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Record W2612772526 · doi:10.5539/jas.v9n6p199

Kinetics of Mango Fruits (Mangifera indica cv. ‘Nam Dok Mai Si Thong’) Quality Changes during Storage at Various Temperatures

2017· article· en· W2612772526 on OpenAlexvenueno aff
Duangjai Noiwan, Panuwat Suppakul, A. Joomwong, Jamnong Uthaibutra, Pornchai Rachtanapun

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsnot available
Fundersnot available
KeywordsMangiferaRipenessRipeningTitratable acidPostharvestHorticultureChemistryRespiration rateFleshRespirationArrhenius equationAnacardiaceaeFood scienceBotanyBiologyActivation energy

Abstract

fetched live from OpenAlex

Effect of various storage temperatures (13, 20, 27 and 34 °C) on biochemical, physical and physiological changes of mango fruits (Mangifera indica cv. ‘Nam Dok Mai Si Thong’) was investigated. Mangoes stored at low temperature revealed a decrease in their respiration rates. The lower respiration rates delayed ripening, ethylene production, weight loss, peel and flesh color changes, firmness, and total soluble solid content (SSC) as well as titratable acidity (TA). A second-order kinetic model, a Gaussian model, and a first-order kinetic model fitted well with response quality parameters on firmness, SSC and TA. The Arrhenius function was used to calculate the activation energies (Ea) of mango qualities including firmness, SSC and TA, which were 46.45, 43.05 and 54.22 kJ mol-1, respectively. These activation energies indicate a moderate temperature sensitivity of ripeness response and represent a good predictive tool for mango quality estimation along the food supply chain. The data reveal that stored mango fruit at 13 °C effectively prolongs the quality attributes and extends the shelf life of mango fruit.

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 score0.906
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.033
GPT teacher head0.275
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

Citations14
Published2017
Admission routes1
Has abstractyes

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