Influence of Sulphate Nutrition on Growth Performance and Antioxidant Enzymes Activities of Spirulina platensis
Bibliographic record
Abstract
The growth of Spirulina platensis is dependent on culture conditions. This study has established adequate conditions for the quality and quantity production of S. platensis. The effect of sulphate salts nutrition on growth performance and biochemical status of S. platensis was assessed in vitro. Prior to culture, the Paracas strain of S. platensis from SAGRIC pond was analysed in different magnesium sulphate (MgSO4; 0.08, 0.16, 0.32, 0.64 and 1.28 g/L), potassium sulphate (K2SO4; 0.08, 0.16, 0.32, 0.64 and 1.28 g/L) and MgSO4/K2SO4 (0.16/0.00, 0.08/0.08, 0.04/0.12, 0.02/0.14 and 0.01/0.15 g/L) concentrations. Culture media pH, total dissolved solids (TDS) and conductivity rate were monitored. Microscopic analysis revealed sulphate salt concentrations influenced the number of whorls and filaments of S. platensis. K2SO4 (1.28 g/L) produced the highest number of whorls and filaments. Moreover, pH level fluctuated by sulphate treatments. K2SO4 (1.28 g/L) had a pH level of 8.77±0.01 (day 5 of culture incubation). TDS and conductivity rate, protein and cysteine contents increased with culture age and K2SO4 concentration in a culture medium. Conversely, negative correlations between protein and cysteine contents were observed, and sugar content decreased. Sulphate salt type and concentrations affected polyphenol oxidase (PPO) and peroxidase (POD) activities. MgSO4/K2SO4 (0.02/0.14 g/L) displayed the best PPO and POD activities. Both enzymes appeared to be negatively correlated to the decreasing sugar content. These results indicate growth performances and biochemical status of S. platensis are significantly improved with the adequate supplementation of sulphate salts (MgSO4 and K2SO4) in culture media.
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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) | 0.001 | 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".