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Related Experiment Video

Updated: May 28, 2026

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

Spectroscopy-Based Cell Culture Predictive Monitoring.

Ahmed Kanfoud¹, Pascal Gerkens², Marie Bastin¹

¹DNAlytics, 1 Rue Jean Monnet, B-1348 Ottignies-Louvain-la-Neuve, Belgium.

Biotech (Basel (Switzerland))

|May 27, 2026

Summary

This summary is machine-generated.

This study introduces a novel method for real-time cell culture monitoring using spectral data and machine learning. The technique anticipates future spectra, enabling predictive monitoring and reducing laboratory testing needs.

Keywords:

cell culture predictive monitoring process analytical technology

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Last Updated: May 28, 2026

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Area of Science:

Biotechnology
Analytical Chemistry
Machine Learning

Background:

Cell culture monitoring is crucial in biomanufacturing.
Current methods require significant hands-on time and laboratory testing.
Real-time monitoring is desirable for process optimization.

Purpose of the Study:

To develop a method for anticipating future spectra in cell cultures.
To enable predictive cell culture monitoring using machine learning.
To reduce the need for extensive laboratory testing and manual intervention.

Main Methods:

Spectral monitoring combined with chemometrics and machine learning.
Extrapolation of spectra in a reduced-dimensionality space.
Retroprojection of extrapolated spectra into the original space.

Main Results:

Anticipated spectra enabled predictive cell culture monitoring up to several dozen hours.
The method achieved satisfactory quality in predictions.
Reduced need for hands-on time and laboratory testing was demonstrated.

Conclusions:

The proposed method allows for near real-time, predictive cell culture monitoring.
Anticipation of future spectra enhances biomanufacturing operations.
This technology can lead to reduced operational costs and improved efficiency.