Sensors and chemometrics in downstream processing

Dürauer, Astrid; Jungbauer, Alois; Scharl, Theresa

doi:10.1002/bit.28499

Cited by 5 publications

(2 citation statements)

References 110 publications

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“…Another major development over the past decade has been in the use of multivariate analysis as soft sensors, which augments further development of rapid-quantification analytical techniques with the potential for real-time or in situ monitoring (Jiang et al, 2017;Rathore et al, 2022). Chemometrics has been used to improve the accuracy and robustness of an analytical methods, thereby facilitating the use of spectral analysis for PAT implementation (Dürauer et al, 2023;Rathore, 2014;Wang et al, 2022). Extensive studies have been conducted in spectral data analysis using classical chemometric techniques such as principal component analysis, partial least square (PLS) regression, synergy interval PLS, and support vector regression (SVR).…”

Section: Introductionmentioning

confidence: 99%

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee,

Mandal,

Jesubalan

et al. 2024

Biotech & Bioengineering

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As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near‐infrared (NIR) spectroscopy, are finding increasing use for real‐time quantitative analysis. Yet detection of multiple low‐concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module‐based two‐dimensional (2D) CNN approach (I‐CNN) for calibrating multiple analytes using NIR spectral data. The I‐CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I‐CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I‐CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real‐time process monitoring and comparison with offline analytics. The proposed I‐CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

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Section: Introductionmentioning

confidence: 99%

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee,

Mandal,

Jesubalan

et al. 2024

Biotech & Bioengineering

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“…More complex processes need more sophisticated mathematical models, and analytical solutions are not always available. These models offer greater extrapolation capabilities compared to data-driven or statistical models (Dürauer et al, 2023;Tang et al, 2023). Establishing robust and efficient models require proper model calibration and suitable calibration experiments (Chen et al, 2022;Yang et al, 2024).…”

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confidence: 99%

Residence time distribution in continuous virus filtration

Chen,

Recanati,

De Mathia

et al. 2024

Biotech & Bioengineering

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Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale‐up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO3. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( 0.90). The model enabled the prediction of RTD with variations—for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas—and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved 0.97. Notably, if the process includes a porous material—such as a porous chromatography material, ultrafilter, or virus filter—it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO3 enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end‐to‐end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.

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Assessment of data‐driven modeling approaches for chromatographic separation processes

Michalopoulou,

Papathanasiou

2024

AIChE Journal

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Chromatographic separation processes are described by nonlinear partial differential and algebraic equations, which may result in high computational cost, hindering further online applications. To decrease the computational burden, different data‐driven modeling approaches can be implemented. In this work, we investigate different strategies of data‐driven modeling for chromatographic processes, using artificial neural networks to predict pseudo‐dynamic elution profiles, without the use of explicit temporal information. We assess the performance of the surrogates trained on different dataset sizes, achieving good predictions with a minimum of 3400 data points. Different activation functions are used and evaluated against the original high‐fidelity model, using accuracy, interpolation, and simulation time as performance metrics. Based on these metrics, the best performing data‐driven models are implemented in a process optimization framework. The results indicate that data‐driven models can capture the nonlinear profile of the process and that can be considered as reliable surrogates used to aid process development.

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Sensors and chemometrics in downstream processing

Cited by 5 publications

References 110 publications

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Residence time distribution in continuous virus filtration

Assessment of data‐driven modeling approaches for chromatographic separation processes

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