Optimisation of Organic Solvent Mediated Solubilisation of Apple Pomace Polyphenolic Compounds Using Response Surface Methodologies
Bibliographic record
Abstract
Polyphenolic compounds extraction from industrial apple pomace was optimised by applying design of experiments (DoE) and surface response methodology using the Central Composite Rotatable Design (CCRD). The degree solubilisation and the yield of total phenolic content from the apple pomace using organic solvents was shown to be influenced by process parameters including solvent type, solvent concentration, temperature, apple pomace to solvent ratio, and extraction time (residency time). Optimal conditions of extracting phenolic compounds were as follows: acetone concentration, 65 % (v/v); solid to solvent ratio 1 %; extraction time 30 minutes and temperature 60oC. Optimum condition for solubilisation was as follows: acetone concentration 78 % (v/v); solid to solvent ratio 4.7 %; extraction time 54 minutes and temperature 21oC. Under these conditions, the total phenolic content and solubilisation were 21.70 ± 0.2 mg GAE/g dw and 19.20 ± 0.1g/100g of the dried apple pomace respectively and largely agreed with those predicted by the Stat-Ease software. Independent variables for optimisation of total phenolic content and solubilisation were completely different. The reverse phase HPLC analysis of the extract revealed the major polyphenolic compounds were chlorogenic acid, procyanidin B2, caffeic acid, epicatechin, ferulic acid, quercetin-3-galactoside, quercetin-3-glucoside and phloridzin.
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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 | 0.001 |
| 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 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".