Temporally segmented modelling: a route to improved bioprocess monitoring using near infrared spectroscopy?

Near infrared spectroscopy (NIRS) was used to monitor an industrial bioprocess for the production of the antibiotic, tylosin, using a segmented modelling approach. Models were built over the entire time course of the fermentation from 0 to 150 h, and also in two distinct phases or segments of the bioprocess from 50 to 100 h (synthetic phase) and from 100 to 150 h (stationary phase). All models were validated externally and the performance of the full range and segmented models compared. The standard error of prediction (SEP) of the segmented models was less in both 50–100 h and 100–150 h and the correlation highest in the 50–100 h range. This would suggest that data segmentation is potentially a useful method of accommodating the impact of the pronounced matrix changes which occur in some bioprocesses in NIRS models for key analytes. While there are many reports on bioprocess monitoring using NIRS, there have been no previous studies on the use of segmented NIR models within a bioprocess as a means of accommodating matrix change.     

At-line monitoring of a submerged filamentous bacterial cultivation using near-infrared spectroscopy

The use of near infra-red spectroscopy (NIRS) to monitor a submerged filamentous bacterial bioprocess was investigated. An industrial strain of the filamentous bacterium Streptomyces fradiae was cultured in a 12 litre stirred tank reactor (STR) using a complex medium. This mycelial 4 phase (oil, water, gas and solid) system produced highly complex and variable matrices, therefore monitoring such a complex fluid with NIRS represented a considerable challenge. Nevertheless, successful models for four key analytes (methyl oleate, glucose, glutamate and ammonium) were built at-line (rapid off-line) using NIRS. In the present study, the methods used to formulate, select and validate the models for the key analytes are discussed, with particular emphasis on how the model performance can be critically evaluated. Since previous reports on NIRS in monitoring bioprocesses have either involved simpler matrices, or, in filamentous systems, have not discussed how NIRS models can be critically assessed, the emphasis in the present study on providing an insight into the modelling process in such a complex matrix, may be particularly important to the applicability of NIRS to such industrial bioprocesses.     

Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy

The use of near infrared spectroscopy (NIRS) was investigated in the context of an efficient high cell density fed-batch industrial Pichia pastoris bioprocess for the production of a therapeutic mammalian protein. This process represented a considerable challenge from the viewpoint of using NIRS to model key analytes because it involved two carbon sources (glycerol and methanol) added at differing rates and times, used a chemically complex medium, and showed a change in liquid phase behaviour due to cell growth. Models for biomass, glycerol, methanol and product were constructed. Different methods of spectral collection and mathematical procedures were used relative to which analyte in the fermentation matrix was being modelled and the rationale behind the model building is clearly described. Regardless of the mode of spectral collection it was essential to consider the changes in modelled analyte concentration relative to changes in other spectral contributors (analytes). The study considerably extends the use of NIRS in fermentation processes to high cell density complex industrial production processes, and comments on how this further developments the technology towards routine in situ NIRS monitoring of bioprocesses.     

Monitoring a recombinant Pichia pastoris fed batch process using Fourier transform mid-infrared spectroscopy (FT-MIRS)

The use of Fourier transform mid-infrared spectroscopy (FT-MIRS) to predict the concentrations of key analytes in fed-batch cultivations of an industrial strain of Pichia pastoris in a chemical complex medium was investigated. Models for glycerol, methanol (substrates), and product (an heterologous protein) were built, and evaluated. The use of a multi-bounce attenuated total reflectance (HATR) accessory aided spectral acquisition in optically dense samples. Generally, all models were robust and performed well on external validation, using data from processes not present in the original modelling exercise. Substrate models lacked the complexity of some previous IR models, and the models performed adequately even at low analyte concentration (<1 g l^–1). Thus, simultaneous, rapid monitoring of low concentrations of multiple analytes in a complex bioprocess matrix with little or no sample pre-treatment is achievable using ATR FT-MIRS.     

