Modified Atmospheric Drying of Fruits and Vegetables: Equipment, Kinetics, and Feasibility
Bibliographic record
Abstract
ABSTRACT Fruits and vegetables, with high moisture levels of 85%–95% and 75%–96%, respectively, are susceptible to enzymatic activity and external factors, leading to rapid degradation through oxidative reactions, microbial proliferation, and respiration mechanisms such as ethylene emission. Drying, a critical preservation method, relies on heat and mass transfer driven by temperature and vapor pressure gradients. However, excessive thermal exposure and oxygen interaction often deteriorate bioactive compounds. Removing oxygen during drying offers a promising strategy to mitigate degradation and enhance product stability. Modified atmospheric drying (MAD) is an advanced technique that replaces atmospheric oxygen with alternative gases such as CO 2 , N 2 , or H 2 to improve drying efficiency and product quality. This review represents the first comprehensive effort to systematically consolidate recent developments in MAD, providing insights into operational mechanisms, equipment design, drying kinetics, quality preservation, and industrial feasibility, with emphasis on potential to reduce oxidation, retain nutrients, and preserve structural integrity. Compared to traditional drying, MAD achieves up to 18% improvement in effective moisture diffusivity, a 17%–29% reduction in drying time, and up to 6% increase in rehydration potential. It also enhances retention of nutritional and bioactive compounds, with total phenolic content maintained at 15%–25% higher levels, ascorbic acid degradation reduced by up to 15%, and improved color stability reflected in a decrease in total color difference (Δ E ) of up to 11%. CO 2 inhibits enzymes in aqueous and fatty matrices, whereas N 2 reduces oxidative and microbial deterioration. Overall, MAD improves product quality, shelf life, and energy efficiency, lowering production costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".