Near-infrared spectroscopy in upstream bioprocesses

Hoehse, Marek; Alves-Rausch, José; Prediger, Andreas; Roch, Patricia; Grimm, Christian

doi:10.4155/pbp.15.1

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Several optical sensors with different wavelength ranges, such as ultraviolet–visible (UV–Vis), near‐infrared (NIR), mid‐infrared (MIR), Raman, and fluorescence spectroscopy are in research for industrial application for the past decades . These sensors enable simultaneous noninvasive, in‐line, and online bioprocess monitoring of various process variables including biological and chemical variables during cell cultivation processes . Each technique or rather each wavelength range has their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

Claßen

Graf

Aupert

et al. 2019

Engineering in Life Sciences

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This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.

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Section: Introductionmentioning

confidence: 99%

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

Claßen

Graf

Aupert

et al. 2019

Engineering in Life Sciences

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“…Near infrared (NIR) spectroscopy is widely used and accepted. Many successful applications of NIR spectroscopy for monitoring of biotechnological upstream processes are reported .…”

Section: Introductionmentioning

confidence: 99%

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

Voss

Mittelheuser

Lemke

et al. 2017

Engineering in Life Sciences

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This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSEcvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSEPrel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.

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“…In this contribution, we established a control strategy for sensors that may have a significant measurement error (>50%), and demonstrate a robust strategy for process control purposes. In the center of attention are those sensors which measure biomass and substrate level and their respective errors, independent of what kind of underlying mechanism was used, i.e., turbidity, capacitance, offgas analysis, online analyzer, disposable sensors, RAMAN, NIR/MIR, FACS, in‐situ microscopes among many others . A mathematical environment was established in Matlab where continuously estimated online biomass, continuously estimated metabolic state, and discretely measured glucose concentrations are used as inputs in a metabolic model.…”

Section: Introductionmentioning

confidence: 99%

A robust feeding strategy to maintain set‐point glucose in mammalian fed‐batch cultures when input parameters have a large error

Konakovsky

Clemens

Müller

et al. 2017

Biotechnology Progress

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Industrial CHO cell cultures run under fed-batch conditions are required to be controlled in particular ranges of glucose, while glucose is constantly consumed and must be replenished by a feed. The most appropriate feeding rate is ideally stoichiometric and adaptive in nature to balance the dynamically changing rate of glucose consumption. However, high errors in biomass and glucose estimation as well as limited knowledge of the true metabolic state challenge the control strategy. In this contribution, we take these errors into account and simulate the output with uncertainty trajectories in silico in order to control glucose concentration. Other than many control strategies, which require parameter estimation, our assumptions are founded on two pillars: (i) first principles and (ii) prior knowledge about the variability of fed-batch CHO cell culture. The algorithm was exposed to an in-silico Design of Experiments (DoE), in which variations of parameters were changed simultaneously, such as clone-specific behavior, precision of equipment and desired control range used. The results demonstrate that our method achieved the target of holding the glucose concentration within an acceptable range. A robust and sufficient level of control could be demonstrated even with high errors for biomass or metabolic state estimation. In a time where blockbuster drugs are queuing up for time slots of their production, this transferable control strategy that is independent of tedious establishment runs may be a decisive advantage for rapid implementation during technology transfer and scale up and decrease in campaign change over time. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:317-336, 2017.

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Near-infrared spectroscopy in upstream bioprocesses

Cited by 12 publications

References 25 publications

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

A robust feeding strategy to maintain set‐point glucose in mammalian fed‐batch cultures when input parameters have a large error

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