Food Drying for Low‐Carbon and Future Food: A Review on the Sustainability of Drying Technology
Bibliographic record
Abstract
Food drying, as a food processing industry with a high carbon footprint, needs to adapt in the face of carbon reduction and future food trends. Through the application of classical theories on food moisture migration and drying kinetics, coupled with life cycle analysis of food drying processes, the low-carbon feasibility of drying engineering can be demonstrated; a survey of drying technologies over the last 20 years has shown a new wave of advances in food drying equipment for clean energy sources such as solar, geothermal, and biomass, with tools such as energy efficiency and monitoring enriching the drying process. The food of the future is a development of traditional and modern food products, for which new R&D trends, resource utilization, environmental load reduction, and customized production of drying technologies can provide insights. In addition, around the issue of economic efficiency of food processing, relevant recommendations for integrating cost reduction algorithms for drying processing and equipment construction are presented. This review provides useful information on carbon reduction in drying as well as trends in drying and assists in the selection of drying equipment.
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 | 0.001 |
| 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) | 0.004 | 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".