Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media

Chip-scale sensor for spectroscopic metrology

Miniaturized spectrometers hold great promise for in situ, in vitro, and even in vivo sensing applications. However, their size reduction imposes vital performance constraints in meeting the rigorous demands of spectroscopy, including fine resolution, high accuracy, and ultra-wide observation window. The prevailing view in the community holds that miniaturized spectrometers are most suitable for coarse identification of signature peaks. Here, we present an integrated reconstructive spectrometer that enables near-infrared (NIR) spectroscopic metrology, and demonstrate a fully packaged sensor with auxiliary electronics. Such a sensor operates over a 520 nm bandwidth together with a resolution below 8 pm, yielding a record-breaking bandwidth-to-resolution ratio of over 65,000. The classification of different types of solid substances and the concentration measurement of aqueous and organic solutions are performed, all achieving approximately 100% accuracy. Notably, the detection limit of our sensor matches that of commercial benchtop counterparts, which is as low as 0.1% (i.e. 100 mg/dL) for identifying the concentration of glucose solution.

The Convergence of FTIR and EVs: Emergence Strategy for Non-Invasive Cancer Markers Discovery

In conjunction with imaging analysis, pathology-based assessments of biopsied tissue are the gold standard for diagnosing solid tumors. However, the disadvantages of tissue biopsies, such as being invasive, time-consuming, and labor-intensive, have urged the development of an alternate method, liquid biopsy, that involves sampling and clinical assessment of various bodily fluids for cancer diagnosis. Meanwhile, extracellular vesicles (EVs) are circulating biomarkers that carry molecular profiles of their cell or tissue origins and have emerged as one of the most promising biomarkers for cancer. Owing to the biological information that can be obtained through EVs' membrane surface markers and their cargo loaded with biomolecules such as nucleic acids, proteins, and lipids, EVs have become useful in cancer diagnosis and therapeutic applications. Fourier-transform infrared spectroscopy (FTIR) allows rapid, non-destructive, label-free molecular profiling of EVs with minimal sample preparation. Since the heterogeneity of EV subpopulations may result in complicated FTIR spectra that are highly diverse, computational-assisted FTIR spectroscopy is employed in many studies to provide fingerprint spectra of malignant and non-malignant samples, allowing classification with high accuracy, specificity, and sensitivity. In view of this, FTIR-EV approach carries a great potential in cancer detection. The progression of FTIR-based biomarker identification in EV research, the rationale of the integration of a computationally assisted approach, along with the challenges of clinical translation are the focus of this review.

Advances in cost-effective integrated spectrometers

The proliferation of Internet-of-Things has promoted a wide variety of emerging applications that require compact, lightweight, and low-cost optical spectrometers. While substantial progresses have been made in the miniaturization of spectrometers, most of them are with a major focus on the technical side but tend to feature a lower technology readiness level for manufacturability. More importantly, in spite of the advancement in miniaturized spectrometers, their performance and the metrics of real-life applications have seldomly been connected but are highly important. This review paper shows the market trend for chip-scale spectrometers and analyzes the key metrics that are required to adopt miniaturized spectrometers in real-life applications. Recent progress addressing the challenges of miniaturization of spectrometers is summarized, paying a special attention to the CMOS-compatible fabrication platform that shows a clear pathway to massive production. Insights for ways forward are also presented.

Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIP-Boruta

Regression models are constructed to predict glucose and lactate concentrations from near-infrared spectra in culture media. The partial least-squares (PLS) regression technique is employed, and we investigate the improvement in the predictive ability of PLS models that can be achieved using wavelength selection and transfer learning. We combine Boruta, a nonlinear variable selection method based on random forests, with variable importance in projection (VIP) in PLS to produce the proposed variable selection method, VIP-Boruta. Furthermore, focusing on the situation where both culture medium samples and pseudo-culture medium samples can be used, we transfer pseudo media to culture media. Data analysis with an actual dataset of culture media and pseudo media confirms that VIP-Boruta can effectively select appropriate wavelengths and improves the prediction ability of PLS models, and that transfer learning with pseudo media enhances the predictive ability. The proposed method could reduce the prediction errors by about 61% for glucose and about 16% for lactate, compared to the traditional PLS model.

