MODELING SOLID–LIQUID EXTRACTION KINETICS OF <i>TRANS</i>‐RESVERATROL AND <i>TRANS</i>‐ε‐VINIFERIN FROM GRAPE CANE
Bibliographic record
Abstract
ABSTRACT Solid–liquid extraction of resveratrol and viniferin from grape cane samples was described by using first‐order kinetic model, Peleg's model, two‐site kinetic model and modified Gompertz equation. Goodness of fits of the models were evaluated by comparing the adjusted determination coefficient and root mean square error and mean percentage error values. Although the two‐site kinetic model with four parameters described the data better, Peleg's model, with only two parameters, could explain the data with a slight loss of goodness of fit. The modified Gompertz equation showed the worst performance for describing the solid–liquid extraction of stilbenes. PRACTICAL APPLICATIONS The present study introduces the comparison of well‐known models applied to explain extraction kinetic of stilbene compounds of grape cane and to determine the best model with its justifications. Mathematical models provide information about the system and/or process to which they are applied. In design and/or process application stages, any information about that process and/or system has crucial importance because in the decision stages, this know‐how helps the designers and researchers find the best design parameters and the most effective process conditions to optimize purposes. Mathematical models are accepted as the most economical ways for these purposes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".