Screening of agro‐industrial wastes for citric acid bioproduction by <i>Aspergillus niger</i><scp>NRRL</scp> 2001 through solid state fermentation
Bibliographic record
Abstract
BACKGROUND: The citric acid (CA) industry is currently struggling to develop a sustainable and economical process owing to high substrate and energy costs. Increasing interest in the replacement of costly synthetic substrates by renewable waste biomass has fostered research on agro-industrial wastes and screening of raw materials for economical CA production. The food-processing industry generates substantial quantities of waste biomass that could be used as a valuable low-cost fermentation substrate. The present study evaluated the potential of different agro-industrial wastes, namely apple pomace (AP), brewer's spent grain, citrus waste and sphagnum peat moss, as substrates for solid state CA production using Aspergillus niger NRRL 2001. RESULTS: Among the four substrates, AP resulted in highest CA production of 61.06 ± 1.9 g kg(-1) dry substrate (DS) after a 72 h incubation period. Based on the screening studies, AP was selected for optimisation studies through response surface methodology (RSM). Maximum CA production of 312.32 g kg(-1) DS was achieved at 75% (v/w) moisture and 3% (v/w) methanol after a 144 h incubation period. The validation of RSM-optimised parameters in plastic trays resulted in maximum CA production of 364.4 ± 4.50 g kg(-1) DS after a 120 h incubation period. CONCLUSION: The study demonstrated the potential of AP as a cheap substrate for higher CA production. This study contributes to knowledge about the future application of carbon rich agro-industrial wastes for their value addition to CA. It also offers economic and environmental benefits over traditional ways used to dispose off agro-industrial wastes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".