Review of osmotic dehydration: Promising technologies for enhancing products’ attributes, opportunities, and challenges for the food industries
Bibliographic record
Abstract
Osmotic dehydration (OD) is an efficient preservation technology in that water is removed by immersing the food in a solution with a higher concentration of solutes. The application of OD in food processing offers more benefits than conventional drying technologies. Notably, OD can effectively remove a significant amount of water without a phase change, which reduces the energy demand associated with latent heat and high temperatures. A specific feature of OD is its ability to introduce solutes from the hypertonic solution into the food matrix, thereby influencing the attributes of the final product. This review comprehensively discusses the fundamental principles governing OD, emphasizing the role of chemical potential differences as the driving force behind the molecular diffusion occurring between the food and the osmotic solution. The kinetics of OD are described using mathematical models and the Biot number. The critical factors essential for optimizing OD efficiency are discussed, including product characteristics, osmotic solution properties, and process conditions. In addition, several promising technologies are introduced to enhance OD performance, such as coating, skin treatments, freeze-thawing, ultrasound, high hydrostatic pressure, centrifugation, and pulsed electric field. Reusing osmotic solutions to produce innovative products offers an opportunity to reduce food wastes. This review explores the prospects of valorizing food wastes from various food industries when formulating osmotic solutions for enhancing the quality and nutritional value of osmotically dehydrated foods while mitigating environmental impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".