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Record W2152449494 · doi:10.5539/jas.v4n10p126

Reduction of Chilling Injury of ‘Nam Dok Mai No. 4’ Mango Fruit by Treatments with Salicylic Acid and Methyl Jasmonate

2012· article· en· W2152449494 on OpenAlexvenueno aff
Chanikan Junmatong, Jamnong Uthaibutra, Danai Boonyakiat, Bualuang Faiyue, Kobkiat Saengnil

Bibliographic record

VenueJournal of Agricultural Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsnot available
FundersGraduate School, Chiang Mai UniversityFaculty of Science, Chiang Mai UniversityCommission on Higher EducationChiang Mai University
KeywordsSalicylic acidChemistryRipeningHorticultureMethyl jasmonateMalondialdehydeCold storagePulp (tooth)PostharvestBotanyFood scienceMedicineBiologyAntioxidantBiochemistryDentistry

Abstract

fetched live from OpenAlex

This study determined the effects of salicylic acid (SA) and methyl jasmonate (MJ) on chilling injury (CI) of stored mangoes at low temperature. Mango fruits cv. Nam Dok Mai No.4 were dipped in SA and MJ at concentrations of 0.1 and 1 mM for 10 minutes and stored at 5±1 ºC with 90±2% RH for 42 days. Fruits were sampled every 7 days to determine the CI index, malondialdehyde (MDA) content, electrolyte leakage (EL), then transferred to room temperature (25±2 ºC, 70±2% RH) for ripening and analyzed for CI index, MDA content, EL, and ripening quality. The results show that mango fruits showed CI symptoms after 21 days of storage at 5 ºC and were unacceptable for consumption after 28 days of cold storage and after transfer to room temperature. SA and MJ treatments significantly reduced CI, MDA content, and EL in the mango skin and pulp during cold storage and after transfer to room temperature. Dipping fruits in 0.1 mM MJ, 1 mM MJ, and 0.1 mM SA reduced CI of cold storage and after transfer to room temperature up to 35 days, whereas dipping in 1 mM SA prolonged this up to 42 days without affecting fruit firmness, percentages of TA, and skin color (°Hue values) of the ripe fruits.

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 score0.783
Threshold uncertainty score0.170

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 communication0.0000.001
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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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