Assessment of near-infrared spectral information for rapid monitoring of bioprocess quality.

Access to real-time process information is desirable for consistent and efficient operation of bioprocesses. Near-infrared spectroscopy (NIRS) is known to have potential for providing real-time information on the quantitative levels of important bioprocess variables. However, given the fact that a typical NIR spectrum encompasses information regarding almost all the constituents of the sample matrix, there are few case studies that have investigated the spectral details for applications in bioprocess quality assessment or qualitative bioprocess monitoring. Such information would be invaluable in providing operator-level assistance on the progress of a bioprocess in industrial-scale productions. We investigated this aspect and report the results of our investigation. Near-infrared spectral information derived from scanning unprocessed culture fluid (broth) samples from a complex antibiotic production process was assessed for a data set that incorporated bioprocess variations. Principal component analysis was applied to the spectral data and the loadings and scores of the principal components studied. Changes in the spectral information that corresponded to variations in the bioprocess could be deciphered. Despite the complexity of the matrix, near-infrared spectra of the culture broth are shown to have valuable information that can be deconvoluted with the help of factor analysis techniques such as principal component analysis (PCA). Although complex to interpret, the loadings and score plots are shown to offer potential in process diagnosis that could be of value in the rapid assessment of process quality, and in data assessment prior to quantitative model development.     

Development of a perfusion fed bioreactor for embryonic stem cell-derived cardiomyocyte generation: oxygen-mediated enhancement of cardiomyocyte output

Cell transplantation is emerging as a promising new approach to replace scarred, nonfunctional myocardium in a diseased heart. At present, however, generating the numbers of donor cardiomyocytes required to develop and test animal models is a major limitation. Embryonic stem (ES) cells may be a promising source for therapeutic applications, potentially providing sufficient numbers of functionally relevant cells for transplantation into a variety of organs. We developed a single-step bioprocess for ES cell-derived cardiomyocyte production that enables both medium perfusion and direct monitoring and control of dissolved oxygen. Implementation of the bioprocess required combining methods to prevent ES cell aggregation (hydrogel encapsulation) and to purify for cardiomyocytes from the heterogeneous cell populations (genetic selection), with medium perfusion in a controlled bioreactor environment. We used this bioprocess to investigate the effects of oxygen on cardiomyocyte generation. Parallel vessels (250 mL culture volume) were run under normoxic (20% oxygen tension) or hypoxic (4% oxygen tension) conditions. After 14 days of differentiation (including 5 days of selection), the cardiomyocyte yield per input ES cell achieved in hypoxic vessels was 3.77 +/- 0.13, higher than has previously been reported. We have developed a bioprocess that improves the efficiency of ES cell-derived cardiomyocyte production, and allows the investigation of bioprocess parameters on ES cell-derived cardiomyogenesis. Using this system we have demonstrated that medium oxygen tension is a culture parameter that can be manipulated to improve cardiomyocyte yield.     

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.     

Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Assessment of belowground interactions in mixed forests has been largely constrained by the ability to distinguish fine roots of different species. Here, we explored near infrared reflectance spectroscopy (NIRS) to predict the proportion of woody fine roots in mixed samples and analyzed whether the prediction quality of NIRS models is related to the complexity of the fine-root mixture. For model calibration and validation purposes, 11 series of artificial mixed species samples containing known amounts of fine roots of up to four temperate tree species and non-woody plants were prepared. Three types of models with different calibration/validation approaches were developed and tested against external independent data for additional validation. With these models the proportion of each species in root mixtures was predicted accurately with low standard error of prediction (RMSECV/RMSEP <6.5%) and high coefficient of determination (r²?>?0.93) for all fine-root mixtures. In addition, NIRS models also provided satisfactory estimates for samples with low (<15%) or no content of particular components. The predictive power of the NIRS models did not decrease substantially with increasing complexity of the root samples. The approach presented here is a promising alternative to hand sorting of fine roots, which may be influenced substantially by operator variation, and it will facilitate investigating belowground interactions between woody species.     

