Exploring conventional and emerging dehydration technologies for slurry/liquid food matrices and their impact on porosity of powders: A comprehensive review
Bibliographic record
Abstract
The contribution of dehydration to the growing market of food powders from slurry/liquid matrices is inevitable. To overcome the challenges posed by conventional drying technologies, several innovative approaches have emerged. However, industrial implementation is limited due to insufficient information on the best-suited drying technologies for targeted products. Therefore, this review aimed to compare various conventional and emerging dehydration technologies (such as active freeze, supercritical, agitated thin-film, and vortex chamber drying) based on their fundamental principles, potential applications, and limitations. Additionally, this article reviewed the effects of drying technologies on porosity, which greatly influence the solubility, rehydration, and stability of powder. The comparison between different drying technologies enables informed decision-making in selecting the appropriate one. It was found that active freeze drying is effective in producing free-flowing powders, unlike conventional freeze drying. Vortex chamber drying could be considered a viable alternative to spray drying, requiring a compact chamber than the large tower needed for spray drying. Freeze-dried, spray freeze-dried, and foam mat-dried powders exhibit higher porosity than spray-dried ones, whereas supercritical drying produces nano-porous interconnected powders. Notably, several factors like glass transition temperature, drying technologies, particle aggregation, agglomeration, and sintering impact powder porosity. However, some binders, such as maltodextrin, sucrose, and lactose, could be applied in controlled agglomeration to enhance powder porosity. Further investigation on the effect of emerging technologies on powder properties and their commercial feasibility is required to discover their potential in liquid drying. Moreover, utilizing clean-label drying ingredients like dietary fibers, derived from agricultural waste, presents promising opportunities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".