Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Real time phase detection based online monitoring of batch fermentation processes

Industrial fermentations conducted in a batch or semi-batch mode demonstrate significant batch-to-batch variability. Current batch process monitoring strategies involve manual interpretation of highly informative but low frequency offline measurements such as concentrations of products, biomass and substrates. Fermentors are also fitted with computer interfaced instrumentation, enabling high frequency online measurements of several variables and automated techniques which can utilize this data would be desirable. Evolution of a batch fermentation, which typically uses complex medium, can be conceptualized as a sequence of several distinct metabolic phases. Monitoring of batch processes can then be achieved by detecting the phase change events, also termed as singular points (SP). In this work, we propose a novel moving window based real-time monitoring strategy for SP detection based only on online measurements. The key hypothesis of the strategy is that the statistical properties of the online data undergo a significant change around an SP. The strategy is easily implementable and does not require past data or prior knowledge of the number or time of occurrence of SPs. The efficacy of the proposed approach has been demonstrated to be superior compared to that of reported techniques for industrially relevant model organisms. The proposed approach can be used to decide offline sampling timings in real time.     

Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass

A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.     

Process Monitoring Strategies for Surface Mount Manufacturing Processes

Establishing reliable surface mount assemblies requires robust design and assembly practices, including stringent process control schemes for achieving high yield processes and high quality solder interconnects. Conventional Shewhart-based process control charts prevalent in today's complex surface mount manufacturing processes are found to be inadequate as a result of autocorrelation, high false alarm probability, and inability to detect process deterioration. Hence, new strategies are needed to circumvent the shortcomings of traditional process control techniques. In this article, the adequacy of Shewhart models in a surface mount manufacturing environment is examined and some alternative solutions and strategies for process monitoring are discussed. For modeling solder paste deposition process data, a time series analysis based on neural network models is highly desirable for both controllability and predictability. In particular, neural networks can be trained to model the autocorrelated time series, learn historical process behavior, and forecast future process performance with low prediction errors. This forecasting ability is especially useful for early detection of solder paste deterioration, so that timely remedial actions can be taken, minimizing the impact on subsequent yields of downstream processes. As for the automated component placement process where very low fraction nonconforming frequently occurs, control-charting schemes based on cumulative counts of conforming items produced prior to detection of nonconforming items is more sensitive in flagging process deterioration. For the reflow soldering and wave-soldering processes, the use of demerit control charts is appealing as it provides not only better control when various defects with a different degree of severity are encountered, but also leads to an improved ARL performance. Illustrative examples of actual process data are presented to demonstrate these approaches.     

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed‐batch fermentations

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical “quality by testing” to rational, knowledge‐based “quality by design” approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real‐time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed‐batch production processes with varying cultivation conditions employing a comprehensive on‐ and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two‐dimensional multi‐wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.     

Real-time dissolved carbon dioxide monitoring I: Application of a novel in situ sensor for CO2 monitoring and control

Dissolved carbon dioxide (dCO₂) is a well-known critical parameter in bioprocesses due to its significant impact on cell metabolism and on product quality attributes. Processes run at small-scale faces many challenges due to limited options for modular sensors for online monitoring and control. Traditional sensors are bulky, costly, and invasive in nature and do not fit in small-scale systems. In this study, we present the implementation of a novel, rate-based technique for real-time monitoring of dCO₂ in bioprocesses. A silicone sampling probe that allows the diffusion of CO₂ through its wall was inserted inside a shake flask/bioreactor and then flushed with air to remove the CO₂ that had diffused into the probe from the culture broth (sensor was calibrated using air as zero-point calibration). The gas inside the probe was then allowed to recirculate through gas-impermeable tubing to a CO₂ monitor. We have shown that by measuring the initial diffusion rate of CO₂ into the sampling probe we were able to determine the partial pressure of the dCO₂ in the culture. This technique can be readily automated, and measurements can be made in minutes. Demonstration experiments conducted with baker's yeast and Yarrowia lipolytica yeast cells in both shake flasks and mini bioreactors showed that it can monitor dCO₂ in real-time. Using the proposed sensor, we successfully implemented a dCO₂-based control scheme, which resulted in significant improvement in process performance.     

