Effect of viscosity and rheological behavior on selective mass transfer during osmotic dehydration of mango slices in natural syrups
Bibliographic record
Abstract
Abstract Osmotic dehydration of mangoes was investigated for the reduction of solids gain (SG) and potential use of natural syrups as osmotic solutions. Different osmotic solutions at 60 °Brix were used (made with sucrose, glucose, fructose, corn syrup solids (CSS), and agave syrup (AS) with or without added xanthan gum [XG] or inulin) during osmotic dehydration at 40°C of mango slices (0.4 and 1.5 cm thickness). Rheological behavior and viscosity of the different osmotic solutions were determined at 22 and 40°C. According to the results, increasing the viscosity and the sample thickness helped to reduce the sugar gain while maintaining an adequate water loss. The highest sugar gain was found for sucrose, glucose, fructose, AS solutions, and the lowest, for CSS solutions and XG added to AS. The impact of increasing apparent viscosity on SG was more pronounced for thin samples, indicating the importance of the Biot number on selective mass transfer during osmotic dehydration. Practical Applications This research aims to obtain osmotically dehydrated mangoes with low sugar content by using a natural multicomponent solution such as an AS with added ingredients. In this study, the role of solution viscosity combined to sample thickness in lowering SG during osmotic dehydration was elucidated. As well, AS used in this research as a model, is particularly interesting due to its rich content in vitamins and prebiotic (inulin) which levels up the mango nutritious values. This study would help industrials to offer healthier snacks, in particular for consumers who wish to reduce their sugar intake.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".