Real-time analyte monitoring of a fungal fermentation, at pilot scale, using in situ mid-infrared spectroscopy

Robust in situ biochemical monitoring is essential for the development of substrate feed control to optimize fermentation processes. The scale up of the fermentation for the fungus Glarea lozoyensis can benefit from such technology to improve the yield of the pharmaceutically important pneumocandin of interest and control the levels of unwanted analogues. A new in situ probe, using a diamond attenuated total reflection element, was evaluated at pilot scale for the quantitative measurement of fermentation analytes using Fourier transform mid-IR spectrometry. The new technology was shown to be stable, unaffected by reactor operation conditions of agitation, airflow, and backpressure, but sensitive to temperature control. Both glucose and phosphate were simultaneously monitored during a seed fermentation at 280 L pilot scale using complex medium with detection to 0.1 g/L for both analytes. Fructose, glutamate, and proline were monitored at 75 L scale using production media with detection limits of 0.1, 0.5, and 0.5 g/L respectively. Partial least squares calibration/prediction models were created for analytes of interest using off-line reference measurements and specific spectral regions. Good fits were obtained between off-line measurements and those predicted by in situ mid-IR. Standard errors of prediction (SEP) for glucose (range 18-0.1 g/L) and phosphate (range 11-7.5 g/L) were 0.16 and 1.8 g/L respectively with mean percentage errors (MPEs) around 2.5%. SEP values for the production process: fructose (range 20-0.1 g/L), glutamate (8-0.5 g/L), and proline (12-0.5 g/L) were 0.44, 0.6, and 0.5 g/L respectively with MPEs of 2.2, 5.3, and 10.1%. The technology effectively demonstrates quantitative multicomponent analysis of fermentation processes using in situ monitoring.     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation

The use of in-situ near infrared spectroscopy (NIRS) as a tool for monitoring four key analytes in a CHO-K1 animal cell culture was investigated. Previous work using on-line NIRS to monitor bioprocesses has involved its application ex-situ where the analyzer is physically outside the fermentor, or to microbial bioprocesses. This novel application of NIRS to monitor analytes within an animal cell culture using a steam sterilizable in-situ fiber optic probe is very important for furthering the use of NIRS within the bioprocessing industry. The method of calibration used to develop the models involved the use of large data sets so that all likely variation in stoichiometry was incorporated within the models. Successful models for glucose, lactate, glutamine, and ammonia were built with Standard Error of Predictions (SEP's) of 0.072 (g/L), 0.0144 (g/L), 0.308 (mM), and 0.036 (mM), respectively of the total concentration range.     

Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization

Modern bioprocess control requires fast data acquisition and in-time evaluation of bioprocess variables. On-line fluorescence spectroscopy and the application of chemometric methods accomplish these goals. In order to demonstrate how time-consuming off-line analysis methods can be replaced for bioprocess monitoring, fluorescence measurements were performed during different cultivations of the fungus Claviceps purpurea. To predict process variables like biomass, protein, and alkaloid concentrations, chemometric models were developed on the basis of the acquired fluorescence spectra. The results of these investigations are presented and the applicability of this approach for bioprocess monitoring is discussed.     

Use of at-line and in-situ near-infrared spectroscopy to monitor biomass in an industrial fed-batch Escherichia coli process

One of the key goals in bioprocess monitoring is to achieve real-time knowledge of conditions within the bioreactor, i.e., in-situ. Near-infrared spectroscopy (NIRS), with its ability to carry out multi-analyte quantification rapidly with little sample presentation, is potentially applicable in this role. In the present study, the application of NIRS to a complex, fed-batch industrial E. coli (RV308/PHKY531) process was investigated. This process undergoes a series of temperature changes and is vigorously agitated and aerated. These conditions can pose added challenges to in-situ NIRS. Using the measurement of a key analyte (biomass) as an illustration, the details of the relationship between the at-line and in-situ use of NIRS are considered from the viewpoint of both theory and practical application. This study shows that NIRS can be used both at-line and in-situ in order to achieve good predictive models for biomass. There are particular challenges imposed by in-situ operation (loss of wavelength regions and noise) which meant the need for signal optimisation studies. This showed that whilst the at-line modelling process may provide some useful information for the in-situ process, there were distinct differences. This study shows that the in-situ use of NIRS in a highly challenging matrix (similar to those encountered in current industrial practice) is possible, and thus extends previous works in the area.     

Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

Adequate biogeochemical characterization and monitoring of aquatic ecosystems, both for scientific purposes and for water management, pose high demands on spatial and temporal replication of chemical analyses. Near-infrared reflectance spectroscopy (NIRS) may offer a rapid, low-cost and reproducible alternative to standard analytical sample processing (digestion or extraction) and measuring techniques used for the chemical characterization of aquatic sediments. We analyzed a total of 191 sediment samples for total and NaCl-extractable concentrations of Al, Ca, Fe, K, Mg, Mn, N, Na, P, S, Si, and Zn as well as oxalate- extractable concentrations of Al, Fe, Mn and P. Based on the NIR spectral data and the reference values, calibration models for the prediction of element concentrations in unknown samples were developed and tested with an external validation procedure. Except Mn, all prediction models of total element concentrations were found to be acceptable to excellent (ratio of performance deviation: RPD 1.8–3.1). For extractable element fractions, viable model precision could be achieved for NaCl-extractable Ca, K, Mg, NH₄ ⁺-N, S and Si (RPD 1.7–2.2) and oxalate-extractable Al, Fe and P (RPD 1.9–2.3). For those elements that showed maximum total values below 3 g kg⁻¹ prediction models were found to become increasingly critical (RPD <2.0). Low concentrations also limited the performance of NIRS calibrations for extracted elements, with critical concentration thresholds <0.1 g kg⁻¹ and 3.3 g kg⁻¹ for NaCl and oxalate extractions, respectively. Thus, reliable NIRS measurements of trace metals are restricted to sediments with high metal content. Nevertheless, we demonstrated the suitability of NIRS measurements to determine a large array of chemical properties of aquatic sediments. The results indicate great potential of this fast technique as an analytical tool to better understand the large spatial and temporal variation of sediment characteristics in an economically viable way.     

Intelligent bioprocessing for haemotopoietic cell cultures using monitoring and design of experiments

The need for successful ex-vivo expansion and directed differentiation of haematopoietic stem cells (HSCs) for therapeutic applications has increased over the past decade. Haematopoietic cell cultures are complex and full characterisation of the process environment has yet to be achieved. The complexity and transient nature of HSC cultures make the identification, maintenance and control of optimal operating conditions challenging. Application of real-time, on-line monitoring techniques and process control strategies enhances the ability to operate bioprocesses of desired reproducibility and high product quality. In this review, we discussed the methods by which in vitro culture information necessary for bioprocess control may be obtained, including process considerations, monitoring and analytical tools, and design of experiments (DOE). The successful application of these tools may result in time- and cost-effective cultures for directed differentiation and expansion of haematopoietic components intended for clinical use.     

Rapid analysis of sugars in fruit juices by FT-NIR spectroscopy.

