Optimization of citric acid production from sugarcane molasses using <i>Aspergillus niger</i> by submerged Fermentation
Bibliographic record
Abstract
ABSTRACT The potentiality of citric acid on economy is high because of its multi-purpose uses, particularly in the food and pharmaceutical industries. Bangladesh spent more than million US dollars to import citric acid mostly from India and China. Its consumption is increasing 3.5–4%, annually, indicating the need for better manufacturing alternatives. Globally, citric acid is primarily produced through microbial fermentation with Aspergillus niger . To support the massive scale of production of citrate, the manufacturing process must be eco-friendly which should be inexpensive and available raw materials for maintaining high yielding in a cost-effective manner. In Bangladesh prospective, the current study has undertaken to optimize citric acid production using one of the most abundant raw materials sugarcane molasses. Moreover, the aim of this study was to determine the optimum conditions to produce citric acid from sugarcane molasses using Aspergillus niger (F-81) by submerged fermentation. The amount of citric acid production was determined by Marier-Boulet colorimetric method. The optimization data suggested that 10% substrate (from processed cane molasses), 4% inoculum size of A. niger , and initial pH 6.0 allowed to produce around 25.8 g/L citric acid. Further study is warranted to assess the feasibility of citrate production in an industrial level as well as improvement of microbial strains is needed to further enhance citric acid production.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".