Biotechnological potential of industrial wastes for economical citric acid bioproduction by <i>Aspergillus niger</i> through submerged fermentation
Bibliographic record
Abstract
Summary Submerged citric acid (CA) bioproduction was carried out by Aspergillus niger NRRL‐567 using various industrial wastes, such as brewery spent liquid (BSL), lactoserum and starch industry water sludge. CA bioproduction was carried out by varying the temperature (25–35 °C), pH (3–5), addition of inducers, incubation time and supplementation with different proportions of apple pomace ultrafiltration sludge (APS). The results indicated that under the best conditions with 3% (v/v) methanol, the optimal concentration of 11.34 g L−1 CA was recorded using BSL at pH 3.5 and temperature 30 °C after 120‐h incubation period. Supplementation of methanol resulted in an increase of 56% CA production. Meanwhile, under similar conditions, higher concentration of 18.34 g L−1 CA was reported with the supplementation of BSL with 40% (v/v) APS having suspended solids concentration of 30 g L−1. The present study demonstrated the potential of BSL supplemented with APS as an alternative cheap substrate for CA fermentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".