Kinetics of Drying and Physical-Chemical Quality of Peach cv. Hubimel
Bibliographic record
Abstract
The objective of this study was to perform the kinetics of peach drying and to adjust the experimental data obtained to empirical and diffusive mathematical models to evaluate the effect of temperature on the physical-chemical quality of the final product. The drying experiments were performed in an air circulation dryer with a velocity of 1.5 m s-1, the drying kinetics were performed at temperatures of 60, 70 and 80 °C. Among the mathematical models applied, the Page model was the one that best fit the experimental data, because it presents greater efficiency in the description of the drying process. The decrease in the drying rate from the initial time to the end of the process was observed, increasing the temperature of the air caused a reduction in the drying time. It was verified through the analytical solution of the diffusion equation with infinite wall geometry that the increase of the drying temperature caused the increase of the diffusivity and convective coefficient of heat transfer. Through the Biot number, it can be stated that the first-type boundary condition would also describe the process satisfactorily. The fresh peach slices present high water content and water activity and the drying effect caused significant differences in all physical-chemical parameters analyzed.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".