Prediction of Mathematical Models of the Drying Kinetics and Physicochemical Quality of the Chili Pepper
Bibliographic record
Abstract
The present work had as objective to determine the kinetics of drying of the chili pepper, to adjust different mathematical models to the experimental values as a function of the water content and to characterize the same in it’s in natura form and after the drying in the temperatures of 60, 70 and 80 °C. The samples were weighted periodically until reaching the equilibrium. The mathematical models of Wang and Singh, Henderson and Pabis, Newton, Page and Thompson were adjusted to the experimental data. The best adjustment was determined in relation to the highest values of the coefficient of determination (R2) and Mean Square Deviation (MSD). The obtained results showed that the drying of the pepper is influenced by the temperature of the drying air. It is concluded that the model of Henderson and Pabis was the one that best fit to the experimental data. The increase of the drying temperature promoted a reduction in the time required for the peppers to reach the moisture of the hygroscopic balance. The parameters of acidity, lipids and proteins remained close at the different drying temperatures used, however higher when compared to the samples in natura. It can be noticed that ashes and vitamin C have suffered considerable decrease as the temperature increased, as a result of the chemical transformations that occurred in the peppers due to heat exposure and loss of moisture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".