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Deciphering the Plasticizers for the Development of Polysaccharide basedBiodegradable Edible Coatings

2022· article· en· W4296523870 on OpenAlexaff
Vikram Kumar, Sudarshan Singh Lakhawat, Pushpender Kumar Sharma, Sunil Kumar, Aishwarya Pandey

Bibliographic record

VenueCurrent Nutrition & Food Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPlasticizerShelf lifeCoatingMoistureFood scienceEnvironmental scienceMaterials scienceChemistryNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Abstract: There is persistently a high demand for fresh fruits and vegetables all over the world. One of the crucial factors that reduces the shelf life of fruits and vegetables is temperature- dependent oxidation during transportation and long storage. Fruits and vegetables coating using eco-friendly coatings hold great advantage over the other synthetic coating materials. The fruits and vegetables coated with coating can prevent from rapid oxidation even at warm temperatures. It enhances the quality and shelf life and maintain the nutritional properties. Though, edible coatings prove to be beneficial, the major drawbacks associated with it is the vulnerability towards moisture- dependent rapid degradation of these fruits and vegetables. Use of appropriate plasticizers would be helpful in enhancing the moisture and oxidation resistance. The current review article will highlight the use of various plasticizers used with polysaccharide-based coatings.

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 score0.003

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.286
Teacher spread0.242 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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