Development of a soft‐sensor based on multi‐wavelength fluorescence spectroscopy and a dynamic metabolic model for monitoring mammalian cell cultures
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Bibliographic record
Abstract
A soft-sensor based on an Extended Kalman Filter (EKF) that combines data obtained using a fluorescence-based soft-sensor with a dynamic mechanistic model, was investigated as a tool for continuous monitoring of a Chinese hamster ovary (CHO) cell cultivation process. A standalone fluorescence based soft-sensor, which uses a combination of an empirical multivariate statistical model and measured spectra, was designed for predicting key culture variables including viable and dead cells, recombinant protein, glucose, and ammonia concentrations. The standalone fluorescence sensor was then combined with a dynamic mechanistic model within an EKF framework, for improving the prediction accuracy and generating predictions in-between sampling instances. The dynamic model used for the EKF framework was based on a structured metabolic flux analysis and mass balances. In order to calibrate the fluorescence-based empirical model and the dynamic mechanistic model, cells were grown in batch mode with different initial glucose and glutamine concentrations. To mitigate the uncertainty associated with the model structure and parameters, non-stationary disturbances were accounted for in the EKF by parameter-adaptation. It was demonstrated that the implementation of the EKF along with the dynamic model could improve the accuracy of the fluorescence-based predictions at the sampling instances. Additionally, it was shown that the major advantage of the EKF-based soft-sensor, compared to the standalone fluorescence-based counterpart, was its capability to track the temporal evolution of key process variables between measurement instances obtained by the fluorescence-based soft-sensor. This is crucial for designing control strategies of CHO cell cultures with the aim of guaranteeing product quality.
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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) | 0.000 | 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) | 0.000 | 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