Assessment of In-Line Near-Infrared Spectroscopy for Continuous Monitoring of Fermentation Processes

Development and Validation of Near-Infrared Methods for the Quantitation of Caffeine, Epigallocatechin-3-gallate, and Moisture in Green Tea Production

The quality of tea leaves (e.g., their color, appearance, and taste) can be directly influenced by the tea production process, which is closely connected with the content of a number of chemical components formed during the production of the tea leaves. However, the production process is now controlled by people's experience, making its quality significantly different. NIRS is a time-saving, cost-saving, and nondestructive method. Therefore, it is necessary to introduce NIRS technology into the quality control of the tea production process. In this study, a quantitative analysis model of caffeine, epigallocatechin-3-gallate (EGCG), and moisture content was established by near-infrared spectroscopy (NIRS) which was united simultaneously with partial least squares (PLSR) for online process monitoring of tea production. The model parameters show that the established model has fine robustness and outstanding measuring accuracy. Then, the feasibility of the established method is verified by the traditional method. Through the verification of the precision of the instrument and the stability of the sample, it is clarified that the model can be further utilized to monitor tea product quality online in a productive process.

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.

Surface nanodroplet-based nanoextraction from sub-milliliter volumes of dense suspensions

A greener analytical technique for quantifying compounds in dense suspensions is needed for wastewater and environmental analysis, chemical or bio-conversion process monitoring, biomedical diagnostics, and food quality control, among others. In this work, we introduce a green, fast, one-step method called nanoextraction for extraction and detection of target analytes from sub-milliliter dense suspensions using surface nanodroplets without toxic solvents and pre-removal of the solid contents. With nanoextraction, we achieve a limit of detection (LOD) of 10-9 M for a fluorescent model analyte obtained from a particle suspension sample. The LOD is lower than that in water without particles (10-8 M), potentially due to the interaction of particles and the analyte. The high particle concentration in the suspension sample, thus, does not reduce the extraction efficiency, although the extraction process was slowed down up to 5 min. As a proof of principle, we demonstrate the nanoextraction for the quantification of model compounds in wastewater slurry containing 30 wt% solids and oily components (i.e. heavy oils). The nanoextraction and detection technology developed in this work may be used in fast analytical technologies for complex slurry samples in the environment, industrial waste, or in biomedical diagnostics.

Soft-sensor development for monitoring the lysine fermentation process

Monitoring cell growth and target production in working fermentors is important for stabilizing high level production. In this study, we developed a novel soft sensor for estimating the concentration of a target product (lysine), substrate (sucrose), and bacterial cell in commercially working fermentors using machine learning combined with available on-line process data. The lysine concentration was accurately estimated in both linear and nonlinear models; however, the nonlinear models were also suitable for estimating the concentrations of sucrose and bacterial cells. Data enhancement by time interpolation improved the model prediction accuracy and eliminated unnecessary fluctuations. Furthermore, the soft sensor developed based on the dataset of the same process parameters in multiple fermentor tanks successfully estimated the fermentation behavior of each tank. Machine learning-based soft sensors may represent a novel monitoring system for digital transformation in the field of biotechnological processes.

Developing global regression models for metabolite concentration prediction regardless of cell line

Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysis

Escherichia coli is one of the most used host microorganism for the production of recombinant products, such as heterologous proteins and plasmids. However, genetic, physiological and environmental factors influence the plasmid replication and cloned gene expression in a highly complex way. To control and optimize the recombinant expression system performance, it is very important to understand this complexity. Therefore, the development of rapid, highly sensitive and economic analytical methodologies, which enable the simultaneous characterization of the heterologous product synthesis and physiologic cell behavior under a variety of culture conditions, is highly desirable. For that, the metabolic profile of recombinant E. coli cultures producing the pVAX-lacZ plasmid model was analyzed by rapid, economic and high-throughput Fourier Transform Mid-Infrared (FT-MIR) spectroscopy. The main goal of the present work is to show as the simultaneous multivariate data analysis by principal component analysis (PCA) and direct spectral analysis could represent a very interesting tool to monitor E. coli culture processes and acquire relevant information according to current quality regulatory guidelines. While PCA allowed capturing the energetic metabolic state of the cell, e.g. by identifying different C-sources consumption phases, direct FT-MIR spectral analysis allowed obtaining valuable biochemical and metabolic information along the cell culture, e.g. lipids, RNA, protein synthesis and turnover metabolism. The information achieved by spectral multivariate data and direct spectral analyses complement each other and may contribute to understand the complex interrelationships between the recombinant cell metabolism and the bioprocess environment towards more economic and robust processes design according to Quality by Design framework. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:285-298, 2017.

