Effect of Osmotic Pre-Treatment on the Air-Drying Behavior and Quality of Plum Tomato Pieces
Bibliographic record
Abstract
The air-drying behavior and quality of plum tomato pieces after pre-treatment with different osmotic solutions was investigated. Four pre-treatment solutions (comprised of salt, sugar and/or calcium lactate), three sample geometries (halves, quarters and eighths) and two air-drying temperatures (55 and 65°C) were studied. During osmotic pre-treatment, the moisture loss of the tomato pieces decreased with osmotic pressure. The proportion of skin to cut surface area was found to be important for osmotic moisture loss. As the percentage of cut surface area decreased (59.6%, 47.6% and 25.3% for the eighths, quarters and halves, respectively) and the percentage of skin on the sample increased, the percentage osmotic moisture loss also decreased. At an air-drying temperature of 55°C, the critical moisture content for storage (15%, wet basis) for the pre-treated halves, quarters and eighths was reached after 25-27, 15-18 and 9-12 hours, respectively. At 65°C, the critical moisture content was reached after 16-19, 9-13 and 6-8 hours, respectively. In both cases, the osmotic pre-treatment reduced the critical drying time. The reduction in moisture ratio over time was described by an exponential model (R2 values ? 0.92). The specific drying rate increased with osmotic pre-treatment and was more affected by air-drying temperature than the type of osmotic solution, while the geometry of the samples had no significant effect. Air-dried samples with osmotic pre-treatment were closer to the color of fresh tomato than samples without pre-treatment.
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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 | 0.001 |
| 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".