A simple analytical procedure using FT-NIR and multivariate techniques for the rapid determination of individual sugars in fruit juices was evaluated. Different NIR detection devices and sample preparation methods were tested by using model solutions to determine their analytical performance. Aqueous solutions of sugar mixtures (glucose, fructose, and sucrose; 0-8% w/v) were used to develop a calibration model. Direct measurements were made by transflection using a reflectance accessory, by transmittance using a 0.5-mm cell, and by reflectance using a fiberglass paper filter. FT-NIR spectral data were transformed to the second derivative. Partial least-squares regression (PLSR) was used to create calibration models that were cross-validated (leave-one-out approach). The prediction ability of the models was evaluated on fruit juices and compared with HPLC and standard enzymatic techniques. The PLSR loading spectra showed characteristic absorption bands for the different sugars. Models generated from transmittance spectra gave the best performance with standard error of prediction (SEP) <0.10% and R(2) of 99.9% that accurately and precisely predicted the sugar levels in juices, whereas lower precision was obtained with models generated from reflectance spectra. FT-NIR spectroscopy allowed for the rapid ( approximately 3 min analysis time), accurate and non-destructive analysis of sugars in juices and could be applied in quality control of beverages or to monitor for adulteration or contamination.     

Rapid estimation of single cell oil content of solid-state fermented mass using near-infrared spectroscopy

Calibration model using near-infrared reflectance spectroscopy (NIRS) for estimation of SCO content in solid-state fermented mass was established. The NIRS calibration model was derived by partial least-squares (PLS) regression and prediction of SCO contents of independent solid-state fermented mass samples fermented by different oleaginous fungi showed the model to be rapid and accurate, giving R(2)-value higher than 0.9552 and root mean standard error of prediction (RMSEP) value lower than 0.5772%. The established NIRS calibration model could be used to estimate the SCO contents of the solid-state fermented masses and will provide much convenience to the research of SCO production in solid-state fermentation.     

Advanced near‐infrared monitor for stable real‐time measurement and control of Pichia pastoris bioprocesses

Near‐infrared spectroscopy is considered to be one of the most promising spectroscopic techniques for upstream bioprocess monitoring and control. Traditionally the nature of near‐infrared spectroscopy has demanded multivariate calibration models to relate spectral variance to analyte concentrations. The resulting analytical measurements have proven unreliable for the measurement of metabolic substrates for bioprocess batches performed outside the calibration process. This paper presents results of an innovative near‐infrared spectroscopic monitor designed to follow the concentrations of glycerol and methanol, as well as biomass, in real time and continuously during the production of a monoclonal antibody by a Pichia pastoris high cell density process. A solid state instrumental design overcomes the ruggedness limitations of conventional interferometer‐based spectrometers. Accurate monitoring of glycerol, methanol, and biomass is demonstrated over 274 days postcalibration. In addition, the first example of feedback control to maintain constant methanol concentrations, as low as 1 g/L, is presented. Postcalibration measurements over a 9‐month period illustrate a level of reliability and robustness that promises its adoption for online bioprocess monitoring throughout product development, from early laboratory research and development to pilot and manufacturing scale operation. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:749–759, 2014     

On-line infrared spectroscopy for bioprocess monitoring

One of the major aims of bioprocess engineering is the real-time monitoring of important process variables. This is the basis of precise process control and is essential for high productivity as well as the exact documentation of the overall production process. Infrared spectroscopy is a powerful analytical technique to analyze a wide variety of organic compounds. Thus, infrared sensors are ideal instruments for bioprocess monitoring. The sensors are non-invasive, have no time delay due to sensor response times, and have no influence on the bioprocess itself. No sampling is necessary, and several components can be analyzed simultaneously. In general, the direct monitoring of substrates, products, metabolites, as well as the biomass itself is possible. In this review article, insights are provided into the different applications of infrared spectroscopy for bioprocess monitoring and the complex data interpretation. Different analytical techniques are presented as well as example applications in different areas.     

Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations

Supervision of batch bioprocess operations in real-time during the progress of a batch run offers many advantages over end-of-batch quality control. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide an efficient modeling and supervision framework. A new type of MPLS modeling technique that is especially suitable for online real-time process monitoring and the multivariate monitoring charts are presented. This online process monitoring technique is also extended to include predictions of end-of-batch quality measurements during the progress of a batch run. Process monitoring, quality estimation and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling. The integrated RTKBS and the implementation of MPLS-based process monitoring and quality control are illustrated using a fed-batch penicillin production benchmark process simulator.