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Fast and reliable methods to determine biomass concentration are necessary to facilitate the large scale production of microalgae. A method for the rapid estimation of Chlorella sorokiniana biomass concentration was developed. The method translates the suspension particle size spectrum gathered though laser reflectance into biomass concentration by means of two machine learning modelling techniques. In each case, the model hyper-parameters were selected applying a simulated annealing algorithm. The results show that dry biomass concentration can be estimated with a very good accuracy (R 2 =0.87). The presented method seems to be suited to perform fast estimations of biomass concentration in suspensions of microalgae cultivated in moderately turbid media with tendency to aggregate., (Copyright © 2017 Elsevier B.V. All rights reserved.)

A surface plasmon resonance (SPR)-based assay has been developed in order to quantify the monoclonal antibody (Mab) Trastuzumab within the supernatant of a mammalian cell culture using the ectodomain of FcγRI (CD64) and confirm Mab bioactivity, i.e. binding to its antigen Her2, in a single biosensing experiment. Under partial mass transport limitation, we were able to quantify Mab present in unpurified samples taken throughout the cell culture. While Mab capture on the biosensor surface confirmed the ability of its Fc region to bind to FcγRI, the binding activity of its Fab region was also tested by injecting increasing concentrations of the Mab antigen (Her2). The kinetics of the interactions we recorded from 48h post transfection until the end of the culture, were superimposable, which highly suggested that the quality attributes of the antibody were conserved throughout the process. This SPR methodology is thus of great interest for atline quality control analysis during Mab production campaign., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The bacterium Escherichia coli is a well-studied recombinant host organism with a plethora of applications in biotechnology. Highly valuable biopharmaceuticals, such as antibody fragments and growth factors, are currently being produced in E. coli. However, the high metabolic burden during recombinant protein production can lead to cell death, consequent lysis, and undesired product loss. Thus, fast and precise analyzers to monitor E. coli bioprocesses and to retrieve key process information, such as the optimal time point of harvest, are needed. However, such reliable monitoring tools are still scarce to date. In this study, we cultivated an E. coli strain producing a recombinant single-chain antibody fragment in the cytoplasm. In bioreactor cultivations, we purposely triggered cell lysis by pH ramps. We developed a novel toolbox using UV chromatograms as fingerprints and chemometric techniques to monitor these lysis events and used flow cytometry (FCM) as reference method to quantify viability offline. Summarizing, we were able to show that a novel toolbox comprising HPLC chromatogram fingerprinting and data science tools allowed the identification of E. coli lysis in a fast and reliable manner. We are convinced that this toolbox will not only facilitate E. coli bioprocess monitoring but will also allow enhanced process control in the future., Competing Interests: The authors declare that they have no conflict of interest.

The on-line monitoring with two-dimensional (2D) fluorescence spectroscopy of Saccharomyces cerevisiae batch cultivations was applied to monitor glucose, ethanol, and biomass concentrations. The measurement of one spectrum by the 2D fluorescence spectrometer has 120 fluorescence intensity values of excitation and emission wavelength combinations (WLCs); scattered light is not considered here. To identify which WLCs of the multi-wavelength fluorescence spectra carry important and relevant information regarding the analyte concentrations, three different methods were compared: a method based on loadings, variable importance in projection, and ant colony optimization. The five selected WLCs for a particular analyte from each method were evaluated by multiple linear regression models to find the most significant variable subsets for predicting the sample concentrations. The most significant WLCs relevant to the three sample properties contained seven different excitation and emission wavelengths, which can combine with each other to have 38 possible wavelength combinations in the fluorescence measurement. Partial least squares (PLS) models were calibrated with the 38 possible variables and the off-line data for the prediction of glucose, ethanol, and biomass concentrations. The best prediction from the PLS models had the percentage of root mean square error of prediction (pRMSEP) in the range of 3.1-6.3 %, which was similar to pRMSEPs of the PLS models with the full variables. Based on these results, it is promising to build up a specific inexpensive fluorescence sensor for the yeast cultivation process using light-emitting diodes and photodiodes. Graphical abstract Flow chart of the selection of important and relevant WLCs by the three methods to predict glucose, ethanol, and biomass concentrations.