Enhanced Cumulative Sum Charts for Monitoring Process Dispersion

The cumulative sum (CUSUM) control chart is widely used in industry for the detection of small and moderate shifts in process location and dispersion. For efficient monitoring of process variability, we present several CUSUM control charts for monitoring changes in standard deviation of a normal process. The newly developed control charts based on well-structured sampling techniques - extreme ranked set sampling, extreme double ranked set sampling and double extreme ranked set sampling, have significantly enhanced CUSUM chart ability to detect a wide range of shifts in process variability. The relative performances of the proposed CUSUM scale charts are evaluated in terms of the average run length (ARL) and standard deviation of run length, for point shift in variability. Moreover, for overall performance, we implore the use of the average ratio ARL and average extra quadratic loss. A comparison of the proposed CUSUM control charts with the classical CUSUM R chart, the classical CUSUM S chart, the fast initial response (FIR) CUSUM R chart, the FIR CUSUM S chart, the ranked set sampling (RSS) based CUSUM R chart and the RSS based CUSUM S chart, among others, are presented. An illustrative example using real dataset is given to demonstrate the practicability of the application of the proposed schemes.     

Toward biotherapeutic product real-time quality monitoring

Biotherapeutics, such as those derived from monoclonal antibodies (mAbs), are industrially produced in controlled multiunit operation bioprocesses. Each unit operation contributes to the final characteristics of the bioproduct. The complexity of the bioprocesses, the cellular machinery, and the bioproduct molecules, typically leads to inherent heterogeneity and variability of the final critical quality attributes (CQAs). In order to improve process control and increase product quality assurance, online and real-time monitoring of product CQAs is most relevant. In this review, the recent advances in CQAs monitoring of biotherapeutic drugs, with emphasis on mAbs, and throughout, the different bioprocess unit operations are reviewed. Recent analytical techniques used for assessment of product-related CQAs of mAbs are considered in light of the analytical speed and ability to measure different CQAs. Furthermore, the state of art modeling approaches for CQA estimation in real-time are presented as a viable alternative for real-time bioproduct CQA monitoring under the process analytical technology and quality-by-design frameworks in the biopharmaceutical industry, which have recently been demonstrated.     

Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision

This article describes the development of Multivariate Statistical Process Control (MSPC) procedures for monitoring batch processes and demonstrates its application with respect to industrial tylosin biosynthesis. Currently, the main fermentation phase is monitored using univariate statistical process control principles implemented within the G2 real-time expert system package. This development addresses integrating various process stages into a monitoring system and observing interactions among individual variables through the use of multivariate projection methods. The benefits of this approach will be discussed from an industrial perspective.     

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno-associated virus production by fluorescence spectroscopy

The process analytical technology (PAT) initiative shifted the bioprocess development mindset towards real-time monitoring and control tools to measure relevant process variables online, and acting accordingly when undesirable deviations occur. Online monitoring is especially important in lytic production systems in which released proteases and changes in cell physiology are likely to affect product quality attributes, as is the case of the insect cell-baculovirus expression vector system (IC-BEVS), a well-established system for production of viral vectors and vaccines. Here, we applied fluorescence spectroscopy as a real-time monitoring tool for recombinant adeno-associated virus (rAAV) production in the IC-BEVS. Fluorescence spectroscopy is simple, yet sensitive and informative. To overcome the strong fluorescence background of the culture medium and improve predictive ability, we combined artificial neural network models with a genetic algorithm-based approach to optimize spectra preprocessing. We obtained predictive models for rAAV titer, cell viability and cell concentration with normalized root mean squared errors of 7%, 4%, and 7%, respectively, for leave-one-batch-out cross-validation. Our approach shows fluorescence spectroscopy allows real-time determination of the best time of harvest to maintain rAAV infectivity, an important quality attribute, and detection of deviations from the golden batch profile. This methodology can be applied to other biopharmaceuticals produced in the IC-BEVS, supporting the use of fluorescence spectroscopy as a versatile PAT tool.     

