Near infrared spectroscopy for bioprocess monitoring and control: current status and future trends

Colony identity clues for Syntermes grandis (Blattodea: Termitidae) individuals using near-infrared spectroscopy and PLS-DA approach

Termites are social insects with high species diversity in tropical ecosystems. Multivariate analysis with near-infrared spectroscopy (NIRS) and data interpretation can separate social insects belonging to different colonies of the same species. The objective of this study was to propose the use of discriminant analysis by partial least squares (PLS-DA) combined with NIRS to identify the colonial origin of the Syntermes grandis (Rambur, 1842) (Blattodea: Termitidae) in 2 castes. Six ground S. grandis colonies were identified and mapped; 30 workers and 30 soldier termites in each colony were submitted to spectral measurement with NIRS. PLS-DA applied to the termites' spectral absorbance was used to detect a spectral pattern per S. grandis colony by caste. PLS-DA regression with NIRS proved to be an approach with 99.9% accuracy for identifying the colonial origin of S. grandis workers and 98.3% for soldiers. The methodology showed the importance of qualitatively characterizing the colonial phenotypic response of this species. NIRS is a high-precision approach to identifying the colony origin of S. grandis workers and soldiers. The PLS-DA can be used to design ecological field studies to identify colony territorial competition and foraging behavior of subterranean termite species.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

In Vitro Monitoring of Magnesium-Based Implants Degradation by Surface Analysis and Optical Spectroscopy

Magnesium (Mg)-based degradable alloys have attracted substantial attention for tissue engineering applications due to their biodegradability and potential for avoiding secondary removal surgeries. However, insufficient data in the existing literature regarding Mg’s corrosion and gas formation after implantation have delayed its wide clinical application. Since the surface properties of degradable materials constantly change after contact with body fluid, monitoring the behaviour of Mg in phantoms or buffer solutions could provide some information about its physicochemical surface changes over time. Through surface analysis and spectroscopic analysis, we aimed to investigate the structural and functional properties of degradable disks. Since bubble formation may lead to inflammation and change pH, monitoring components related to acidosis near the cells is essential. To study the bubble formation in cell culture media, we used a newly developed Mg alloy (based on Mg, zinc, and calcium), pure Mg, and commercially available grade 2 Titanium (Ti) disks in Dulbecco’s Modified Eagle Medium (DMEM) solution to observe their behaviour over ten days of immersion. Using surface analysis and the information from near-infrared spectroscopy (NIRS), we concluded on the conditions associated with the medical risks of Mg alloy disintegration. NIRS is used to investigate the degradation behaviour of Mg-based disks in the cell culture media, which is correlated with the surface analysis where possible.

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

Challenges and opportunities for the production of lactic acid bacteria inoculants aimed for ensiling processes

The use of bacterial inoculants for ensiling based on lactic acid bacteria (LAB) to obtain conserved forages has become an alternative for the improvement of milk and meat productivity in cattle, specifically by optimizing the nutritional and microbial quality of animal feed. LAB inoculant production involves microbial and technological aspects such as biomass obtention, the use of cocultures, the inclusion of probiotics, the production of antimicrobial peptides, operational methods used in bioreactors, and the formulation of the end product to be commercialized to farmers. This review explores the technical aspects of the manufacture of bacterial inoculants, from the main features desired in LAB for ensiling purposes to the alternatives of the bioprocess involved.

Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production

The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids (SLs) and sodium gluconate (SG) were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 was greater than 0.98, exhibiting a good linear relationship. The root mean square error of prediction shows that the model has high credibility. Through the control of appropriate glucose concentration in SG fermentation as well as glucose and oil concentrations SLs fermentation by NIR model, the titers of SG and SLs were increased to 11.8% and 26.8%, respectively. Although high cost of NIR spectrometer is a key issue for its wide application in an industrial scale. This work provides a basis for the application of NIR spectroscopy in complex fermentation systems.

Potential applications of halophilic microorganisms for biological treatment of industrial process brines contaminated with aromatics

Saline wastewater contaminated with aromatic compounds can be frequently found in various industrial sectors. Those compounds need to be degraded before reuse of wastewater in other process steps or release to the environment. Halophiles have been reported to efficiently degrade aromatics, but their application to treat industrial wastewater is rare. Halophilic processes for industrial wastewater treatment need to satisfy certain requirements: a continuous process mode, low operational expenditures, suitable reactor systems and a monitoring and control strategy. The aim of this review is to provide an overview of halophilic microorganisms, principles of aromatic biodegradation, and sources of saline wastewater containing aromatics and other contaminants. Finally, process examples for halophilic wastewater treatment and potential process monitoring strategies are discussed. To further illustrate the significant potential of halophiles for saline wastewater treatment and to facilitate development of ready-to-implement processes, future research should focus on scale-up and innovative process monitoring and control strategies.