The use of spectroscopic sensors for bioprocess monitoring is a powerful tool within the process analytical technology (PAT) initiative of the US Food and Drug Administration. Spectroscopic sensors enable the simultaneous real-time bioprocess monitoring of various critical process parameters including biological, chemical, and physical variables during the entire biotechnological production process. This potential can be realized through the combination of spectroscopic measurements (UV/Vis spectroscopy, IR spectroscopy, fluorescence spectroscopy, and Raman spectroscopy) with multivariate data analysis to obtain relevant process information out of an enormous amount of data. This review summarizes the newest results from science and industry after the establishment of the PAT initiative and gives a critical overview of the most common in-line spectroscopic techniques. Examples are provided of the wide range of possible applications in upstream processing and downstream processing of spectroscopic sensors for real-time monitoring to optimize productivity and ensure product quality in the pharmaceutical industry.

Rapid quantitation of product titer is a critical input for control of any bioprocess. This measurement, however, is marred by the myriad components that are present in the fermentation broth, often requiring extensive sample pretreatment before analysis. Spectroscopy techniques such as fluorescence spectroscopy are widely recognized as potential monitoring tools. Here, we investigate the possibility of using fluorescence of the culture supernatant as a potential at-line monitoring tool to measure the concentration of a recombinant therapeutic protein expressed in a Pichia pastoris fed-batch fermentation. We propose an integrated method wherein both the target protein and total protein concentrations are predicted using intrinsic riboflavin fluorescence and extrinsic fluorescence, respectively. The root mean square error for estimating the concentrations of the target protein (using riboflavin fluorescence) and total protein (using extrinsic fluorescence) have been estimated to be <0.1 and <0.2, respectively. The proposed approach has been validated for two different biotherapeutic products, human serum albumin and granulocyte colony stimulating factor, that were expressed using Mut + and Mut s strains of P. pastoris , respectively. The proposed approach is rapid (1 min analysis time, 10 min total with at line sampling) and thus could be a significant enabler for process analytical technology implementation in Pichia fermentation., (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.)

In this work, the evolution of different biogenic fluorophores involved in the metabolism of Pichia pastoris was determined at four different single-wavelength pairs (excitation/emission) during batch culture in microwell plates and used for effective and reliable biomass estimation by means of chemometric tools. The chemometric tools for biomass estimation were multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Variable importance in the projection (VIP) scores were used to rate the importance of model input variables, indicating tryptophan as the most important variable for biomass estimation. A direct correlation between the single fluorescence signals of tryptophan and biomass was additionally set up. Results indicate a successful fitting of the MLR, PLSR, PCR, and direct tryptophan correlation models for the present case and confirm the relevance of biogenic fluorophores for bioprocess state variables monitoring. The root mean squared error of prediction (RMSEP) between the predicted and measured values for the validation batches was 0.017, 0.023, 0.025, and 0.049 g L(-1) dry cell weight for MLR, PLSR, PCR, and direct tryptophan correlation, respectively. The presented approach of indirectly measuring biomass based on combined single-wavelength fluorescence measurements can be used for the development of a low-cost alternative to 2D fluorescence spectroscopy. Biotechnol. Bioeng. 2016;113: 2394-2402. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Fluorescence spectroscopy in combination with multivariate statistical methods was employed as a tool for monitoring the manufacturing process of pertactin (PRN), one of the virulence factors of Bordetella pertussis utilized in whopping cough vaccines. Fluorophores such as amino acids and co-enzymes were detected throughout the process. The fluorescence data collected at different stages of the fermentation and purification process were treated employing principal component analysis (PCA). Through PCA, it was feasible to identify sources of variability in PRN production. Then, partial least square (PLS) was employed to correlate the fluorescence spectra obtained from pure PRN samples and the final protein content measured by a Kjeldahl test from these samples. In view that a statistically significant correlation was found between fluorescence and PRN levels, this approach could be further used as a method to predict the final protein content.

