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Record W4413655332 · doi:10.1016/j.fochx.2025.102961

Valorization of mango byproducts for sustainable active packaging: Advances in functionalized biopolymer films

2025· review· en· W4413655332 on OpenAlexaff
Guihong Fang, Bangdi Liu, Junyan Guo, Gülden Gökşen, Mansuri M. Tosif, Shima Jafarzade, Parya Ezati, A. Pal Pandi

Bibliographic record

VenueFood Chemistry X · 2025
Typereview
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Guelph
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Hainan Province
KeywordsBiopolymerBiotechnologyPulp and paper industryBusinessNanotechnologyPolymer scienceMaterials scienceEngineeringBiologyComposite materialPolymer

Abstract

fetched live from OpenAlex

Faced with the environmental crisis caused by plastic waste, recent research has focused on the development of biopolymer-based food packaging films with enhanced properties, among which agricultural byproducts are promising resources. This review comprehensively analyzes the application value of different mango byproducts-peel, seeds, and leaves-in sustainable active packaging films. It evaluates the latest progress in utilizing these wastes: peel phenolics and pectins can enhance antioxidant, UV-protective, and smart pH-responsive properties; seed starch, kernel extracts, and oils can improve mechanical strength, thermal stability, and hydrophobicity; and leaf bioactives exhibit strong antimicrobial functions. This work innovatively synthesizes strategies for extracting these functional components and integrating them into various biopolymer matrices (e.g., chitosan, polylactic acid, starch), demonstrating their efficacy in significantly extending the shelf life of perishable foods. This study highlights mango byproducts as a versatile and environmentally friendly alternative that aligns with circular economy principles and has the potential to transform waste into high-value packaging materials-despite challenges in standardization and scalability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.293
Teacher spread0.278 · 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 designSystematic review
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

Citations7
Published2025
Admission routes1
Has abstractyes

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