History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance

Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a “one-to-one” representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.

Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa

In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.

Research Progress of Near-Infrared Fluorescence Immunoassay

Near-infrared fluorescence probes (NIFPs) have been widely used in immunoassay, bio-imaging and medical diagnosis. We review the basic principles of near-infrared fluorescence and near-infrared detection technology, and summarize structures, properties and characteristics of NIFPs (i.e., cyanines, xanthenes fluorescent dyes, phthalocyanines, porphyrin derivates, single-walled carbon nanotubes (SWCNTs), quantum dots and rare earth compounds). We next analyze applications of NIFPs in immunoassays, and prospect the application potential of lateral flow assay (LFA) in rapid detection of pathogens. At present, our team intends to establish a new platform that has highly sensitive NIFPs combined with portable and simple immunochromatographic test strips (ICTSs) for rapid detection of food-borne viruses. This will provide technical support for rapid detection on the port.

Real-time monitoring of ethanol production during Pichia stipitis NRRL Y-7124 alcoholic fermentation using transflection near infrared spectroscopy

The application of in situ near-infrared spectroscopy monitoring of xylose metabolizing yeast such as Pichia stipitis for ethanol production with semisynthetic media, applying chemometrics, was investigated. During the process in a bioreactor, biomass, glucose, xylose, ethanol, acetic acid, and glycerol determinations were performed by a transflection probe immersed in the culture broth and connected to a near-infrared process analyzer. Wavelength windows in near-infrared spectra recorded between 800 and 2200 nm were pretreated using Savitzky-Golay smoothing, second derivative and multiplicative scattering correction in order to perform a partial least squares regression and generate the calibration models. These calibration models were tested by external validation (78 samples). Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. Moreover, regressions coefficients (β) and variable influence in the projection plots were used to assess the results. A novelty is the use of β versus VIP dispersion plots to determine which vectors have more influence on the response in order to improve process comprehension and operability. Validated models were used in a real-time monitoring during P. stipitis NRRL Y7124 semisynthetic media fermentations.

Sensors for disposable bioreactors

Modern bioprocess monitoring demands sensors that provide on-line information about the process state. In particular, sensors for monitoring bioprocesses carried out in single-use bioreactors are needed because disposable systems are becoming increasingly important for biotechnological applications. Requirements for the sensors used in these single-use bioreactors are different than those used in classical reusable bioreactors. For example, long lifetime or resistance to steam and cleaning procedures are less crucial factors, while a requirement of sensors for disposable bioreactors is a cost that is reasonable on a per-use basis. Here, we present an overview of current and emerging sensors for single-use bioreactors, organized by the type of interface of the sensor systems to the bioreactor. A major focus is on non-invasive, in-situ sensors that are based on electromagnetic, semiconducting, optical, or ultrasonic measurements. In addition, new technologies like radio-frequency identification sensors or free-floating sensor spheres are presented. Notably, at this time there is no standard interface between single-use bioreactors and the sensors discussed here. In the future, manufacturers should address this shortcoming to promote single-use bioprocess monitoring and control.

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS)

Concentrations of different steroid hormones have been used in cows as a measure of adrenal or gonadal activity and, thus, as indicators of stress or reproductive state. Detecting cortisol and progesterone in cow hair provides a long-term integrative value of retrospective adrenal or gonadal/placental activity, respectively. Current techniques for steroid detection require a hormone-extraction procedure that involves time, several types of equipment, management of reagents, and some assay procedures (which can also be time-consuming and can destroy the samples). In contrast, near-infrared reflectance spectroscopy (NIRS) is a multi-component predictor technique, characterized as rapid, nondestructive for the sample, and reagent-free. However, as a predictor technique, NIRS needs to be calibrated and validated for each matrix, hormone, and species. The main objective of this study was to evaluate the predictive value of the NIRS technique for hair cortisol and progesterone quantification in cows by using specific enzyme immunoassay as a reference method. Hair samples from 52 adult Friesian lactating cows from a commercial dairy farm were used. Reflectance spectra of hair samples were determined with a NIR reflectance spectrophotometer before and after trimming them. Although similar results were obtained, a slightly better relationship between the reference data and NIRS predicted values was found using trimmed samples. Near infrared reflectance spectroscopy demonstrated its ability to predict cortisol and progesterone concentrations with certain accuracy (R²= 0.90 for cortisol and R²= 0.87 for progesterone). Although NIRS is far from being a complete alternative to current methodologies, the proposed equations can offer screening capability. Considering the advantages of both fields, our results open the possibility for future work on the combination of hair steroid measurement and NIRS methodology.