Increasingly stringent demands on the production of biopharmaceuticals demand monitoring of process parameters that impact on their quality. We developed an automated platform for on-line, near real-time monitoring of suspension cultures by integrating microfluidic components for cell counting and filtration with a high-resolution separation technique. This enabled the correlation of the growth of a human lymphocyte cell line with changes in the essential metabolic markers, glucose, glutamine, leucine/isoleucine and lactate, determined by Sequential Injection-Capillary Electrophoresis (SI-CE). Using 8.1 mL of media (41 μL per run), the metabolic status and cell density were recorded every 30 min over 4 days. The presented platform is flexible, simple and automated and allows for fast, robust and sensitive analysis with low sample consumption and high sample throughput. It is compatible with up- and out-scaling, and as such provides a promising new solution to meet the future demands in process monitoring in the biopharmaceutical industry., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Rapid, efficient, and low-cost technologies for monitoring the fermentation process during second generation (2G) or cellulosic ethanol production are essential for the successful implementation of this process at the commercial scale. Here, the use of near-infrared (NIR) spectroscopy associated with partial least squares (PLS) regression was investigated as a tool for monitoring the production of 2G ethanol from lignocellulosic sugarcane residues including bagasse, straw, and tops. The spectral data was based on a set of 103 alcoholic fermentation samples. Models based on different pre-processing techniques were evaluated. The best root mean square error of prediction (RMSEP) values obtained in the external validation were around 3.02 g/L for ethanol and 6.60 g/L for glucose. The findings showed that the PLS-NIR methodology was efficient in accurately predicting the glucose and ethanol concentrations during the production of 2G ethanol, demonstrating potential for use in monitoring and control of large-scale industrial processes., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

In an anaerobic/aerobic/anoxic (A/O/A) sequencing batch reactor (SBR), non-filamentous bulking sludge granulated after the adjustment of cycle duration and influent composition directed by pH, oxidation-reduction potential (ORP) and dissolved oxygen (DO). The turning points and plateaux of pH, ORP and DO profiles indicated the end of biochemical reactions, such as chemical oxygen demand (COD) consumption, P release, ammonium oxidation, P uptake and denitrification. The difference of nutrient concentration between the beginning and turning points represented the actual treatment capability of the sludge. Non-filamentous bulking with SVI30 of 255 mL g(-1) resulted in a huge biomass loss. After regulation, the cycle duration was shortened from 310 to 195 min without unnecessary energy input. In addition, the settling ability was obviously improved as SVI30 reduced to 28 mL g(-1). Moreover, matured granules with an average diameter of 600 μm were obtained after 45 days, and simultaneous COD, ammonium and phosphate (P) removal was also realized after granulation.

: This chapter addresses the update progress in bioprocess engineering. In addition to an overview of the theory of multi-scale analysis for fermentation process, examples of scale-up practice combining microbial physiological parameters with bioreactor fluid dynamics are also described. Furthermore, the methodology for process optimization and bioreactor scale-up by integrating fluid dynamics with biokinetics is highlighted. In addition to a short review of the heterogeneous environment in large-scale bioreactor and its effect, a scale-down strategy for investigating this issue is addressed. Mathematical models and simulation methodology for integrating flow field in the reactor and microbial kinetics response are described. Finally, a comprehensive discussion on the advantages and challenges of the model-driven scale-up method is given at the end of this chapter.

By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions., (© 2015 American Institute of Chemical Engineers.)

Intrinsic fluorescence spectroscopy, in conjunction with partial least squares regression (PLSR), was investigated as a potential technique for online quality control and quantitative monitoring of Immunoglobulin G (IgG) aggregation that occurs following exposure to conditions that emulate those that can occur during protein downstream processing. Initially, the impact of three stress factors (temperature, pH, and protein concentration) on the degree of aggregation determined using size exclusion chromatography data, was investigated by performing a central composite designexperiment and applying a fitting response surface model. This investigation identified the influence of the factors as well as the operating regions with minimum propensity to induce protein aggregation. Spectral changes pertinent to the stressed samples were also investigated and found to corroborate the high sensitivity of the intrinsic fluorescence to conformational changes of the proteins under study. Ultimately, partial least squares regression was implemented to formulate two fluorescence-based soft sensors for quality control--product classification--and quantitative monitoring--concentration of monomer. The resulting regression models exhibited accurate prediction ability and good potential for in situ monitoring of monoclonal antibody downstream purification processes., (© 2015 American Institute of Chemical Engineers.)

