Analysis of the composition of Brazilian black soybean seeds and mathematical modelling of intermittent drying and extraction of antioxidant compounds using fractional calculus
Bibliographic record
Abstract
Abstract The present study aims to analyze the composition of black soybean seeds, evaluate the intermittent drying of black soybean seeds by fractional calculus, optimize the extraction conditions of the antioxidant compounds by varying the solvent ratio and the extraction time, fit traditional models of extraction kinetics, and compare with the fractional order model. Regarding the oil content, it is analyzed that black soybeans have a high lipid content (18.86%), an oil source of interest for research and industrial applications. Regarding drying, it was found that the first order model cannot be used to describe the kinetics of intermittent drying for black soybean seeds and the best kinetic fits were obtained with the Page and fractional order models, which can be applied in simulations of drying and dryer designs. Regarding the extraction process, ethanol/water 45/55 (v/v) solvent proportion obtained the highest antioxidant compound content during 1 h of extraction. So and MacDonald's and hyperbolic kinetic models presented the best fitting to experimental data. About the fractional order model, it is found that for extraction conditions with more significant amounts of water in the ethanolic solution, the α value obtained is less than 1, resulting in the phenomenon of subdiffusion. For the extraction condition with a low amount of water in the ethanolic solution, the α value was greater than 1, depicting a superdiffusion process.
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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 | 0.000 |
| 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".