Sequential optimization of drying and extraction processes for enhanced antioxidant recovery from <i>Parquetina nigrescens</i> leaves: Assessing drying parameters and extraction model reliability
Bibliographic record
Abstract
Abstract The study explored enhanced extraction of phenolic‐rich bioactive compounds from Parquetina nigrescens leaves. Two crucial industrial processes, drying and extraction, pivotal for processing P. nigrescens leaves for bioactive extract recovery, were extensively examined. Convective drying characteristics of P. nigrescens leaves were examined using a laboratory oven, varying air temperatures (35.0, 45.0, 55.0, and 65.0°C) at a constant air velocity of 1.50 m/s. Quality assessment of dried leaves determined optimal drying conditions for bioactive compound preservation. Sequential optimization of bioactive extract recovery was performed using the Box–Behnken design, a component of response surface methodology (BBD‐RSM), to optimize heat‐assisted extraction (HAE) process variables: operating temperature (OT), solid–liquid ratio (S/L), and extraction time (ET). Models were developed to predict total phenolic content (TPC), extract yield (EY), and antioxidant activity (AA). Monte Carlo simulation assessed the robustness of developed models. Drying time and temperature significantly influenced the process, with effective moisture diffusivity ranging from 8.20 E‐11 to 3.55 E‐10 m 2 /s. Leaves dried at 45.0°C for 640 min exhibited optimal quality. Extraction process variables (OT, S/L, ET) also showed significant effects. Multi‐objective optimization led to the attainment of 8.97 mg GAE/g for TPC, 25.0% for EY, and 6.31 μM AAE/g for AA. These achievements were realized with an OT of 55.0°C, ET of 151 min, and S/L of 1:40.8 g/mL. The developed BBD‐RSM models exhibited high reliability (TPC = 98.1%; EY = 99.9%; AA = 91.4%) aiding informed decision‐making in research and industrial applications. This study provides data for process design, equipment selection, and sustainable, value‐added product development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".