For the development of biotechnological processes in academia as well as in industry new techniques are required which enable online monitoring for process characterization and control. Nuclear magnetic resonance (NMR) spectroscopy is a promising analytical tool, which has already found broad applications in offline process analysis. The use of online monitoring, however, is oftentimes constrained by high complexity of custom-made NMR bioreactors and considerable costs for high-field NMR instruments (>US$200,000). Therefore, low-field (1) H NMR was investigated in this study in a bypass system for real-time observation of fermentation processes. The new technique was validated with two microbial systems. For the yeast Hansenula polymorpha glycerol consumption could accurately be assessed in spite of the presence of high amounts of complex constituents in the medium. During cultivation of the fungal strain Ustilago maydis, which is accompanied by the formation of several by-products, the concentrations of glucose, itaconic acid, and the relative amount of glycolipids could be quantified. While low-field spectra are characterized by reduced spectral resolution compared to high-field NMR, the compact design combined with the high temporal resolution (15 s-8 min) of spectra acquisition allowed online monitoring of the respective processes. Both applications clearly demonstrate that the investigated technique is well suited for reaction monitoring in opaque media while at the same time it is highly robust and chemically specific. It can thus be concluded that low-field NMR spectroscopy has a great potential for non-invasive online monitoring of biotechnological processes at the research and practical industrial scales., (© 2015 Wiley Periodicals, Inc.)

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration., (© 2015 American Institute of Chemical Engineers.)

Pooling decisions in preparative liquid chromatography for protein purification are usually based on univariate UV absorption measurements that are not able to differentiate between product and co-eluting contaminants. This can result in inconsistent pool purities or yields, if there is a batch-to-batch variability of the feedstock. To overcome this analytical bottleneck, a tool for selective inline quantification of co-eluting model proteins using mid-UV absorption spectra and Partial Least Squares Regression (PLS) was presented in a previous study and applied for real-time pooling decisions. In this paper, a process-data-based method for the PLS model calibration will be introduced that allows the application of the tool towards chromatography steps of real-life processes. The process-data-based calibration method uses recorded inline mid-UV absorption spectra that are correlated with offline fraction analytics to calibrate PLS models. In order to generate average spectra from the inline data, a Visual Basic for Application macro was successfully developed. The process-data-based model calibration was established using a ternary model protein system. Afterwards, it was successfully demonstrated in two case studies that the calibration method is applicable towards real-life separation issues. The calibrated PLS models allowed a successful quantification of the co-eluting species in a cation-exchange-based aggregate and fraction removal during the purification of monoclonal antibodies and of co-eluting serum proteins in an anion-exchange-based purification of Cohn supernatant I. Consequently, the presented process-data-based PLS model calibration in combination with the tool for selective inline quantification has a great potential for the monitoring of future chromatography steps and may contribute to manage batch-to-batch variability by real-time pooling decisions., (© 2015 Wiley Periodicals, Inc.)

The amino acid composition of cultivation broth is known to affect the biomass accumulation, productivity, and vitality of yeast during cultivation. A separation method based on capillary electrophoresis with laser-induced fluorescence (LIF) detection was developed for the determination of amino acid consumption by Saccharomyces cerevisiae during beer fermentation. Intraday relative standard deviations were less than 2.1% for migration times and between 2.9% and 9.9% for peak areas. Interday relative standard deviations were less than 2.5% for migration times and between 4.4% and 18.9% for peak areas. The quantification limit was even as low as 62.5 pM which equals to below attomole level detection. The method was applied to study the rate of amino acid utilization during beer fermentation., (Copyright © 2014 Elsevier B.V. All rights reserved.)

