A robust multinutrient kinetic model for enhanced lutein and biomass yields in mixotrophic microalgae cultivation: A step towards successful large‐scale productions
Bibliographic record
Abstract
Abstract Mixotrophic cultivation holds great promise to significantly enhance the productivities of biomass and valuable metabolites from microalgae. In this study, a new kinetic model is developed, explicitly describing the effect of the most influential environmental factors on both biomass growth and the production of the high‐value product lutein. This extensive study of multinutrient kinetics for Tetradesmus obliquus in a mixotrophic regime covers various nutritional conditions. Crucial nutrients governing the model include nitrate, phosphate, and glucose. Using seven state variables and 13 unknown parameters, the model's accuracy was ensured through a well‐designed two‐factor, four‐level experimental setup, providing ample data for reliable calibration and validation. Results accurately predict dynamic concentration profiles for all validation experiments, revealing broad applicability. Optimizing nitrogen availability led to significant increases in biomass (up to fourfold) and lutein production (up to 12‐fold), with observed maximum biomass concentration of 6.80 g L−1 and lutein reaching 25.58 mg L−1. Noticeably, the model exhibits a maximum specific growth rate of 4.03 day−1, surpassing reported values for photoautotrophic and heterotrophic conditions, suggesting synergistic effects. Valuable guidance is provided for applying the method to various microalgal species and results are large‐scale production‐ready. Future work will exploit these results to develop real‐time photobioreactor operation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".