A model based approach for monitoring Bordetella pertussis fermentation with an inline spectro-fluorescence probe
Bibliographic record
Abstract
Presently, the extraction of the antigen pertactin poses a challenge in the manufacturing of the whooping cough vaccine due to its low and variable yield [1]. In this work, a hybrid model that combines empirical and mechanistic parts and in-line fluorescence measurements is used to design an estimator for monitoring the manufacturing process in bioreactors. The empirical part of the hybrid model uses Partial Least Squares (PLS) regression to estimate biomass, carbon source, and pertactin productivity from fluorescence data. In view that significant correlations are observed between oxidative stress and productivity, the mechanistic part of the hybrid model is based on key oxidative reaction pathways. Estimation based on a hybrid model is shown to improve the prediction accuracy of antigen productivity as compared to purely empirical or purely mechanistic model-based estimators. The proposed estimator enables real-time monitoring of the manufacturing process and opens the possibility of future implementation of mid-point corrective actions. • An online estimator was designed for monitoring pertussis vaccine manufacturing process. • Significant correlations were observed between oxidative stress and Pertactin (Antigen) productivity. • The hybrid model developed is based on key oxidative stress pathways. • Hybrid model has better accuracy than purely mechanistic model and purely statistical model (PLSR).
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".