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Record W3036926152 · doi:10.21608/mjae.2018.96052

OPTIMIZATION OF STRAWBERRY DRYING PROCESS UNDER DIFFERENT PRETREATMENTS AND GEOMETRIES AT LOW TEMPERATURES

2018· article· en· W3036926152 on OpenAlexaff
A. Ghaly, Abd El-Kader El-Nakib, H. AbdelMwla, Khaled Nagy, Ahmed I. Hassan

Bibliographic record

VenueMisr journal of agricultural engineering · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsDalhousie University
Fundersnot available
KeywordsProcess (computing)Materials sciencePulp and paper industryProcess engineeringChemical engineeringComposite materialChemistryComputer scienceEngineeringProgramming language

Abstract

fetched live from OpenAlex

The drying kinetics of strawberry were investigated and optimized. They affected by the strawberry geometries and the drying temperature in addition to the pretreatments including the thermal treatments and osmotic dehydration. Three geometries (whole, halve and quarter), two thermal pretreatments (hot water and microwave), osmotic dehydration (sucrose + calcium chloride and glucose + calcium chloride) and three temperatures (40, 50 and 60°C) were evaluated. The initial moisture content of the fresh strawberry samples was varied between 93.4 and 77 % (w.b). The results indicated that thehalf that treated by sucrose, Whole that treated by sucrose and hot water (80 °C) for 10 sec and half that treated by glucose, microwave (1100 W) for 10 sec at 40ºC. Also, half that treated by glucose, whole that treated by sucrose and hot water (80 °C) for 10 sec and whole that treated by sucrose, microwave (1100 W) for 10 sec at 50ºC. The optimum conditions at the highest temperature 60 ºC were half that treated by sucrose, whole that treated by sucrose and hot water (80 °C) for 10 sec and whole that treated by sucrose, microwave (1100 W) for 10 sec.

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 score0.503
Threshold uncertainty score0.185

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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