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Tuning the mechanical properties of pectin films with polyphenol-rich plant extracts

2023· article· en· W4387783564 on OpenAlexaff
Joseph Robert Nastasi, Melissa Fitzgerald, Vassilis Kontogiorgos

Bibliographic record

VenueInternational Journal of Biological Macromolecules · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides and Plant Cell Walls
Canadian institutionsUniversity of British Columbia
FundersAustralian Research Council
KeywordsPectinPolyphenolChemistryBotanyFood scienceOrganic chemistryBiologyAntioxidant

Abstract

fetched live from OpenAlex

The mechanical properties of pectin films enhanced with polyphenol-rich fruit extracts were investigated. The scavenging and reducing activity of plant extracts incorporated into the pectin films were determined using bench assays, and their antioxidant activity was correlated with a high presence of polyphenols, which were predominantly comprised of flavonoids and anthocyanins. The pectin films generated from the extracts exhibited a range of mechanical properties; tensile strength (4.99 MPa – 6.91 MPa), elongation at break (45.8 % - 52.3 %), and stiffness (1835 g mm−1 - 2765 g mm−1). To investigate the underlying relationships between plant extract composition and mechanical properties, Projection to Latent Structures (PLS) models were developed. The PLS models revealed that extracts containing high sugar and polyphenol content increase the tensile strength and moisture content of films. The elongation at break of the films was improved or diminished depending on the profile of sugar, acids, and polyphenols in the fruit extracts. Furthermore, the structures and concentration of anthocyanins and flavonoids were identified to strongly influenced the elongation at break differences. By modifying the concentration of sugars, organic acids, and polyphenols, the mechanical properties of pectin-based films can be tuned for tailored applications as food 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.

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 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 score0.139
Threshold uncertainty score0.133

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.041
GPT teacher head0.228
Teacher spread0.186 · 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.

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

Citations37
Published2023
Admission routes1
Has abstractyes

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