Inline monitoring of lactobionic acid production from cheese whey by <scp> <i>Pseudomonas taetrolens</i> </scp> in a stirred bioreactor using electrical conductivity
Bibliographic record
Abstract
Abstract In this study, we introduce a novel experimental approach and present a simplified mathematical model for a quick monitoring of a biotec process producing lactobionic acid (LBA). It relies on monitoring the electrical conductivity of the fermentation broth and it is designed to predict the concentration of LBA throughout the microbial cheese whey valorization via LBA production. Following a systematic series of experiments conducted to refine the mathematical model, we performed conductivity monitoring during LBA production from “caciotta” and “squacquerone” wheys by Pseudomonas taetrolens in a 3 L stirred tank bioreactor. Throughout the bioproduction process, the conductivity values exhibited an upward trend corresponding to the increase in LBA concentration. Our findings underscore the feasibility and advantages of employing inline conductivity monitoring during LBA production from various cheese wheys. The results emphasize that conductivity measurements can effectively estimate product concentration in a fermentation process, particularly when there is a shift in ionic concentration. Furthermore, these conductivity measurements offer valuable insights for monitoring and optimizing the working conditions in a fermentation process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".