Role of pH on antioxidants production by Spirulina ( Arthrospira ) platensis
Bibliographic record
Abstract
Algae can tolerate a broad range of growing conditions but extreme conditions may lead to the generation of highly dangerous reactive oxygen species (ROS), which may cause the deterioration of cell metabolism and damage cellular components. The antioxidants produced by algae alleviate the harmful effects of ROS. While the enhancement of antioxidant production in blue green algae under stress has been reported, the antioxidant response to changes in pH levels requires further investigation. This study presents the effect of pH changes on the antioxidant activity and productivity of the blue green alga Spirulina (Arthrospira) platensis. The algal dry weight (DW) was greatly enhanced at pH 9.0. The highest content of chlorophyll a and carotenoids (10.6 and 2.4mg/g DW, respectively) was recorded at pH 8.5. The highest phenolic content (12.1mg gallic acid equivalent (GAE)/g DW) was recorded at pH 9.5. The maximum production of total phycobiliprotein (159mg/g DW) was obtained at pH 9.0. The antioxidant activities of radical scavenging activity, reducing power and chelating activity were highest at pH 9.0 with an increase of 567, 250 and 206% compared to the positive control, respectively. Variation in the activity of the antioxidant enzymes superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) was also reported. While the high alkaline pH may favor the overproduction of antioxidants, normal cell metabolism and membrane function is unaffected, as shown by growth and chlorophyll content, which suggests that these conditions are suitable for further studies on the harvest of antioxidants from S. platensis.
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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) | 0.000 | 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 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".