Taking the pulse of bioprocesses: at-line and in-line monitoring of mammalian cell cultures

Real-time and near real-time monitoring of cell culture processes are critical to the evolving process analytical technology (PAT) paradigm for upstream bioprocessing. The responses measured from these analytical instruments can enable rapid feedback to perturbations that can otherwise lead to batch failures. Historically, real-time monitoring of bioreactor processes has been relegated to parameters such as pH, dissolved oxygen, and temperature. Other analytical results, such as cell growth and metabolites, are provided through manual daily sampling. In order to reduce sample error and increase throughput, real-time and near real-time instruments have been developed. Here we discuss recent advances in these technologies. This article aims to focus on other developing at-line and in-line technologies that enable monitoring of bioreactor processes, including dielectric spectroscopy, NIR, off-gas spectrometry, integrated at-line HPLC, and nanofluidic devices for monitoring cell growth and health, metabolites, titer, and product quality.

Faster, reduced cost calibration method development methods for the analysis of fermentation product using near-infrared spectroscopy (NIRS)

Recent innovations in synthetic biology, fermentation, and process development have decreased time to market by reducing strain construction cycle time and effort. Faster analytical methods are required to keep pace with these innovations, but current methods of measuring fermentation titers often involve manual intervention and are slow, time-consuming, and difficult to scale. Spectroscopic methods like near-infrared (NIR) spectroscopy address this shortcoming; however, NIR methods require calibration model development that is often costly and time-consuming. Here, we introduce two approaches that speed up calibration model development. First, generalized calibration modeling (GCM) or sibling modeling, which reduces calibration modeling time and cost by up to 50% by reducing the number of samples required. Instead of constructing analyte-specific models, GCM combines a reduced number of spectra from several individual analytes to produce a large pool of spectra for a generalized model predicting all analyte levels. Second, randomized multicomponent multivariate modeling (RMMM) reduces modeling time by mixing multiple analytes into one sample matrix and then taking the spectral measurements. Afterward, individual calibration methods are developed for the various components in the mixture. Time saved from the use of RMMM is proportional to the number of components or analytes in the mixture. When combined, the two methods effectively reduce the associated cost and time for calibration model development by a factor of 10.

Preliminary Study on the Identification of BRAFV600E Mutation in Colorectal Cancer by Near-Infrared Spectroscopy

Introduction

In metastatic colorectal cancer (mCRC), the B-type Raf kinase (BRAF)^V600E mutation is a molecular biomarker of poor prognosis and is of great importance to drug target. Currently, the commonly used methods for detecting BRAF^V600E mutation include immunohistochemistry (IHC) and gene sequencing, but both present certain limitations. Near-infrared (NIR) spectroscopy is a spectroscopy technology that takes advantage of the electromagnetic wavelength between visible light and mid-infrared light.

Methods

IHC was used to detect the expression of BRAF^V600E protein with the BRAF^V600E (VE1) antibody in 42 cases of paraffin-embedded (FFPE) mCRC tissue sections. The NIR-discriminant analysis model (NIRS-DA) was established using 6 cases of wild-type and 6 cases of mutant-type BRAF specimens.

Results

IHC detection results revealed 13 cases of weakly positive (+), 1 case of moderately positive (++), and 28 cases of negative (-) CRC. Compared with the next-generation sequencing (NGS) results, the positive rate was 66.7%. The classification accuracy of calibration (CAC) was 100% compared with the results of NGS, demonstrating that the BRAF^V600E mutant NIRS-DA model, verified by 2 cases of wild-type and 2 cases of mutant-type CRC samples was established. The NIRS-DA model was used to predict gene mutation in the CRC samples, 7 cases were positive (+), and 35 cases were negative (-), and the classification accuracy of prediction (CAP) was 83.3% (35/42).

Discussion

The NIRS-DA model-predicted results were in high agreement with the detection results of NGS, and the difference in IHC is not statistically significant (P>0.05). However, this study is a preliminary discussion on a methodology due to its small sample size.

Near-Infrared Spectroscopy in Bio-Applications

Near-infrared (NIR) spectroscopy occupies a specific spot across the field of bioscience and related disciplines. Its characteristics and application potential differs from infrared (IR) or Raman spectroscopy. This vibrational spectroscopy technique elucidates molecular information from the examined sample by measuring absorption bands resulting from overtones and combination excitations. Recent decades brought significant progress in the instrumentation (e.g., miniaturized spectrometers) and spectral analysis methods (e.g., spectral image processing and analysis, quantum chemical calculation of NIR spectra), which made notable impact on its applicability. This review aims to present NIR spectroscopy as a matured technique, yet with great potential for further advances in several directions throughout broadly understood bio-applications. Its practical value is critically assessed and compared with competing techniques. Attention is given to link the bio-application potential of NIR spectroscopy with its fundamental characteristics and principal features of NIR spectra.

