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Record W1991862484 · doi:10.1002/jsfa.1303

Postharvest storage of Giant Cavendish bananas using ethylene oxide and sulphur dioxide

2003· article· en· W1991862484 on OpenAlexafffund
Opal J. Williams, Vijaya Raghavan, Kerith D. Golden, Yvan Gariépy

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPostharvestShelf lifeRipeningHorticultureEthylene oxideEthyleneModified atmosphereControlled atmosphereChemistryToxicologySulfur dioxideFood scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The potential of ethylene oxide (EO) and sulphur dioxide (SO 2 ) to prevent ethylene‐stimulated fruit ripening was assessed using Giant Cavendish bananas. Studies were carried out in a cold room at 15 °C and terminated after 6 weeks. Product quality was assessed via visual observations and physiological assessments. Fruits were exposed to EO concentrations in the range 0–400 ppm for 12 h followed by storage in regular atmosphere (RA). Results showed that EO delayed ripening at 50 and 100 ppm for single exposure and at 50 and 200 ppm for repeated exposure. Treatment with 2 and 8 µg kg −1 SO 2 was efficient in extending the shelf life of bananas for 4 weeks in RA and for 6 weeks under controlled atmosphere (CA). SO 2 preserved the quality of bananas and reduced the incidence of fungal infections during storage; however, a concentration of 15 µg kg −1 shortened the shelf life. Fruits treated with low concentrations of EO and SO 2 had harvest‐fresh appearance, good colour, minimum mould and excellent marketability compared with controls and store‐bought references. EO and SO 2 are not approved for use on fresh fruits, with the exception of the use of SO 2 on grapes and citrus fruits. © 2003 Society of Chemical Industry

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.192

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 studies0.0000.000
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 head0.022
GPT teacher head0.221
Teacher spread0.199 · 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

Citations17
Published2003
Admission routes2
Has abstractyes

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