy images showed a very uniform alginate network in the control films. Several large cellulose fibers were visible in the films with extracted cellulose, while the cellulose fibers in the films with commercial cellulose were difficult to distinguish. Despite these apparent differences in cellulose fiber length, the 2 cellulose films had similar puncture and tensile properties.

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)0.001
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 score0.007
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.226
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

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
Published2014
Admission routes1
Has abstractyes

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