Side-by-side comparability of batch and continuous downstream for the production of monoclonal antibodies

Continuous processing is the future production method for monoclonal antibodies (mAbs). A fully continuous, fully automated downstream process based on disposable equipment was developed and implemented inside the MoBiDiK pilot plant. However, a study evaluating the comparability between batch and continuous processing based on product quality attributes was not conducted before. The work presented fills this gap comparing both process modes experimentally by purifying the same harvest material (side-by-side comparability). Samples were drawn at different time points and positions in the process for batch and continuous mode. Product quality attributes, product-related impurities, as well as process-related impurities were determined. The resulting polished material was processed to drug substance and further evaluated regarding storage stability and degradation behavior. The in-process control data from the continuous process showed the high degree of accuracy in providing relevant process parameters such as pH, conductivity, and protein concentration during the entire process duration. Minor differences between batch and continuous samples are expected as different processing conditions are unavoidable due to the different nature of batch and continuous processing. All tests revealed no significant differences in the intermediates and comparability in the drug substance between the samples of both process modes. The stability study of the final product also showed no differences in the stability profile during storage and forced degradation. Finally, online data analysis is presented as a powerful tool for online-monitoring of chromatography columns during continuous processing.     

Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique

Water quality indicators can be used to characterize the status and quantify and qualify the change of aquatic ecosystems under different disturbance regimes. Although many studies have been done to develop and assess indicators and discuss interactions among them, few studies have focused on how to improve the predicted indicators and explore their variations in receiving water bodies. Accurate and effective predictions of ecological indictors are critical to better understand changes of water quality in aquatic ecosystems, especially for the real-time forecasting. Process-based water quality models can predict the spatiotemporal variations of the water quality indicators and provide useful information for policy-makers on sound management of water resources. Given their inherent constraints, however, such process models alone cannot actually guarantee perfect results since water quality models generally have a large number of parameters and involve many processes which are too complex to be efficiently calibrated. To overcome these limitations and explore a fast and efficient forecasting method for the change of water quality indictors, we proposed a new framework which combines the process-based models and data assimilation technique. Unlike most traditional approaches in which only the model parameters or initial conditions are updated or corrected and the models are run online, this framework allows the information extracted from observations and outputs of process models to be directly used in a data-driven local/modified local model. The results from the data-driven model are then assimilated into the original process model to further improve its forecasting ability. This approach can be efficiently run offline to directly correct and update the output of water quality models. We applied this framework in a real case study in Singapore. Two of the water quality indicators, namely salinity and oxygen were selected and tested against the observations, suggesting that a good performance of improving the model results and reducing computation time can be obtained. This approach is simple and efficient, especially suitable for real-time forecasting systems. Thus, it can enhance forecasting of water quality indictors and thereby facilitate the effective management of water resources.     

Identifying Sources of Variation in Sheet Metal Stamping

Manufacturers using traditional process control charts to monitor their sheet metal stamping processes often encounter out-of-control signals indicating that the process mean has changed. Unfortunately, a sheet metal stamping process does not have the necessary adjustability in its process variable input settings to allow easily correcting the mean response in an out-of-control condition. Hence the signals often go ignored. Accordingly, manufacturers are unaware of how much these changes in the mean inflate the variance in the process output. We suggest using a designed experiment to quantify the variation in stamped panels attributable to changing means. Specifically, we suggest classifying stamping variation into three components: part-to-part, batch-to-batch, and within batch variation. The part-to-part variation represents the short run variability about a given stable or trending batch mean. The batch-to-batch variation represents the variability of the individual batch mean between die setups. The within batch variation represents any movement of the process mean during a given batch run. Using a two-factor nested analysis of variance model, a manufacturer may estimate the three components of variation. After partitioning the variation, the manufacturer may identify appropriate countermeasures in a variation reduction plan. In addition, identifying the part-to-part or short run variation allows the manufacturer to predict the potential process capability and the inherent variation of the process given a stable mean. We demonstrate the methodology using a case study of an automotive body side panel.     

