Optimization of phytase production from potato waste using Aspergillus ficuum
Bibliographic record
Abstract
Solid-state fermentation (SSF) can divert food waste from landfills and produce high-value products. This study was aimed to investigate the feasibility of using SSF and optimize the conditions of production of phytase by Aspergillus ficuum from potato waste. Different parameters including pH of the potato waste, inoculum level, moisture content, incubation period, temperature, and supplementary nitrogen and carbon sources were evaluated. The results indicated that pH, inoculum level, and moisture content did not significantly vary phytase production. However, different incubation periods, incubation temperatures, nitrogen sources, and carbon sources changed the phytase production significantly. The ideal and economic conditions for phytase production consisted of a normal moisture content (79%) of potato waste, 1.0 ml inoculum size, and normal pH 6.1 at room temperature for 144 h incubation time. The highest phytase activity (5.17 ± 0.82 U/g ds) was obtained under the aforementioned optimized conditions. When (NH 4 )2SO 4 was used as a nitrogen source in the substrate, the phytase activity increased to 12.93 ± 0.47 U/g ds, which was a 2.5-fold increase compared to the control treatment. This study proposed a novel and economical way to convert food processing waste to highly valuable products and investigated the optimal conditions of the production of phytase during SSF in potato waste.
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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) | 0.000 | 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".