On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development

NIR spectroscopy is a non-destructive tool for in-situ, on-line bioprocess monitoring. One of its most frequent applications is the determination of metabolites during cultivation, especially glucose. Previous studies have usually investigated the applicability of Near Infrared (NIR) spectroscopy at one bioreactor scale but the effect of scale up was not explored. In this study, the complete scale up from shake flask (1 L) through 20 L, 100 L and 1000 L up to 5000 L bioreactor volume level was monitored with on-line NIR spectroscopy. The differences between runs and scales were examined using principal component analysis. The bioreactor runs were relatively similar regardless of scales but the shake flasks differed strongly from bioreactor runs. The glucose concentration throughout five 5000 L scale bioreactor runs were predicted by partial least squares regression models that were based on pre-processed spectra of bioreactor runs and combinations of them. The model that produced the lowest error of prediction (4.18 mM on a 29 mM concentration range) for all five runs in the prediction set was based on the combination of 20 L and 100 L data. This result demonstrated the capabilities and the limitations of an NIR system for glucose monitoring in mammalian cell cultivations.

Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics

Two of the primary issues with characterizing the variability of raw materials used in mammalian cell culture, such as wheat hydrolysate, is that the analyses of these materials can be time consuming, and the results of the analyses are not straightforward to interpret. To solve these issues, spectroscopy can be combined with chemometrics to provide a quick, robust and easy to understand methodology for the characterization of raw materials; which will improve cell culture performance by providing an assessment of the impact that a given raw material will have on final product quality. In this study, four spectroscopic technologies: near infrared spectroscopy, middle infrared spectroscopy, Raman spectroscopy, and fluorescence spectroscopy were used in conjunction with principal component analysis to characterize the variability of wheat hydrolysates, and to provide evidence that the classification of good and bad lots of raw material is possible. Then, the same spectroscopic platforms are combined with partial least squares regressions to quantitatively predict two cell culture critical quality attributes (CQA): integrated viable cell density and IgG titer. The results showed that near infrared (NIR) spectroscopy and fluorescence spectroscopy are capable of characterizing the wheat hydrolysate's chemical structure, with NIR performing slightly better; and that they can be used to estimate the raw materials' impact on the CQAs. These results were justified by demonstrating that of all the components present in the wheat hydrolysates, six amino acids: arginine, glycine, phenylalanine, tyrosine, isoleucine and threonine; and five trace elements: copper, phosphorus, molybdenum, arsenic and aluminum, had a large, statistically significant effect on the CQAs, and that NIR and fluorescence spectroscopy performed the best for characterizing the important amino acids. It was also found that the trace elements of interest were not characterized well by any of the spectral technologies used; however, the trace elements were also shown to have a less significant effect on the CQAs than the amino acids. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers, 33:1127-1138, 2017.

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.

Coupled turbidity and spectroscopy problems: a simple algorithm for the volumetric analysis of optically thin or dilute two-phase systems

We report an algorithm for measuring the phase volume fraction and solute concentration of a two-phase system, applicable to either optically thin or optically dilute spatially homogeneous systems. Probing light is directed into the sample, and the elastically scattered light (EE) is collected as one signal and the inelastically scattered light (IE) collected as another signal. The IE can be pure fluorescence or Raman or an unresolved combination of the two. As the IE and the EE are produced by fundamentally different processes, they are independent. The algorithm, derived from radiation transfer theory, shows that phase volume and concentration are linear functions of the EE and IE. The parameters are derived from a training set. We present examples of how the algorithm performs when the assumption of spatial homogeneity is violated and when light-induced photochemistry causes changes in the IE. Although this is a generally valid algorithm with many potential applications, its use is discussed briefly in the context of blood and tissue analysis since the algorithm was originally designed for noninvasive in vivo probing of human skin.

Configuration of bioreactors

Lab-scale stirred-tank bioreactors (0.2-20 l) are used for fundamental research on animal cells and in process development and troubleshooting for large-scale production. In this chapter, different configurations of bioreactor systems are shortly discussed and setting up these different configurations is described. In addition, online measurement and control of bioreactor parameters is described, with special attention to controller settings (PID) and online measurement of oxygen consumption and carbon dioxide production. Finally, methods for determining the oxygen transfer coefficient are described.

