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Record W4313415136 · doi:10.3390/ma16010279

Active Food Packaging Made of Biopolymer-Based Composites

2022· review· en· W4313415136 on OpenAlexafffund
Xuanjun Hu, Chao Lu, Howyn Tang, Hossein Pouri, Etienne Joulin, Jin Zhang

Bibliographic record

VenueMaterials · 2022
Typereview
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiopolymerFood packagingMaterials scienceActive packagingBiodegradationAntimicrobialFood industryComposite materialFood scienceChemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Food packaging plays a vital role in protecting food products from environmental damage and preventing contamination from microorganisms. Conventional food packaging made of plastics produced from unrenewable fossil resources is hard to degrade and poses a negative impact on environmental sustainability. Natural biopolymers are attracting interest for reducing environmental problems to achieve a sustainable society, because of their abundance, biocompatibility, biodegradability, chemical stability, and non-toxicity. Active packaging systems composed of these biopolymers and biopolymer-based composites go beyond simply acting as a barrier to maintain food quality. This review provides a comprehensive overview of natural biopolymer materials used as matrices for food packaging. The antioxidant, water barrier, and oxygen barrier properties of these composites are compared and discussed. Furthermore, biopolymer-based composites integrated with antimicrobial agents-such as inorganic nanostructures and natural products-are reviewed, and the related mechanisms are discussed in terms of antimicrobial function. In summary, composites used for active food packaging systems can inhibit microbial growth and maintain food quality.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.054
GPT teacher head0.306
Teacher spread0.252 · 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.

Study designBench or experimental
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

Citations39
Published2022
Admission routes2
Has abstractyes

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