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein- and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>€35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.

Workflow for multi-analyte bioprocess monitoring demonstrated on inline NIR spectroscopy of P. chrysogenum fermentation

Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate analysis has been applied in bioprocesses for a couple of decades. Nevertheless the papers published in this field are case-specific and do not focus on providing the community generic workflows to conduct experiments, especially as a standard Design of Experiment (DoE) for a multi-analyte process might require overwhelming amount of measurements. In this paper, a workflow for feasibility studies and inline implementation of FT-NIR spectrometer in multi-analyte fermentation processes is presented. The workflow is applied to Penicillium crysogenum fermentation, where the similarities in chemical structures and growth trends between the key analytes together with the aeration and growing fungi make the task challenging: first, the pure analytes are measured off-line with FT-NIR and clustered using principal component analysis. To study the separability of the gained clusters, a DoE approach by spiking is applied. The multivariate modelling of the separable analytes is conducted using the off-line and inline data followed by a comparison of the properties of the different models. Finally, the model output constraints are set by means of outlier diagnostics. As a result, biomass, penicillin (PEN), phenoxyacetic acid (POX), ammonia and biomass were shown to be separable with root mean square error of predictions of 2.62 g/l, 0.34 g/l, 0.51 g/l and 18.3 mM, respectively. Graphical abstract Flowchart illustrating the workflow for feasibility studies and implementation of models for inline monitoring of Ammonia, Biomass, Phenoxyacetic acid and Penicillin.

Monitoring of the cellulosic ethanol fermentation process by near-infrared spectroscopy

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.

Fluorescence spectroscopy and chemometric modeling for bioprocess monitoring

On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables.

Control of specific carbon dioxide production in a fed-batch culture producing recombinant protein using a soft sensor

The feeding of a fed-batch cultivation producing recombinant protein was controlled by a soft sensor set-up. It was assumed that the control approach could be based on the cell's production of carbon dioxide and that this parameter indicates the metabolic state occurring at induced protein expression. The soft sensor used the on-line signals from a carbon dioxide analyser and a near-infrared (NIR) probe for biomass to estimate the specific production rate qCO2. Control experiments were carried out with various qCO2 set-points where we observe that the feeding of nutrients to the culture could easily be controlled and resulted in a decreased variability compared to uncontrolled cultivations. We therefore suggest that this control approach could serve as an alternative to other commonly applied methods such as controlling the cell's overflow metabolism of acetate or the cell's specific growth rate. However, further studies of the internal metabolic fluxes of CO2 during protein expression would be recommended for a more precise characterization of the relationship between qCO2 and protein expression in order to fully interpret the control behaviour.

Challenges in industrial fermentation technology research

Industrial fermentation processes are increasingly popular, and are considered an important technological asset for reducing our dependence on chemicals and products produced from fossil fuels. However, despite their increasing popularity, fermentation processes have not yet reached the same maturity as traditional chemical processes, particularly when it comes to using engineering tools such as mathematical models and optimization techniques. This perspective starts with a brief overview of these engineering tools. However, the main focus is on a description of some of the most important engineering challenges: scaling up and scaling down fermentation processes, the influence of morphology on broth rheology and mass transfer, and establishing novel sensors to measure and control insightful process parameters. The greatest emphasis is on the challenges posed by filamentous fungi, because of their wide applications as cell factories and therefore their relevance in a White Biotechnology context. Computational fluid dynamics (CFD) is introduced as a promising tool that can be used to support the scaling up and scaling down of bioreactors, and for studying mixing and the potential occurrence of gradients in a tank.

Semisynthetic model calibration for monitoring glucose in mammalian cell culture with in situ near infrared spectroscopy

Near infrared (NIR) spectroscopy has the capability of providing real-time, multi-analyte monitoring of the complex reaction mixture associated with cell culture processes. However, the development of robust models to predict the concentration of key analytes has proven difficult. In this study, a modeling methodology using semisynthetic process samples was used to predict glucose concentrations in Chinese Hamster Ovary (CHO) cell culture processes. Partial Least Squares (PLS) regression models were built from in situ NIR spectra, and glucose levels between 4.0 and 14.0 g/L. Two models were constructed. The "standard model" used data provided by cell culture production process samples. The "full model" included the data provided from both cell culture production process samples and semisynthetic samples. The semisynthetic samples were generated by titrating cell culture samples with target viable cell density (VCD) and lactate levels to defined glucose concentrations. The robustness of each model was gauged by predicting glucose in a subsequent cell culture process utilizing a media formulation and cell line not contained in the calibration data sets. The "full model" generated glucose predictions with a root mean square error of prediction (RMSEP) of 0.99 g/L while the "standard model" provided glucose predictions with a RMSEP of 2.26 g/L. The modeling approach utilizing semisynthetic samples proved to be faster development and more effective than using just standard cell culture processes.

