Apple Cultivar and Temperature at Cutting Affect Quality of Fresh Slices
Bibliographic record
Abstract
The response of four apple (Malus ×domestica) cultivars (Gala, Granny Smith, Ambrosia, and Aurora Golden Gala™) to fresh-cut processing at core temperatures of 1, 5, 13, and 20 °C was investigated. Fruit were cut after a 24-h preconditioning at one of the four temperatures and a commercial antibrowning formulation was applied as a 7% (w/v) dip before packaging the slices and storing them for 3 weeks at 5 °C. Fruit firmness generally decreased with increasing core temperature, except for Aurora Golden Gala™, which maintained similar firmness at all temperatures. Firmness varied among cultivars, but all except Granny Smith apples held at 13 and 20 °C, were at or above a minimum processing firmness standard of 14 lbf. Cut-edge browning of slices, in response to processing temperature, varied among the cultivars. In the extreme, ‘Granny Smith’ was the most responsive, showing the largest variance in surface lightness across the temperature range. ‘Ambrosia’ was the least responsive to temperature, showing no significant difference in L-value despite the temperature at which it was processed. ‘Gala’ and Aurora Golden Gala™ were intermediate in response. The visual quality rating for ‘Granny Smith’ at 3 weeks was poor for slices from all processing temperatures. ‘Ambrosia’ slices maintained acceptable quality ratings over the full test temperature range. ‘Gala’ slices had lower quality ratings when processed at warmer temperatures, whereas Aurora Golden Gala™ showed increased quality ratings with warmer processing temperatures. It was concluded that ‘Gala’ were best processed at low core temperatures, ‘Ambrosia’ could be processed at all tested temperatures, and Aurora Golden Gala™ produced better quality slices when fruit were are room temperature (20 °C) before slicing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".