ORIGINAL RESEARCH: Centrifugal recovery of rhizobial cells from fermented starch industry wastewater & development of stable formulation
Bibliographic record
Abstract
Application of rhizobial inoculant is an agricultural practice successfully used to increase growth and yield of leguminous plants. Development of a concentrated culture is essential for advanced formulations. This study assessed the use of starch industry wastewater as a potent carbon source for production of Sinorhizobium meliloti, with a maximum cell count of 4×109 CFU/mL. Optimal conditions for maximum recovery of cells (>99%) during centrifugation were determined to be 8 000 g, at a temperature of 20 °C for 20 min, pH 7, using a swinging bucket rotor and a surface response methodology. Of the different centrifugal aids tested, starch (2% w/v) best minimized the loss of microbial cells during recovery from the supernatant. A fixed- angle-rotor experiment was also carried out to determine differences in recovery between centrifugal configurations; optimal recovery was observed with the swinging bucket rotor (>99%) versus a fixed angle rotor (90%). A further decrease (from 90% to 87%) in recovery was observed with a 20 times increase in broth volume at 8 000 g, centrifuged for 20 min at 20 °C. The addition of soya oil during centrifugation contributed to emulsion formulation. A slight decrease was observed in CFU values using an antimicrobial agent, as compared to the control of centrifugate and oil emulsified with 0.1% v/v surfactant, after one month of storage. Suspension formulation with alginate additive showed a cell viability of more than 10 9 CFU/mL after 9 weeks of storage. This study demonstrates the feasibility of cell recovery and simultaneous formulation development of Sinorhizobium. Further investigation of parameter optimization for development of advanced formulations using recovered cells is warranted.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".