The effects of iron oxide incorporation on the chondrogenic potential of three human cell types

Non-invasive monitoring of living cells in vivo provides an important tool in the development of cell-based therapies in cartilage tissue engineering. High-resolution magnetic resonance imaging (MRI) has been used to monitor target cell populations in vivo. However, the side-effects on cell function of the labelling reagents, such as superparamagnetic iron oxide (SPIO), are still unclear. This study investigated the effect of SPIO particles on the chondrogenic differentiation of human bone marrow stromal cells (HBMSCs), neonatal and adult chondrocytes in vitro. Cells were labelled with SPIO for 24 h and chondrogenesis induced in serum-free medium including TGFβ3. For labelled/unlabelled cells, viability, morphology and proliferation were determined using CellTracker™ Green and PicoGreen dsDNA assays. The expression of SOX9, COL2A1 and ACAN was investigated using qRT-PCR after 2, 7 and 14 days. The results showed that viability was unaffected in all of the cells but cell morphology changed towards a 'stretched' phenotype following SPIO uptake. Cell proliferation was reduced only for labelled neonatal chondrocytes. SOX9 and COL2A1 expression decreased at day 2 but not at days 7 and 14 for labelled HBMSCs and adult chondrocytes; ACAN expression was unaffected. In contrast, SOX9 and COL2A1 expression were unaffected in labelled neonatal chondrocytes but a decrease in ACAN expression was seen at day 14. The results suggest that downregulation of chondrogenic genes associated with SPIO labelling is temporary and target cell-dependent. Resovist® can be used to label HBMSCs or mature chondrocytes for MR imaging of cells for cartilage tissue engineering.

Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris

Background

Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles.

Results

This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R², above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements.

Conclusions

The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.

Soft sensors in bioprocessing: a status report and recommendations

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology - Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

Automated measurement and monitoring of bioprocesses: key elements of the M(3)C strategy

The state-of-routine monitoring items established in the bioprocess industry as well as some important state-of-the-art methods are briefly described and the potential pitfalls discussed. Among those are physical and chemical variables such as temperature, pressure, weight, volume, mass and volumetric flow rates, pH, redox potential, gas partial pressures in the liquid and molar fractions in the gas phase, infrared spectral analysis of the liquid phase, and calorimetry over an entire reactor. Classical as well as new optical versions are addressed. Biomass and bio-activity monitoring (as opposed to "measurement") via turbidity, permittivity, in situ microscopy, and fluorescence are critically analyzed. Some new(er) instrumental analytical tools, interfaced to bioprocesses, are explained. Among those are chromatographic methods, mass spectrometry, flow and sequential injection analyses, field flow fractionation, capillary electrophoresis, and flow cytometry. This chapter surveys the principles of monitoring rather than compiling instruments.

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.

Weaver ants Oecophylla smaragdina encounter nasty neighbors rather than dear enemies

The evolution of territorial behavior requires that the benefits of territoriality outweigh the costs. The costs are primarily those of territorial defense against encroaching neighbors or against floaters seeking to establish their own territory. One way to reduce the cost of defense might be to restrict serious conflict to encounters with those posing the greatest threat. Studies of many animals have found that less aggression is shown toward neighbors than toward, strangers, a phenomenon known as the "dear enemy" effect. However, the opposite can also be true, namely, that more aggression is shown toward neighbors than strangers: the "nasty neighbor" effect. This may be particularly true of group-living species that defend a resource-based territory. Here we show that (1) colonies of the weaver ant Oecophylla smaragdina were able to recognize a greater proportion of workers from neighboring colonies as non-colony members; and (2) when recognized as non-colony members, more aggression was exhibited toward neighbors than non-neighbors. We present for the first time evidence that differential levels of aggression involve both a perceptual and behavioral component. On the other hand, we found no evidence that weaver ant workers were better able to recognize workers from previously unknown colonies or responded more aggressively to them, even after a 10-day period of contact. This contrasts with other species in which rapid learning of the identity of new potential enemies has been demonstrated. We suggest that such a response is unnecessary for weaver ants, as encounters with intruders from non-neighboring colonies are probably rare and of little consequence. This study adds to the growing body of evidence that the nasty neighbor effect may be much more common than the dear enemy effect among group-living species.

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l(-1) biomass and 0.8 g l(-1) total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.