Humidity Control and Its Implications in the Storage of Apples and Pears: A Review
Bibliographic record
Abstract
Abstract At harvest, the water content of apples and pears is at its maximum, about 83–85% by mass. The rate of fruit water loss after harvest is a function of three factors: the fruit surface area, the epidermis water vapour permeance, and the difference in internal and external fruit water vapour pressure. However, the water vapour pressure difference is the only factor that can be controlled during storage, i.e. by altering the storage humidity. Curiously, most publications on apple and pear storage do not discuss storage humidity even though the postharvest quality of both depends largely upon it. Apples and pears can experience up to ~ 2% mass loss without loss of commercial value, but further mass loss creates visible shrivelling, and may also increase bitter pit, depending on the cultivar. The prevailing commercial advice has been to minimise mass loss by maximising the relative humidity at the chosen storage temperature. However, this advice should be re-evaluated, and humidity management protocols revised, from minimising mass loss to optimising mass loss. Growing evidence indicates that storage humidity affects the postharvest performance of apples and pears. A review of the literature over the last 100 years shows that some mass loss, achieved by lowering storage atmosphere humidity, can improve apple and pear quality by reducing non-bitter pit disorders, bruising, respiration, ethylene production and decay, especially if the mass loss occurs at the beginning of storage. The effects of mass loss on calcium-related disorders suggest that mass loss may affect postharvest calcium redistribution in the fruit. The fruit mass loss for refrigerated air and controlled atmosphere rooms can be estimated by collecting the defrost water from the refrigeration cooling coils and expressing this as a percentage of the initial total mass of fruit in the room. A ‘2-in-2’ guideline (up to 2% mass loss in the first 2 months) is proposed, but further research is needed to refine this guidance, especially for cultivars that may benefit from greater mass loss without increasing shrivelling or bitter pit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".