An approach combining real-time release testing with near-infrared spectroscopy to improve quality control efficiency of Rhizoma paridis

Raw material examination is a critical process in the industrial production of traditional Chinese medicine (TCM); high accuracy and minimal time consumption are both required. In this study, near-infrared (NIR) spectroscopy was applied to improve the quality control efficiency of Rhizoma paridis. Partial least squares regression (PLSR) was first used to develop quantitative calibration models, and the discriminant analysis model was established to qualitatively discriminate the qualified samples from the unqualified samples. These two established NIR models were applied for real-time release testing (RTRT) of R. paridis. R. paridis saponins (RPS)≥0.6% and moisture ≤12% were used as the quantitative releasing criteria of RTRT according to the Chinese Pharmacopoeia. Qualified samples classified by the discriminant analysis model were deemed to meet the qualitative releasing criterion of RTRT. Using the established quantitative model, 24 samples were allowed to be released to the subsequent production processes with 100% accuracy. For the qualitative RTRT analysis, three samples were misclassified as the unqualified class and were released unsuccessfully, the accuracy of the qualitative RTRT was 90%. Therefore, the quantitative RTRT was more feasible for actual manufacturing processes. Based on this study, a rapid and effective quantitative NIR spectroscopic method was proposed for the RTRT of R. paridis. The combination of RTRT and NIR spectroscopy could be a potential tool to improve the quality control efficiency of R. paridis.

Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

Applications of Infrared and Raman Spectroscopies to Probiotic Investigation

In this review, we overview the most important contributions of vibrational spectroscopy based techniques in the study of probiotics and lactic acid bacteria. First, we briefly introduce the fundamentals of these techniques, together with the main multivariate analytical tools used for spectral interpretation. Then, four main groups of applications are reported: (a) bacterial taxonomy (Subsection 4.1); (b) bacterial preservation (Subsection 4.2); (c) monitoring processes involving lactic acid bacteria and probiotics (Subsection 4.3); (d) imaging-based applications (Subsection 4.4). A final conclusion, underlying the potentialities of these techniques, is presented.

In situ near-infrared (NIR) versus high-throughput mid-infrared (MIR) spectroscopy to monitor biopharmaceutical production

The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coli cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R(2)) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.

Galacto-oligosaccharides and lactulose as protectants against desiccation of Lactobacillus delbrueckii subsp. bulcaricus

Lactobacillus delbrueckii subsp. bulgaricus CIDCA 333 was dehydrated on desiccators containing silica gel in the presence of 20% w/w of two types of galacto-oligosaccharides (GOS Biotempo and GOS Cup Oligo H-70®) and lactulose, until no changes in water desorption were detected. After rehydration, bacterial growth was monitored at 37°C by determining: (a) the absorbance at 600 nm and (b) the near infrared spectra (NIR). Principal component analysis (PCA) was then performed on the NIR spectra of samples dehydrated in all conditions. A multiparametric flow cytometry assay was carried out using carboxyfluorescein diacetate and propidium iodide probes to determine the relative composition of damaged, viable, and dead bacteria throughout the growth kinetics. The absorbance at 600 nm and the position of the second derivative band at ∼1370 nm were plotted against the time of incubation. The efficiency of the protectants was GOS Biotempo > GOS Cup Oligo H-70®  > lactulose. The better protectant capacity of GOS Biotempo was explained on the basis of the lower contribution of damaged cells immediately after rehydration (t = 0). PCA showed three groups along PC1, corresponding to the lag, exponential and stationary phases of growth, which explained 99% of the total variance. Along PC2, two groups were observed, corresponding to damaged or viable cells. The results obtained support the use of NIR to monitor the recovery of desiccated microorganisms in real time and without the need of chemical reagents. The use of GOS and lactulose as protectants in dehydration/rehydration processes was also supported.

Combined shifted-excitation Raman difference spectroscopy and support vector regression for monitoring the algal production of complex polysaccharides

The applicability of shifted-excitation Raman difference spectroscopy (SERDS) in combination with signal regression analysis as an alternative and non-invasive approach for monitoring the cultivation of phototrophic microorganisms producing complex molecules of pharmaceutical relevance in a bioreactor is demonstrated. As a model system, the cultivation of the red unicellular algae Porphyridium purpureum is used for focusing on the segregation of sulfated exopolysaccharides (EPS) which exhibit antiviral activity. The spectroscopic results obtained by partial linear least squares regression (PLSR) and by nonlinear support vector regression (SVR) are discussed against the corresponding results from the reference analytics based on the phenol-sulfuric acid assay. The SERDS-approach turns out to have strong potential as a non-invasive tool for online-monitoring of biotechnological 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.

