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Record W7011620791

Microwave osmotic dehydration of apples («Red Gala») under continuous flow medium spray conditions (MWODS) for improving moisture transport rate and product quality

2011· other· en· W7011620791 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2011
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsOsmotic dehydrationMass transferMoistureDehydrationMicrowaveKineticsResponse surface methodologyVolumetric flow rate
DOInot available

Abstract

fetched live from OpenAlex

Microwave osmotic dehydration (MWOD) is a novel technique with a good potential for more efficient osmotic drying of fruits and vegetables. It combines the microwave heating with osmotic dehydration for enhancing moisture transfer rate in the osmotic dehydration process and product quality. Preliminary studies were carried out to compare the osmotic dehydration kinetics of apple (Red Gala) cylinders in microwave osmotic dehydration under continuous flow medium spray conditions (MWODS) with microwave osmotic dehydration under continuous flow medium immersion conditions (MWODI), as well as conventional osmotic drying (COD) in immersion (CODI) and spray (CODS) modes. The results showed that the MWODS process considerably enhanced the moisture transfer rate from the fruit, and limited the solids gain at the same time. In the second part, the two-parameter Azuara model and the conventional diffusion model were evaluated for describing the mass transfer kinetics of apple (Red Gala) cylinders during MWODS, MWODI, CODS and CODI. The results showed that both models adequately described the transient mass transfer kinetics during the OD process; however the Azuara model was superior. MWODS was further studied to evaluate the effect of various process variables (sucrose concentration, medium temperature, flow rate and contact time) by using response surface methodology and a central composite rotatable design. Predictive models were developed relate the response variables to process parameters. Finally optimization studies were carried out to elucidate optimal processing conditions under MWODS. The study demonstrated that moisture loss (ML), solids gain (SG) and weight reduction (WR) were predictably higher at higher sucrose concentrations, higher medium temperatures, longer contact times and higher flow rates. Since OD only results in partial dehydration, a second stage drying was evaluated employing conventional air drying and compared with freeze drying to identify cost effective systems for preserving the quality of the osmotically dehydrated shelf-stable fruits. The effect of MWODS pretreatment on air-drying kinetics and quality parameters (color, texture, and rehydration characteristics) of apple (Red Gala) cylinders was evaluated. The results revealed that drying time decreased with increasing concentrations and medium temperature of the MWODS treatment. Compared with untreated control samples, MWODS air-dried samples had higher coefficient of moisture diffusivity (Dm). In terms of quality parameters, the MWODS air-drying combination process resulted in a product with lower color change and a more chewy structure. The air dried product without MWODS had the least desirable quality characteristics. While the color was better preserved in the freeze dried product, it was much more brittle than MWODS – air-dried product. The rehydration capacity of MWODS air-dried products was lower than freeze-dried products and higher than air-dried. Overall, the thesis research contributes to a better understanding of the moisture transfer behavior during microwave osmotic dehydration under continuous flow medium spray processing conditions. Together with a simple second stage air-drying it can produce high quality dehydrated apple products.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.162
Teacher spread0.153 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2011
Admission routes1
Has abstractyes

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