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Record W2878915880 · doi:10.5539/jas.v10n8p438

Postharvest Life Extension of Fresh-Cut Mango (Mangifera indica cv. Fa-Lun) Using Chitosan and Carboxymethyl Chitosan Coating

2018· article· en· W2878915880 on OpenAlexvenueno aff
Duangjai Noiwan, Kiattisak Sutenan, Chatchai Yodweingchai, Pornchai Rachtanapun

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsnot available
FundersFaculty of Science, Chiang Mai UniversityCommission on Higher EducationMaterials Science Research Center, Faculty of Science, Chiang Mai UniversityOffice of the Higher Education CommissionChiang Mai University
KeywordsPostharvestChitosanMangiferaShelf lifeBrowningChemistryHorticultureFood scienceBiology

Abstract

fetched live from OpenAlex

Postharvest life extension of fresh-cut mango (Mangifera indica cv. Fa-Lun) using chitosan and carboxymethyl chitosan (CMCH) coating was studied. Fresh-cut mango was treated with chitosan and carboxymethyl chitosan solution of 0.5-1.5% w/v, after that fresh-cut mango was placed on foam tray, over-wrapped with PVC film and then stored at 6 °C. Weight loss, texture analysis, soluble solid content, color and sensory quality were evaluated. The shelf life of non-coated fresh-cut mango was only 2 days while that of fresh-cut mango coated with chitosan and carboxymethyl chitosan was 4 and 6 days, respectively. Effect of chitosan concentration on quality of fresh-cut mango was significantly different but of carboxmethyl chitosan concentration was not. In this study, Coating with carboxymethyl chitosan could extend shelf life of fresh-cut mango by delayed flesh browning which correlated to the sensory score.

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)0.001
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 score0.291
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.015
GPT teacher head0.259
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

Citations3
Published2018
Admission routes1
Has abstractyes

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