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Record W1544437856 · doi:10.18697/ajfand.55.11020

A comparison of the kinetics of mango drying in open-air, solar, and forced-air dryers

2012· article· en· W1544437856 on OpenAlexafffund
D.G. Mercer

Bibliographic record

VenueAfrican Journal of Food Agriculture Nutrition and Development · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsSolar dryerTrayForced-airOpen airEnvironmental scienceWater contentForced convectionMoistureAir temperatureEnvironmental engineeringPulp and paper industryMeteorologyGeographyEngineeringMechanical engineeringConvectionGeotechnical engineering

Abstract

fetched live from OpenAlex

Mangoes are under-utilized fruits that grow naturally in many sub-Saharan African countries. At the present time most mangoes are sold fresh in local markets. There is little done to preserve them for use during the off-season. Drying is one way in which the economic potential of mangoes could be exploited. This study was undertaken to investigate and compare the kinetics of mango drying using three basic drying methods: open-air drying on wire mesh racks; solar drying in a prototype dryer equipped with solar-powered exhaust fans; and forced-air drying in an Armfield Model UOP8 laboratory-scale tray dryer. Results could then be used to determine appropriate drying techniques for mango processing in sub-Saharan Africa on both local and commercial scales. Of these methods, forced air drying was found to provide the best overall results, based on water removal rates and general control over the drying process. Solar drying, while viewed as a promising technology for application in developing countries, was considerably slower than forced- air drying and is severely restricted by climatic conditions. A similar situation was observed for open-air drying, which was the slowest drying method of the three. Based upon mathematical models developed for each drying method, 11.6 hours was predicted as being required for mangoes in the forced-air dryer to a final moisture content of 10% (wet basis). Sixteen (16) hours and 24 hours of exposure to appropriate drying conditions were predicted as being required for solar drying and open-air drying, respectively. This could take three or four days to achieve under actual operating conditions. These times were supported by experimentally determined values. The impact of air temperature and linear air velocity on the drying kinetics of sliced mangoes were also investigated using the forced-air dryer. A linear velocity of 0.5 m/s was found to be sufficient for satisfactory drying of the mango slices when combined with an air temperature in the range of 50ΕC to 60ΕC. It is recommended that forced-air drying be utilized whenever possible for the drying of mango slices for both food safety and food quality reasons.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.141

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 head0.045
GPT teacher head0.260
Teacher spread0.214 · 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

Citations12
Published2012
Admission routes2
Has abstractyes

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