Improving dietary citric acid production by the wild‐type <i>Aspergillus niger</i> ASP26 strain isolated from date by‐product
Bibliographic record
Abstract
Abstract This research investigates citric acid (CA) synthesis using the indigenous strain Aspergillus niger ASP26, which was isolated from date by‐products. The study initially involved isolating fungi capable of CA production and identifying the most potent strain based on its characteristic enzymatic activity. A. niger ASP26 was acknowledged in a previous study for its remarkable ability to produce extracellular enzymes, such as cellulase and amylase, which enable it to degrade organic materials effectively. After the identification phase, these isolates were screened for CA production using a modified Czapek‐Dox medium. The research identified significant factors affecting CA production in submerged fermentation, including pH, carbon source, inoculum size, and fermentation time. Optimal conditions were determined for A. niger ASP26, resulting in a maximum CA yield of 16.89 g/L. These conditions included a 2.5% spore suspension at 2 × 10 7 spores/mL, an initial glucose concentration of 125 g/L, and incubation at 30°C for 144 h. Notably, A. niger ASP26 demonstrated the ability to produce CA under stress conditions as well. Citric acid is essential for various biological processes, such as cellular respiration, and is naturally present in citrus fruits. It also serves as a preservative and flavor enhancer in processed foods and beverages. The ability of A. niger ASP26 to produce CA from agricultural residues positions it as a viable candidate for sustainable CA production, harnessing the value from organic waste materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".