In-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz based on qualitative and quantitative uses of near-infrared spectroscopy

The application of near-infrared (NIR) spectroscopy for in-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz was investigated. For NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm pathlength flow cell were utilized to collect spectra in real-time. High performance liquid chromatography (HPLC) was used as a reference method to determine scutellarein in extract solution. Partial least squares regression (PLSR) calibration model of Savitzky-Golay smoothing NIR spectra in the 5450-10,000 cm(-1) region gave satisfactory predictive results for scutellarein. The results showed that the correlation coefficients of calibration and cross validation were 0.9967 and 0.9811, respectively, and the root mean square error of calibration and cross validation were 0.044 and 0.105, respectively. Furthermore, both the moving block standard deviation (MBSD) method and conformity test were used to identify the end point of extraction process, providing real-time data and instant feedback about the extraction course. The results obtained in this study indicated that the NIR spectroscopy technique provides an efficient and environmentally friendly approach for fast determination of scutellarein and end point control of extraction process.

Near infrared and acoustic chemometrics monitoring of volatile fatty acids and dry matter during co-digestion of manure and maize silage

In this study, two process analytical technologies, near infrared spectroscopy and acoustic chemometrics, were investigated as means of monitoring a maize silage spiked biogas process. A reactor recirculation loop which enables sampling concomitant with on-line near infrared characterisation was applied. Near infrared models resulted in multivariate models for total and volatile solids with ratio of standard error of performance to standard deviation (RPD) values of 5 and 5.1, indicating good on-line monitoring prospects. The volatile fatty acid models had slopes between 0.83 and 0.92 (good accuracy) and RPD between 2.8 and 3.6 (acceptable precision). A second experiment employed at-line monitoring with both near infrared spectroscopy and acoustic chemometrics. A larger calibration span was obtained for total solids by spiking. Both process analytical modalities were validated with respect to the total solids prediction. The near infrared model had an RPD equal to 5.7, while the acoustic chemometrics model resulted in a RPD of 2.6.

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting-PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.

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.

Future aspects of bioprocess monitoring

Nature has the impressive ability to efficiently and precisely control biological processes by applying highly evolved principles and using minimal space and relatively simple building blocks. The challenge is to transfer these principles into technically applicable and precise analytical systems that can be used for many applications. This article summarizes some of the new approaches in sensor technology and control strategies for different bioprocesses such as fermentations, biotransformations, and downstream processes. It focuses on bio- and chemosensors, optical sensors, DNA and protein chip technology, software sensors, and modern aspects of data evaluation for improved process monitoring and control.

A practical method to calibrate self-reported adherence to antiretroviral therapy

OBJECTIVE

Self-report of antiretroviral medications adherence is inexpensive and simple to use in clinical settings but grossly overestimates adherence. We investigated methods to calibrate patients' self-reported adherence to match objectively measured adherence more closely for the purpose of developing a practical and more accurate self-reported adherence measure.

DESIGN

Longitudinal cohort design.

METHODS

Using data from 2 prospective longitudinal clinical investigations conducted at 5 HIV clinics, we examined the discrepancy between self-reported adherence and objectively measured adherence. We evaluated the relation between attitudinal measures and the degree of discrepancy and used a cross-validation approach to propose candidate items to improve adherence survey methodology.

RESULTS

Among 330 patients, self-reported adherence was consistently higher than objectively measured adherence. The best calibration models included the patient's self-reported adherence, duration of the antiretroviral regimen, and attitudinal measures (ability to take medication as instructed, believing medication can help one to live longer, whether or not it is too troublesome to take antiretrovirals, and feeling things are going the right way).

CONCLUSION

The method efficiently identified survey items to improve self-reported adherence measurement. The calibrated measure more closely approximates objectively measured adherence and is more sensitive for detecting nonadherence. These models merit evaluation in other settings.

