Decolorizing of seaweed extract by electrocoagulation
Bibliographic record
Abstract
Abstract Electrocoagulation (EC) is a technique commonly used in wastewater treatment to remove biological and chemical contaminants, but the process has the potential to be used in clarifying plant extracts for the isolation and identification of secondary metabolites. Seaweed extracts contain copious amounts of chlorophyll and other pigments that obscure the characterization of secondary metabolites such as phenolic acids and flavonoids. In place of conventional methods that utilize solvents, EC can potentially be applied to clarify and fractionate extracts. In this research, an EC duration of 30 min (22 V, 0.3–0.5A) with aluminum electrodes resulted in a significant decrease, about 76%, of chlorophyll and 70% of carotenoids from seaweed extract measured at 666 nm and 410 nm. The decrease in extract green and yellow color intensity also mirrored a decrease in total phenolic content (TPC) of the extract from 54 ± 1.55 mg GAE/g DW to 3.2 ± 0.01 mg GAE/g DW after 30 min of EC. However, the phenolic acid profile of the extract after electrocoagulation via HPLC-RP indicated the removal of an interference probably caused by polymeric compounds from the extract, thus leaving the simple phenolic acids in solution for detection. The major phenolic acids detected in seaweed crude extract were p-coumaric, o-coumaric, ferulic and syringic acid. Flavonoids detected included catechin, epicatechin, quercetin-3-glucoside and rutin. The results of this study show the potential of replacing conventional plant extract purification methods with a green method that requires no additional solvent. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".