Soybean peroxidase for industrial wastewater treatment: a mini review
Bibliographic record
Abstract
The potential of oxidoreductases, such as laccases and peroxidases, to remove organic pollutants from industrial wastewater and process water is addressed in this short review, with an emphasis on the peroxidase work completed or in progress in the authors’ laboratory. The major drawback to this treatment is the cost of the enzyme. However, with new sources and recent advances in the biotechnology industry, it is becoming a feasible alternative. A niche where enzymatic treatment may be first applied is not as a primary treatment but as a secondary treatment (pretreatment or polishing) coupled to existing physico-chemical or biological processes to increase their overall efficiency and economy. Soybean seed coat peroxidase is well suited because of its stability, ease of extraction, widespread availability and potential for adding to the soy value chain. Since crude enzyme often works better than purified enzyme, the only additional cost may be in concentrating the extract. This review briefly covers aspects of the enzymatic treatment such as cost, use of additives for increased enzyme economy, enzyme recycling and studies already completed on industrial wastewaters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".