Using experimental data designs and multivariate modeling to assess the effect of glycated serum protein concentration on glucose prediction from near-infrared spectra of human serum

Near-infrared (NIR) spectra of human blood serum consist of overlapping strong absorption bands of water and serum proteins, which affect the ability of multivariate calibration models to predict glucose. Furthermore, serum proteins such as albumin and globulins undergo a glycation reaction by forming covalent bonds with freely available glucose molecules in the serum. In diabetic individuals with poor glucose control, more and more serum protein molecules react with glucose, resulting in a high glycated protein concentration. The glucose molecules covalently bonded to serum proteins might contribute to the overall glucose signal acquired by NIR spectroscopy. This might affect the prediction ability of multivariate calibration models such as partial least squares regression (PLSR). In this study, we investigated the effect of total protein concentration and the glycated protein concentration in blood serum on the prediction ability of PLSR calibration models. Serum samples were subjected to ultra-filtration, and the PLSR model was built using NIR spectra of filtered serum solutions. Prediction performance was found to improve by 39-42% in absence of serum protein molecules. Various experimental data set designs were generated by carefully varying the glycated serum protein concentration in calibration and test sets of PLSR models. This investigation revealed that the impact of varying glycated protein concentration on the root mean square error of prediction was not drastic. To test the statistical significance of the prediction results, a multiple linear regression model was built. The glycated serum protein concentration was found to be statistically insignificant (p = 0.86) in predicting glucose concentration. Overall, it was concluded that the glycated serum proteins do not affect the glucose prediction accuracy of PLSR models using NIR spectra of human serum.

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.

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.

Matrix effects during monitoring of antibody and host cell proteins using attenuated total reflection spectroscopy

Production of recombinant proteins, e.g. antibodies, requires constant real-time monitoring to optimize yield and quality attributes and to respond to changing production conditions, such as host cell protein (HCP) titers. To date, this monitoring of mammalian cell culture-based processes is done using laborious and time consuming enzyme-linked immunosorbent assays (ELISA), two-dimensional sodium dodecylsulphate polyacrylamide gel electrophoresis, and chromatography-based systems. Measurements are usually performed off-line, requiring regular sample withdrawal associated with increased contamination risk. As information is obtained retrospectively, the reaction time to adapt to process changes is too long, leading to lower yield and higher costs. To address the resulting demand for continuous online-monitoring systems, we present a feasibility study using attenuated total reflection spectroscopy (ATR) to monitor mAb and HCP levels of NS0 cell culture in situ, taking matrix effects into account. Fifty-six NS0 cell culture samples were treated with polyelectrolytes for semi-selective protein precipitation. Additionally, part of the samples was subjected to filtration prior to analysis, to change the background matrix and evaluate effects on chemometric quantification models. General models to quantify HCP and mAb in both filtered and unfiltered matrix showed lower prediction accuracy compared to models designed for a specific matrix. HCP quantification in the range of 2,000-55,000 ng mL(-1) using specific models was accurate for most samples, with results within the accepted limit of an ELISA assay. In contrast, mAb prediction was less accurate, predicting mAb in the range of 0.2-1.7 g L(-1) . As some samples deviated substantially from reference values, further investigations elucidating the suitability of ATR for monitoring are required.

Low-cost optical lifetime assisted ratiometric glutamine sensor based on glutamine binding protein

Here we report a reagentless fluorescence sensing technique for glutamine in the submicromolar range based on the glutamine binding protein (QBP). The S179C mutant is labeled with the short-lived acrylodan (lifetime<5ns) and the long-lived tris(dibenzoylmethane) mono(5-amino-1,10-phenanthroline)europium(III) (lifetime > 300 micros) at the -SH and the N-terminal positions, respectively. In the presence of glutamine the fluorescence of acrylodan is quenched, while the fluorescence of europium complex remains constant. In this report we describe an innovative technique, the so called lifetime assisted ratiometric sensing to discriminate the two fluorescence signals using minimal optics and power requirements. This method exploits the large difference between the fluorescence lifetimes of the two fluorophores to isolate the individual fluorescence from each other by alternating the modulation frequency of the excitation light between 300 Hz and 10 kHz. The result is a ratiometric optical method that does not require expensive and highly attenuating band pass filters for each of the dyes, but only one long pass filter for both. Thus, the signal to noise ratio is enhanced, and at the same time, the optical setup is simplified. The end product is a simple sensing device suitable for low-cost applications such as point-of-care diagnostics or in-the-field analysis.

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories. This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications.

Analysis of ultraviolet absorption spectrum of Chinese herbal medicine-Cortex Fraxini by double ANN

A fast, accurate and convenient method for the simultaneous determination of multi-component in the Chinese herbal medicine was proposed by using ultraviolet absorption spectrum. In this method, dummy components were added to training sample, and a double artificial neural network (DANN) that has the function of high self-revision and self-simulation was used. Effect of other interference components could be eliminated by adjusting concentration of dummy components. Therefore, the accuracy of concentration prediction for multi-component in the complicated Chinese herbal medicine was improved. It has been realized that two effective components of Cortex Fraxini, aesculin and aesculetin, were simultaneously determined, without any separation. The predicted accuracy was 92% within the permitted relative errors. The measurement precisions of the aesculin and aesculetin were 0.37% and 1.5%, respectively.

Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture

Cellular physiology and metabolism were monitored using a quasi real-time combination of on-line and off-line data to estimate metabolic fluxes in an established bioreaction network. The utility of this approach towards optimizing bioreactor operation was demonstrated for CHO cells cultivated in 15 L perfusion reactors at 20 x 10(6) cells/mL. Medium composition and dilution rates were changed to obtain several steady states with varying glucose and glutamine concentrations. When cells were restored to initial culture medium and perfusion rate conditions after being exposed to lower glucose and glutamine concentrations, the pyruvate flux into the TCA cycle was increased 30% while the pyruvate flux through lactate was decreased 30%, suggesting steady-state multiplicity. By appropriately altering cellular metabolism, perfusion bioreactors can operate at lower perfusion rates without significant accumulation of inhibitory metabolites such as lactate. Changes in glucose, lactate and glutamine uptake/production rates had significant effects on the calculation of other fluxes in the network. Sensitivity analysis of these key metabolic fluxes highlighted the need for accurate and reliable real-time sensors. Overall, rapid observation of metabolic fluxes can be a valuable tool for bioprocess development, monitoring and control. The framework presented in this study offers a convenient means for quasi real-time estimation of metabolic fluxes and represents a step towards realizing the potential of metabolic flux analysis for accelerated bioprocess optimization.

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

The development of Near Infrared Spectroscopy has paralleled that of the PC, and the application of NIR in many industries has undergone explosive growth in recent years. This has been particularly apparent in the area of microbial and cell culture system monitoring and control. Potentially, NIR offers the prospect of real-time control of the physiology of cultured cells in fermenters, leading to marked improvements in authenticity, purity and production efficiency. Despite this, NIR is not yet as widely applied within the bioprocessing industry as its potential might suggest. This review critically evaluates the development of this rapidly moving area as it pertains to microbial and cell culture system control and highlights the critical stages in the development of the technology. It indicates the work that must still be carried out if the full potential of NIR is to be exploited in making proteins, hormones and antibiotics by the fermentation route. The review comes at a particularly timely moment when NIR stands on the threshold of widespread acceptance in bioprocessing. This is the ideal moment to assess what the technology can offer the microbiologist, and where it may develop in the future.

Control of yeast fed-batch process through regulation of extracellular ethanol concentration

At high growth rates, the biomass yield of baker's yeast (Saccharomyces cerevisiae) decreases due to the production of ethanol. For this reason, it is standard industrial practice to use a fed-batch process whereby the specific growth rate, mu, is fixed at a level below the point of ethanol production, i.e., mucrit. Optimally, growth should be maintained at mucrit, but in practice, this is difficult because mucrit is dependent upon strain and culture conditions. In this work, growth was maintained at a point just above mucrit by regulating ethanol concentration in the bioreactor. The models used for control design are shown, as are the experimental results obtained when this strategy was implemented. This technique should be applicable to all microorganisms that exhibit an "overflow" type metabolism.

Monitoring and control of Gluconacetobacter xylinus fed-batch cultures using in situ mid-IR spectroscopy

A partial least-squares calibration model, relating mid-infrared spectral features with fructose, ethanol, acetate, gluconacetan, phosphate and ammonium concentrations has been designed to monitor and control cultivations of Gluconacetobacter xylinus and production of gluconacetan, a food grade exopolysaccharide (EPS). Only synthetic solutions containing a mixture of the major components of culture media have been used to calibrate the spectrometer. A factorial design has been applied to determine the composition and concentration in the calibration matrix. This approach guarantees a complete and intelligent scan of the calibration space using only 55 standards. This calibration model allowed standard errors of validation (SEV) for fructose, ethanol, acetate, gluconacetan, ammonium and phosphate concentrations of 1.16 g/l, 0.36 g/l, 0.22 g/l, 1.54 g/l, 0.24 g/l and 0.18 g/l, respectively. With G. xylinus, ethanol is directly oxidized to acetate, which is subsequently metabolized to form biomass. However, residual ethanol in the culture medium prevents bacterial growth. On-line spectroscopic data were implemented in a closed-loop control strategy for fed-batch fermentation. Acetate concentration was controlled at a constant value by feeding ethanol into the bioreactor. The designed fed-batch process allowed biomass production on ethanol. This was not possible in a batch process due to ethanol inhibition of bacterial growth. In this way, the productivity of gluconacetan was increased from 1.8 x 10(-3) [C-mol/C-mol substrate/h] in the batch process to 2.9 x 10(-3) [C-mol/C-mol substrate/h] in the fed-batch process described in this study.