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Record W4280600630 · doi:10.3390/polym14101987

Effect on the Properties of Edible Starch-Based Films by the Incorporation of Additives: A Review

2022· review· en· W4280600630 on OpenAlexaff
Gurvendra Pal Singh, Sneh Punia Bangar, Tianxi Yang, Monica Trif, Vinod Kumar, Dinesh Kumar

Bibliographic record

VenuePolymers · 2022
Typereview
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOxygen permeabilityFood packagingStarchMaterials scienceFiller (materials)Shelf lifeAntimicrobialActive packagingModified starchFood scienceComposite materialAntioxidantChemical engineeringOxygenChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

At present, people more actively pursuing biodegradable-based food packaging to lower the environmental problems of plastic-based packaging. Starch could become a promising alternative to plastic because of its properties (easily available, nontoxic, tasteless, biodegradable, ecofriendly, and edible). This review article is focused mainly on the impact of the properties of starch-based biodegradable films, such as their thickness, morphology, and optical, water-barrier, mechanical, oxygen-barrier, antioxidant, and antimicrobial properties, after the incorporation of additives, and how such films fulfill the demands of the manufacturing of biodegradable and edible food-based film with preferable performance. The incorporation of additives in starch-based films is largely explained by its functioning as a filler, as shown via a reduction in water and oxygen permeability, increased thickness, and better mechanical properties. Additives also showed antimicrobial and antioxidant properties in the films/coatings, which would positively impact the shelf life of coated or wrapped food material.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.294
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations128
Published2022
Admission routes1
Has abstractyes

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