A soft-sensor based on an Extended Kalman Filter (EKF) that combines data obtained using a fluorescence-based soft-sensor with a dynamic mechanistic model, was investigated as a tool for continuous monitoring of a Chinese hamster ovary (CHO) cell cultivation process. A standalone fluorescence based soft-sensor, which uses a combination of an empirical multivariate statistical model and measured spectra, was designed for predicting key culture variables including viable and dead cells, recombinant protein, glucose, and ammonia concentrations. The standalone fluorescence sensor was then combined with a dynamic mechanistic model within an EKF framework, for improving the prediction accuracy and generating predictions in-between sampling instances. The dynamic model used for the EKF framework was based on a structured metabolic flux analysis and mass balances. In order to calibrate the fluorescence-based empirical model and the dynamic mechanistic model, cells were grown in batch mode with different initial glucose and glutamine concentrations. To mitigate the uncertainty associated with the model structure and parameters, non-stationary disturbances were accounted for in the EKF by parameter-adaptation. It was demonstrated that the implementation of the EKF along with the dynamic model could improve the accuracy of the fluorescence-based predictions at the sampling instances. Additionally, it was shown that the major advantage of the EKF-based soft-sensor, compared to the standalone fluorescence-based counterpart, was its capability to track the temporal evolution of key process variables between measurement instances obtained by the fluorescence-based soft-sensor. This is crucial for designing control strategies of CHO cell cultures with the aim of guaranteeing product quality., (© 2014 Wiley Periodicals, Inc.)

Selective quantification of co-eluting proteins in chromatography is usually performed by offline analytics. This is time-consuming and can lead to late detection of irregularities in chromatography processes. To overcome this analytical bottleneck, a methodology for selective protein quantification in multicomponent mixtures by means of spectral data and partial least squares regression was presented in two previous studies. In this paper, a powerful integration of software and chromatography hardware will be introduced that enables the applicability of this methodology for a selective inline quantification of co-eluting proteins in chromatography. A specific setup consisting of a conventional liquid chromatography system, a diode array detector, and a software interface to Matlab® was developed. The established tool for selective inline quantification was successfully applied for a peak deconvolution of a co-eluting ternary protein mixture consisting of lysozyme, ribonuclease A, and cytochrome c on SP Sepharose FF. Compared to common offline analytics based on collected fractions, no loss of information regarding the retention volumes and peak flanks was observed. A comparison between the mass balances of both analytical methods showed, that the inline quantification tool can be applied for a rapid determination of pool yields. Finally, the achieved inline peak deconvolution was successfully applied to make product purity-based real-time pooling decisions. This makes the established tool for selective inline quantification a valuable approach for inline monitoring and control of chromatographic purification steps and just in time reaction on process irregularities., (© 2014 Wiley Periodicals, Inc.)

Mass spectrometry has been frequently applied to monitor the O₂ and CO₂ content in the off-gas of animal cell culture fermentations. In contrast to classical mass spectrometry the proton transfer reaction mass spectrometry (PTR-MS) provides additional information of volatile organic compounds by application of a soft ionization technology. Hence, the spectra show less fragments and can more accurately assigned to particular compounds. In order to discriminate between compounds of non-metabolic and metabolic origin cell free experiments and fed-batch cultivations with a recombinant CHO cell line were conducted. As a result, in total eight volatiles showing high relevance to individual cultivation or cultivation conditions could be identified. Among the detected compounds methanethiol, with a mass-to-charge ratio of 49, qualifies as a key candidate in process monitoring due to its strong connectivity to lactate formation. Moreover, the versatile and complex data sets acquired by PTR MS provide a valuable resource for statistical modeling to predict non direct measurable parameters. Hence, partial least square regression was applied to the complete spectra of volatiles measured and important cell culture parameters such as viable cell density estimated (R²  = 0.86). As a whole, the results of this study clearly show that PTR-MS provides a powerful tool to improve bioprocess-monitoring for mammalian cell culture. Thus, specific volatiles emitted by cells and measured online by the PTR-MS and complex variables gained through statistical modeling will contribute to a deeper process understanding in the future and open promising perspectives to bioprocess control., (© 2014 American Institute of Chemical Engineers.)