Optimization of carotenoids production by Rhodotorula mucilaginosa (MTCC-1403) using agro-industrial waste in bioreactor: A statistical approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bio-colorants are preferred over synthetic colors as bio-colorants not only impart characteristic color to the food also contain harmless bio-active antioxidant nutrients. The present study was undertaken to investigate the potential of agro-industrial waste (Onion peels, potato skin, mung bean husk and pea pods) for carotenoid production from Rhodotorula mucilaginosa. After screening of appropriate carbon, nitrogen sources from agro-industrial waste, the fermentation conditions (pH, temperature, agitation) were optimized using Response Surface Methodology and optimum conditions were pH 6.1, temperature 25.8 ᴼC and agitation 119.6 rpm. Further, to evaluate the effect of aeration on carotenoids synthesis, fermentation was carried out in 3 L bio-reactor under optimum conditions with an air input of 1.0 vvm. Aeration causes elevation of more than 100 μg carotenoids per g of dry biomass. LC-MS of extracted pigment confirmed the presence of some other carotenoids along with β-carotene. The major carotenoid compounds were found from the investigation were torularhodin, β-carotene, and torulene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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