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Record W2408353673 · doi:10.5539/jfr.v5n3p61

Ellagic Acid May Improve Mechanical and Barrier Properties in Films of Starch-A Review Paper

2016· article· en· W2408353673 on OpenAlexvenueno aff
Juan Manuel Tirado‐Gallegos, David R. Sepúlveda, Paul Baruk Zamudio‐Flores, María L. Rodríguez-Marín, Francisco Hernández-Centeno, R. Salgado-Delgado

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsEllagic acidStarchDifferential scanning calorimetryThermogravimetric analysisFood packagingFourier transform infrared spectroscopyPolymerMaterials scienceBioplasticAntioxidantChemical engineeringChemistryOrganic chemistryFood scienceWaste managementPolyphenolEngineering

Abstract

fetched live from OpenAlex

<p>Packaging increases the shelf life of food and facilitates its handling, transportation and marketing. The main packaging materials are plastics derived from petroleum, but their accumulation has given rise to environmental problems. An alternative is the use of biodegradable materials. In this regard, starch is an excellent choice because it is an abundant and renewable source with film-forming properties. However, the films obtained from starch have some limitations with respect to their mechanical and barrier properties. Several strategies have been developed in order to improve these limitations, ranging from the addition of lipids to the modification of the polymer structure. The aim of this review was propose the use of ellagic acid as a cross-linking agent that may improves the mechanical and barrier properties in films based on exists reports that phenolic compounds interact with starch-based materials decreasing their rate of retrogradation. Furthermore, ellagic acid is a powerful natural antioxidant, which would allow the production of active packaging with antioxidant properties, in addition to the improvement of the mechanical and barrier properties of starch films. In this concern more studies such as Fourier transform infrared spectroscopy, X-ray diffraction, differential scanning calorimetry and thermogravimetric analysis are necessary to verify the structural changes and interactions between starch and ellagic acid. We expect extensive use of it in the future of packaging materials.</p>

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)0.005
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.059
GPT teacher head0.333
Teacher spread0.274 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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