Multi‐objective optimization of drying conditions for the <i>Olea</i> <i>europaea</i> L. leaves with NSGA‐II
Bibliographic record
Abstract
Phenolic compounds from olive leaves, which attract great attention with their high phenolic content and high antioxidant activity, need to be dried to increase the extraction efficiency before extraction. For this purpose, it is aimed to obtain optimal drying conditions of hot air inlet temperature (Tg), orifice inlet air flow rate (ν), drying time (t), and rehydration ratio (RR) for moisture removal of olive leaves. For this purpose, final moisture content (MC) and moisture loss (ML) were modeled and optimized by using soft computing methods. Pareto sets were obtained from predicted quadratic linear models using the least-squares (LS) approach. The nondominated sorting genetic algorithm-II (NSGA-II) was used for optimization. The compromise solution was obtained using the fuzzy c-means clustering algorithm (FCM) algorithm, one of the multicriteria decision-making methods. When the olive leaf is dried under optimum conditions determined by FCM (Tg = 63.84°C, v = 1.700 m/s, t = 997 s, RR = 2.21%), the experimental values for total phenolic content (TPC) and oleuropein amount were (20.07 ± 1.01) mg GAE/g dw and (7.30 ± 0.71) mg/g dw, respectively. It was found that drying olive leaves under low airflow rate and low hot air input temperature resulted in less alteration of TPC and oleuropein amount. Practical applications After drying olive leaves under optimum conditions, high value-added compounds and phytochemicals can be obtained. A chemometric study based on soft computing approaches can be used for a drying process. Optimization of predicted fuzzy responses can be achieved in a multi-objective perspective through the nondominated sorting genetic algorithm-II (NSGA-II). Soft computing-based modeling and optimization tools can be applied to determine the optimum conditions of chemical, food, and pharmaceutical industry processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".