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Record W4200396600 · doi:10.1111/1541-4337.12874

Triggered and controlled release of active gaseous/volatile compounds for active packaging applications of agri‐food products: A review

2021· review· en· W4200396600 on OpenAlexafffund
Amr Zaitoon, Xiaoyu Luo, Loong‐Tak Lim

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2021
Typereview
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Guelph
FundersOntario Agri-Food Innovation Alliance
KeywordsActive packagingFood scienceChemistryFood packagingBusinessBiochemical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Gaseous and volatile active compounds are versatile to enhance safety and preserve quality of agri‐food products during storage and distribution. However, the use of these compounds is limited by their high vapor pressure and/or chemical instability, especially in active packaging (AP) applications. Various approaches for stabilizing and controlling the release of active gaseous/volatile compounds have been developed, including encapsulation (e.g., into supramolecular matrices, polymer‐based films, electrospun nonwovens) and triggered release systems involving precursor technology, thereby allowing their safe and effective use in AP applications. In this review, encapsulation technologies of gases (e.g., CO 2 , ClO 2 , SO 2 , ethylene, 1‐methylcyclopropene) and volatiles (e.g., ethanol, ethyl formate, essential oils and their constituents) into different solid matrices, polymeric films, and electrospun nonwovens are reviewed, especially with regard to encapsulation mechanisms and controlled release properties. Recent developments on utilizing precursor compounds of bioactive gases/volatiles to enhance their storage stability and better control their release profiles are discussed. The potential applications of these controlled release systems in AP of agri‐food products are presented as well.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.060
GPT teacher head0.340
Teacher spread0.280 · 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 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

Citations68
Published2021
Admission routes2
Has abstractyes

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Same venueComprehensive Reviews in Food Science and Food SafetySame topicNanocomposite Films for Food PackagingFrench-language works237,207