Combined Effects of Temperature and Medium Composition on Exopolysaccharide Production by <i>Lactobacillus</i> <i>rhamnosus</i> RW‐9595M in a Whey Permeate Based Medium
Bibliographic record
Abstract
The effects of temperature (22-42 degrees C), whey permeate concentration (WP, 1.6-8.4%), and supplementation level with yeast nitrogen base (YNB, 0-2.0%) on exopolysaccharide (EPS) production was studied during 20 pH-controlled (pH = 6.0) batch cultures with Lactobacillus rhamnosus RW-9595M, using a central composite design (CCD). The EPS production was measured using both the conventional method based on ethanol precipitation of EPS and a new ultrafiltration (UF) method. EPS production was not growth-associated for high temperatures (32-42 degrees C) and WP concentrations (7.0-8.4%). In contrast, at suboptimal temperature (22-26 degrees C), EPS production was growth-associated. Maximal EPS production measured with the UF method was approximately 2-fold higher than those measured with the conventional method and varied from 125 to 477 mg/L. This parameter was significantly influenced by WP and YNBWP interaction, whereas ANOVA for maximal EPS production measured by the conventional method did not show significant factor effects. EPS volumetric productivities varied from 3.0 to 16.4 mg EPS/L small middle doth. YNB supplementation did not promote cell growth but did increase EPS production at high WP concentrations. Our data indicate the potential of L. rhamnosus RW-9595M for producing EPS in a supplemented WP medium and suggest that this production could be further increased by the addition of a growth-limiting nutrient in the medium.
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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) | 0.001 | 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".