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Record W2571111639 · doi:10.3390/molecules22010100

Antibacterial Activity of Neat Chitosan Powder and Flakes

2017· article· en· W2571111639 on OpenAlexafffund
Nury Ardila, France Daigle, Marie‐Claude Heuzey, Abdellah Ajji

Bibliographic record

VenueMolecules · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesUniversité de MontréalPolytechnique Montréal
KeywordsChitosanAntibacterial activityChemistryAntimicrobialIonic strengthFood scienceBacteriaListeriaStaphylococcus aureusFood spoilageEscherichia coliListeria monocytogenesAqueous solutionBiochemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

This study investigates the antibacterial activity of neat chitosan powder and flakes against three different bacterial species, Escherichia coli, Listeria innocua and Staphylococcus aureus, which are frequent causes of food spoilage. The effect of chitosan concentration and purity, as well as the influence of temperature, ionic strength (salt) and impact of a solid physical support in the medium are examined. Results show that the antibacterial activity of neat chitosan: (i) requires partial solubilisation; (ii) can be promoted by environmental factors such as adequate temperature range, ionic strength and the presence of a solid physical support that may facilitate the attachment of bacteria; (iii) depends on bacterial species, with a sensitivity order of E. coli > L. innocua > S. aureus; and (iv) increases with chitosan concentration, up to a critical point above which this effect decreases. The latter may be due to remaining proteins in chitosan acting as nutrients for bacteria therefore limiting its antibacterial activity. These results on the direct use of chitosan powder and flakes as potential antimicrobial agents for food protection at pH values lower than the chitosan pKa (6.2–6.7) are promising.

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 score0.090
Threshold uncertainty score0.397

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 science0.0000.000
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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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

Citations65
Published2017
Admission routes2
Has abstractyes

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