Advances in Freeze Drying to Improve Efficiency and Maintain Quality of Dehydrated Fruit and Vegetable Products
Bibliographic record
Abstract
High moisture content and soft texture are the major factors that result in higher losses to the tune of even 25–40% in fresh fruit and vegetables, in turn causing colossal waste of food, time, efforts, input costs, and pre-harvest resources. Drying technology has been used as a valuable technology to reduce volume, preserve quality, and enhance storability far beyond what is possible for fresh horticultural commodities since ages. With advancement in time, different drying technologies have come up. Notable among them are freeze drying, which is known to produce the best-quality dried produce, and air drying, which is used most commonly due to ease of handling and cost efficacy. However, the major drawbacks of freeze drying are the long drying time and the higher energy consumption, which lead to a higher per-unit cost of the dried product. The current times demand an intelligent, cost-effective drying system with a quality of dried produce comparable to its fresh counterpart. This brings us to the need for advancement in freeze-drying systems to lower energy consumption and improve efficiency.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".