Optimization of citric acid production by Aspergillus niger : NRRL 567 in various fermentation systems
Bibliographic record
Abstract
Among the various fungal strains screened for citric acid production, Aspergillus niger is known to produce considerable amounts of citric acid and other organic acids when cultivated in carbohydrate-rich medium in solid substrate fermentation (SSF). Since A. niger on a solid substrate grows under conditions similar to the natural habitat, SSF is ideal to cultivate A. niger for the purpose of producing citric acid. An initial optimization (study 1) was conducted in batch type fermentation experiments using peat moss supplemented with glucose to simulate an organic waste. The effects of various nutrients (glucose, (NH4)2SO 4, KH2PO4 and NaCl), fermentation parameters (moisture content, temperature, inoculum density, composition of solid substrate and particle size) and of initial level of potential stimulators (ethanol, methanol, phytate and surfactant) were evaluated with respect to citric acid production by A. niger grown on damp peat moss. In these experiments, optimization using a traditional 'one-factor-at-a-time' method was applied to determine key factor ranges for the production of citric acid. When the fermentation was carried out using the final optimal conditions. This allowed for a 50-fold increase in citric acid production compared to the production of citric acid by A. niger grown on peat moss supplemented with 100 g glucose/kg DPM. A second set of experiments (study 2) was conducted to optimize fermentation conditions for citric acid production in a column bioreactor. A. niger NRRL 567 grown on damp peat moss was held within a column bioreactor and periodically irrigated with a glucose-rich solution simulating field conditions. Three variables including aeration, thickness of solid substrate bed and incubation temperature were optimized using a 23 full factorial design (FFD). Under optimum, the total citric acid production and yield were 120.6 g/kg DPM and 18.5% respectively. A third experiment (study 3) compared the production of citric acid by A. niger in submerged fermentation using cheese whey, as opposed to batch and semi-continuous fermentation using peat moss. Various fermentation conditions such as nutrients (glucose, (NH4)2SO 4 and KH2PO4), stimulators (methanol, olive oil and phytate) and fermentation parameters (pH, fermentation time and inoculum density) were optimized using a central composite design (CCD). Citric acid production improved citric acid production by a factor of 13.3 when compared to the production of citric acid by A. niger NRRL 567 using whey-based medium (50 g/l) alone. As compared to submerged and semi-continuous fermentation, Batch type SSF could take higher levels of initial glucose and produce the high concentration of citric acid within a shorter period of time. Thus, SSF may be considered to be better technique than submerged fermentation, if main disadvantage like non-homogeneous fermentation conditions could be overcome.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".