Optimized extraction of total proteic mass from water hyacinth dry leaves
Bibliographic record
Abstract
Optimization and effect simulation of the one-step solid:liquid extraction of the proteic mass from water hyacinth were achieved for glutathione recovery, through a factorial 33 experiment design. The experiment was carried out using dry water hyacinth leaves, previously crushed into a fine powder with particle size not exceeding 0.1 mm, and a sodium phosphate buffer as the extracting phase. For this purpose, 27 attempts were made by varying the dry leaves to buffer w/v ratio (solid:liquid (S/L)), the buffer pH, and extraction time, which are regarded as being key parameters. The total protein mass extracted attained values of 75–76 wt.% of the initial mass of dry hyacinth leaves, at 30 °C, S/L = 1:7.5–1:8.0 and pH 8.2. Time has no influence on the amount of extracted proteins, but a minimum time of 20 min is recommended. The effects of acidity and ionic strength upon protein fractionation were also investigated via potentiometric titrations of the collected extract with citric acid and ammonium sulphate, respectively, in a temperature range (20–60 °C). Most of proteins precipitate at pH 4.5–6.0 in the presence of citric acid and at pH 6.5–5.85 in the presence of ammonium sulphate. Glutathione was detected using high performance liquid chromatography (HPLC) in a liquid fraction after protein precipitation at pH = 5.65 in the presence of ammonium sulphate. Key words: Factorial 33 Design, solid:liquid extraction, water hyacinth, protein separation, optimization, glutathione.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".