Review of recent applications and research progress in hybrid and combined microwave-assisted drying of food products: Quality properties
Bibliographic record
Abstract
The growing concerns over product quality have increased demand for high quality dried food products and encouraged researchers to explore and producers of such products to implement novel microwave (MW)-assisted drying methods. This paper presents a critical review of the key principles and drawbacks of MW-assisted drying as well as needs for future research. In this article, recent research into application of microwaves as an alternative heat source, applications and progress in hybrid MW-assisted drying that rely on various drying media and combined two or three stages of MW-assisted drying for the preservation of food products is reviewed critically. The effect of different MW-assisted drying methods, conditions and initial pretreatments on the thermophysical properties, color, nutritional value and rehydration potential of dried food products is discussed in detail along with the discussion on how the material properties evolve and change in structure, color, and composition during MW-assisted drying and recent attempts at mathematical modeling of these changes made for different fruits and vegetables. It should be noted that most of the published results were obtained in laboratory-scale dryers. Pilot-scale testing is needed to bridge the gap between laboratory research and industrial applications to fulfill the potential for novel hybrid and combined MW-assisted drying methods and to expand their role in food processing.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".