Synergy of Novel Technologies in Food Drying and its Applications
Bibliographic record
Abstract
Food drying often has some issues which may conflict with requirements of efficiency,safety, quality,and energy consumption.High efficiency can lead to low energy consumption and saving drying costs but may cause reduced quality and even problems with safety of the dried foods which may reduce the value of the dried products.So it is very important for the researchers and manufacturers to balance the needs among safe shelf life along with high quality, good efficiency, and lower energy consumption.A comprehensive review of recent developments in synergy of novel technologies in food drying can provide the new trend in food drying R & D.Synergy of several novel technologies, such as Ultrasonic technology, nanotechnology, intelligent and/or computer simulation technology, hyperspectral imaging technology, NMR, have been synergied to meet these special requirements of efficiency, energy consumption, safety and quality.These highly efficient synergied methods can protect diverse quality parameters of fresh foods (such as color, flavor, nutrients, rehydration, appearance, uniformity, etc) and safety during an energy-saving drying process.Potential for future applications and research opportunities will be identified for both academia and industry.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".