Key Nutrient Drivers for Biomass and C-Phycocyanin Production in Spirulina sp. Revealed by Media Optimization
Bibliographic record
Abstract
Optimizing nutrient formulations is essential to improving the biomass yield and C-phycocyanin (C-PC) productivity of Spirulina sp., a cyanobacterium with wide-ranging applications in food, pharmaceutical, and biotechnological industries. This study evaluated the effects of macronutrient modifications on growth and pigment biosynthesis using a two-level full factorial design across eight Zarrouk-based formulations compared to the standard medium. Cultivation experiments were conducted in triplicate, and growth was evaluated using linear growth rate, maximum optical density (OD680), and dry biomass, while C-PC was quantified in crude extracts (PCL), dried biomass (PCD), and the purity index (PI). Among the tested formulations, F2 (16 g/L NaHCO3, 5 g/L NaNO3, 0.25 g/L K2HPO4) achieved the highest biomass productivity, yielding a 37.6% increase in dry weight and a 38.1% improvement in daily productivity compared to the control. In contrast, F3 (16 g/L NaHCO3, 5 g/L NaNO3, 1 g/L K2HPO4) yielded the highest C-PC content, nearly doubling both PCL and PCD values and enhancing pigment purity by 40.2%. ANOVA and interaction analyses confirmed that carbon and nitrogen synergistically promoted biomass formation, while phosphorus had a strong effect on pigment biosynthesis through C:N:P interactions. These findings demonstrate that Spirulina sp. requires distinct nutrient balances for optimal growth and pigment formation. Formulation F2 is ideal for maximizing biomass productivity, whereas F3 is optimal for high-value C-PC production. The results provide a rational framework for designing nutrient-efficient cultivation systems to advance sustainable Spirulina-based biomanufacturing.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".