Chemometric analysis of in-line multi-wavelength fluorescence measurements obtained during cultivations with a lipase producingAspergillus oryzae strain

The filamentous fungus, Aspergillus oryzae, was cultivated in batch and fed-batch cultivations in order to investigate the use of multi-wavelength fluorescence for monitoring course of events during filamentous fungi cultivations. The A. oryzae strain applied expressed a fungal lipase from Thermomyces lanuginosus. Spectra of multi-wavelength fluorescence were collected every 5 min with the BioView system (DELTA, Denmark) and both explorative and predictive models, correlating the fluorescence data with cell mass and lipase activity, were built. During the cultivations, A. oryzae displayed dispersed hyphal growth and under these conditions no fouling of the multi-wavelength fluorescence sensor was observed. The scores of a parallel factor analysis (PARAFAC) model, based on the fluorescence spectra, gave clear evidence of, for example, the on-set of the feeding phase. The predictive models, estimating the cell mass, showed correlations between 0.73 and 0.97 with root mean square error of cross validation (RMSECV) values between 1.48 and 0.77 g . kg(-1). A model estimating the lipase activity was also constructed for the fed-batch cultivations with a correlation of 0.93. The results presented here clearly show that multi-wavelength fluorescence is a useful tool for monitoring fed-batch cultivations of filamentous fungi.

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.

Determination of cell mass and polymyxin using multi-wavelength fluorescence

Multi-wavelength fluorescence was applied for on-line monitoring of cell mass and the antibiotic polymyxin B in Bacillus polymyxa cultivations. By varying the phosphate and nitrogen content of the medium different polymyxin-cell mass ratios could be obtained. Using this strategy, it was possible to investigate if multi-wavelength fluorescence is able to give independent prediction of the two parameters. Partial least square (PLS) regression was applied to establish mathematical relationships between off-line determined cell mass and polymyxin concentrations and on-line collected fluorescence data. For polymyxin one universal PLS model, with a correlation of 0.95 and a root mean square error of cross validation (RMSECV) of 35 mgl(-1), could be constructed. However, correlation between fluorescence and cell mass dry weight could not be established including data from all three types of cultivations. For data from each type of cultivation, separate models with high correlation and low RMSECV values were built. A large variation in cellular composition as a result of the different levels of nitrogen and phosphorus in the cultivations was the probable reason to the necessity of building three models. The results of the present investigation indicate that production of polymyxin is concomitantly regulated by phosphate and nitrogen as the highest polymyxin yield on cell mass, 0.17+/-0.01 gg(-1), was reached in the cultivations where both nitrogen and phosphate concentrations were kept low.

Near-infrared spectroscopic monitoring of biomass, glucose, ethanol and protein content in a high cell density baker's yeast fed-batch bioprocess

The use of at-line NIRS to monitor a high cell density fed-batch baker's yeast bioprocess was investigated. Quantification of the key analytes (biomass, ethanol and glucose) and the product quality indicator (percentage protein content) was studied. Biomass was quantitatively modelled using whole matrix samples (as was percentage protein content). The dominance of the whole matrix spectrum by biomass, and its associated light scattering effects, were overcome by use of filtrate samples and adapted (semi-synthetic) filtrate samples, which allowed successful ethanol and glucose modelling, respectively. Calibrations were rigorously challenged via external validation with large sample sets relative to the calibration sample size, ensuring model robustness and potential practical utility. The standard errors of calibration for biomass, glucose, ethanol and total intracellular protein were (g/l) 1.79, 0.19, 0.79 and 0.91, respectively, comparable to those of the primary assays. The calibration strategies necessary to generate quantitative models for this range of analytes in such a complex high cell density bioprocess fluid are discussed.

Sensor combination and chemometric modelling for improved process monitoring in recombinant E. coli fed-batch cultivations

The key objective for the optimisation of recombinant protein production in bacteria is to optimize the exploitation of the host cell's synthesis potential. Recent studies show that the novel concept of transcription rate control allows the tuning of recombinant gene expression in relation to the metabolic capacity of the host cell. To adjust the inducer-biomass ratio to a tolerable level, real-time knowledge about key process variables is paramount. Since there are no reliable online-sensors for key variables such as biomass or recombinant product, it is necessary to relate available online signals to process variables by mathematical models. To improve chemometric modelling of process variables, dielectric spectroscopy and a multi-wavelength online fluorescence sensor for two-dimensional fluorescence spectroscopy were applied in a series of recombinant Escherichia coli fed-batch cultivations applying two different process operation states. Dielectric spectroscopy signals were closely correlated to biomass, while two-dimensional fluorescence spectroscopy allowed the monitoring of fluorescent biogenic components. Chemometric modelling of key process variables with two different modelling techniques showed that this sensor combination greatly improved the estimation (i.e. reduce error magnitude) of process variables in recombinant E. coli cultivations, thereby enhancing process monitoring capabilities.