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Advances in Protein-Based Materials for Functional Food Packaging

2025· article· en· W4409034729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Food Science & Technology · 2025
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of WaterlooUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFood packagingFood scienceMaterials scienceComputer scienceChemistry

Abstract

fetched live from OpenAlex

The heightened awareness of environmental issues has spurred significant research on sustainable and functional packaging materials. Proteins stand out as promising candidates for such applications due to the natural abundance and versatility of the functional groups available for modification. This review offers an in-depth exploration of recent developments and future prospects of protein-based materials for functional packaging. The biodegradability and renewability of proteins make them environmentally friendly alternatives to conventional packaging materials. From plant-derived proteins to animal-derived counterparts, such as collagen, whey, albumin, and casein, diverse sources of protein feedstock have been examined. As sustainable packaging has gained enormous interest, this review provides insights into the innovative use of protein-based materials and their potential to revolutionize functional packaging practices. Additionally, the review highlights the limited areas for potential future research studies that are imperative for advancing protein-based functional packaging to a level where it can effectively compete with synthetic packaging materials.

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.

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)0.001
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 score0.033
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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