In‐line prediction of viability and viable cell density through machine learning‐based soft sensor modeling and an integrated systems approach: An industrially relevant <scp>PAT</scp> case study

Sneha Suman, Michaela Murr, Jacob Crowe, Spencer Holt, Jakob Morris, Andrew Yongky, Kyle McElearney, Glen Bolton

Open source

DOI: 10.1002/btpr.3520
Published: 2025-01-23
Container: Biotechnology Progress
Publisher: Wiley
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1002/btpr.3520,
  title = {In‐line prediction of viability and viable cell density through machine learning‐based soft sensor modeling and an integrated systems approach: An industrially relevant
                    <scp>PAT</scp>
                    case study},
  author = {Sneha Suman and Michaela Murr and Jacob Crowe and Spencer Holt and Jakob Morris and Andrew Yongky and Kyle McElearney and Glen Bolton},
  year = {2025},
  journal = {Biotechnology Progress},
  doi = {10.1002/btpr.3520},
  url = {https://doi.org/10.1002/btpr.3520}
}

RIS

TY  - JOUR
TI  - In‐line prediction of viability and viable cell density through machine learning‐based soft sensor modeling and an integrated systems approach: An industrially relevant
                    <scp>PAT</scp>
                    case study
AU  - Sneha Suman
AU  - Michaela Murr
AU  - Jacob Crowe
AU  - Spencer Holt
AU  - Jakob Morris
AU  - Andrew Yongky
AU  - Kyle McElearney
AU  - Glen Bolton
PY  - 2025
JO  - Biotechnology Progress
DO  - 10.1002/btpr.3520
UR  - https://doi.org/10.1002/btpr.3520
ER  -

APA

Suman, S., Murr, M., Crowe, J., Holt, S., Morris, J., Yongky, A., McElearney, K., & Bolton, G. (2025). In‐line prediction of viability and viable cell density through machine learning‐based soft sensor modeling and an integrated systems approach: An industrially relevant <scp>PAT</scp> case study. Biotechnology Progress. https://doi.org/10.1002/btpr.3520

Source records

crossref · retrieved 2026-09-25T00:46:48.699Z