Enhanced solid-state citric acid bio-production using apple pomace waste through surface response methodology
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: To evaluate the potential of apple pomace (AP) supplemented with rice husk for hyper citric acid production through solid-state fermentation by Aspergillus niger NRRL-567. Optimization of two key parameters, such as moisture content and inducer (ethanol and methanol) concentration was carried out by response surface methodology. METHODS AND RESULTS: In this study, the effect of two crucial process parameters for solid-state citric acid fermentation by A. niger using AP waste supplemented with rice husk were thoroughly investigated in Erlenmeyer flasks through response surface methodology. Moisture and methanol had significant positive effect on citric acid production by A. niger grown on AP (P < 0·05). Higher values of citric acid on AP by A. niger (342·41gkg(-1) and 248·42gkg(-1) dry substrate) were obtained with 75% (v/w) moisture along with two inducers [3% (v/w) methanol and 3% (v/w) ethanol] with fermentation efficiency of 93·90% and 66·42%, respectively depending upon the total carbon utilized after 144h of incubation period. With the same optimized parameters, conventional tray fermentation was conducted. The citric acid concentration of 187·96gkg(-1) dry substrate with 3% (v/w) ethanol and 303·34gkg(-1) dry substrate with 3% (v/w) methanol were achieved representing fermentation efficiency of 50·80% and 82·89% in tray fermentation depending upon carbon utilization after 120h of incubation period. CONCLUSIONS: Apple pomace proved to be the promising substrate for the hyper production of citric acid through solid-state tray fermentation, which is an economical technique and does not require any sophisticated instrumentation. SIGNIFICANCE AND IMPACT OF THE STUDY: The study established that the utilization of agro-industrial wastes have positive repercussions on the economy and will help to meet the increasing demands of citric acid and moreover will help to alleviate the environmental problems resulting from the disposal of agro-industrial wastes.
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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 it