Monitoring Mammalian Cell Cultivations for Monoclonal Antibody Production Using Near-Infrared Spectroscopy

  • João G. Henriques
  • Stefan Buziol
  • Elena Stocker
  • Arthur Voogd
  • José C. Menezes
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 116)


Near-infrared (NIR) spectroscopy as a process monitoring and process supervision technique is reviewed in the context of biomanufacturing.

An industrial pilot-plant mammalian cell cultivation process has been chosen to illustrate the use of on-line in-situ NIR monitoring by means of an immersion transflectance NIR probe.

NIR calibration development must be performed carefully and should incorporate a number of steps to obtain a properly validated model which exhibits long-term robustness and is independent of process scale. A description of such good modelling practises is given. In general, NIR can be as accurate as the reference methods employed and at least as precise provided that sufficient spectral selectivity and sensitivity exists.

NIR can also be used as a direct technique for very fast process monitoring and process supervision, thus enabling one to follow the trajectory of a process. This alternative to the indirect use of NIR through laborious calibration development with direct reference methods has been little explored. Since NIR is sensitive to both chemical and physical properties, the analysis of whole samples enables relevant process information to be captured and thus generates better process state estimates than by simply looking at defined process parameters one at a time.


Process Analytical Technology Biomanufacturing Process Spectro scopy NIR mammalian cells cultivation 

Symbols and Abbreviations


High performance liquid chromatography


Latent variable


Monoclonal antibody




Process analytical technologies


Principal component


Principal component analysis


Partial least-squares


Correlation coefficient for cross-validation predictions


Correlation coefficient for external validation predictions


Root mean square error of cross-validation


Root mean square error of prediction


Standard error of laboratory




Standard normal variate


Variable importance plot



Dr Licinia O. Rodrigues (4TUNE Engineering Ltd) for discussions on the material in the paper. Mrs Miriam Ahlert (Roche Diagnostics GmbH, Germany) for support in the analytical work.


Genetic algorithm

Numerical method for feature selection based on the mechanisms of biologic evolution


Matrix describing how the original variables relate to the new principal components of a PCA

Partial least-squares

Factorisation method typically used for regression when large numbers of collinear variables are present

Principal component analysis

Data factorisation methods that creates new orthogonal variables called principal components as linear combination of the original variables capturing the most possible variance in the original data


Projection of an observation (sample) in the principal component space of a PCA


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • João G. Henriques
    • 1
    • 2
  • Stefan Buziol
    • 3
  • Elena Stocker
    • 3
  • Arthur Voogd
    • 4
    • 5
  • José C. Menezes
    • 6
  1. 1.4TUNE Engineering Ltd – Atrium SaldanhaLisbonPortugal
  2. 2.HOVIONE, Sete CasasLisbonPortugal
  3. 3.Roche Diagnostics GmbH – Pharmaceutical Biotech Production and DevelopmentPenzbergGermany
  4. 4.Yokogawa Europe BVAmersfoortThe Netherlands
  5. 5.LaboSer BVRotterdamThe Netherlands
  6. 6.IBB-Institute for Biotechnology and BioengineeringIST-Technical University of LisbonLisbonPortugal

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