Dynamics of single cell property distributions in Chinese hamster ovary cell cultures monitored and controlled with automated flow cytometry

Two important variables that are often not measured online in Chinese hamster ovary (CHO) cell cultures are cell number concentration and culture viability. We have developed an automated flow cytometry system that measured the cell number concentration, single cell viability based on propidium iodide (PI) exclusion, and single cell light scattering from bioreactor samples every 30 min. The bioreactor was monitored during batch growth, and then the cell number concentration was controlled at a set point during cytostat operation. NH₄Cl was added during steady state operation in cytostat mode to monitor the transient cell population response to adverse growth conditions. The automated measurements correlated well to cell concentration and viability determined manually using a hemacytometer. The described system provides a method to study mammalian cell culture physiology and dynamics in great detail. It presents a new method for the monitoring and control of animal cell culture.     

Microfluidic biolector—microfluidic bioprocess control in microtiter plates

In industrial‐scale biotechnological processes, the active control of the pH‐value combined with the controlled feeding of substrate solutions (fed‐batch) is the standard strategy to cultivate both prokaryotic and eukaryotic cells. On the contrary, for small‐scale cultivations, much simpler batch experiments with no process control are performed. This lack of process control often hinders researchers to scale‐up and scale‐down fermentation experiments, because the microbial metabolism and thereby the growth and production kinetics drastically changes depending on the cultivation strategy applied. While small‐scale batches are typically performed highly parallel and in high throughput, large‐scale cultivations demand sophisticated equipment for process control which is in most cases costly and difficult to handle. Currently, there is no technical system on the market that realizes simple process control in high throughput. The novel concept of a microfermentation system described in this work combines a fiber‐optic online‐monitoring device for microtiter plates (MTPs)—the BioLector technology—together with microfluidic control of cultivation processes in volumes below 1 mL. In the microfluidic chip, a micropump is integrated to realize distinct substrate flow rates during fed‐batch cultivation in microscale. Hence, a cultivation system with several distinct advantages could be established: (1) high information output on a microscale; (2) many experiments can be performed in parallel and be automated using MTPs; (3) this system is user‐friendly and can easily be transferred to a disposable single‐use system. This article elucidates this new concept and illustrates applications in fermentations of Escherichia coli under pH‐controlled and fed‐batch conditions in shaken MTPs. Biotechnol. Bioeng. 2010;107: 497–505. © 2010 Wiley Periodicals, Inc.     

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.     

Process analytical technology implementation for protein refolding: GCSF as a case study

Process analytical technology is gaining interest in the biopharmaceutical industry as a means to enable consistency in processing and thereby in product quality via process control. Protein refolding is known to be significantly impacted by critical process parameters and feed material attributes including composition and pH of the solubilisation and refolding buffers. Hence, to achieve robust process control and product quality, these attributes and parameters need to be monitored. This paper presents an approach towards statistical process control and monitoring of protein refolding, from buffer preparation to refold quenching, during manufacturing of therapeutic proteins from Escherichia coli based systems. The proposed approach utilises measurements of online redox potential, temperature, and pH for development of a statistical model. The model has then been integrated with LabView to permit real-time monitoring of the refolding process. The proposed system has been demonstrated to successfully identify process deviations and thereby enable process control for manufacturing product of consistent quality.     

Supervisory Control System for Monitoring a Pharmaceutical Hot Melt Extrusion Process

Continuous pharmaceutical manufacturing processes are of increased industrial interest and require uni- and multivariate Process Analytical Technology (PAT) data from different unit operations to be aligned and explored within the Quality by Design (QbD) context. Real-time pharmaceutical process verification is accomplished by monitoring univariate (temperature, pressure, etc.) and multivariate (spectra, images, etc.) process parameters and quality attributes, to provide an accurate state estimation of the process, required for advanced control strategies. This paper describes the development and use of such tools for a continuous hot melt extrusion (HME) process, monitored with generic sensors and a near-infrared (NIR) spectrometer in real-time, using SIPAT (Siemens platform to collect, display, and extract process information) and additional components developed as needed. The IT architecture of such a monitoring procedure based on uni- and multivariate sensor systems and their integration in SIPAT is shown. SIPAT aligned spectra from the extrudate (in the die section) with univariate measurements (screw speed, barrel temperatures, material pressure, etc.). A multivariate supervisory quality control strategy was developed for the process to monitor the hot melt extrusion process on the basis of principal component analysis (PCA) of the NIR spectra. Monitoring the first principal component and the time-aligned reference feed rate enables the determination of the residence time in real-time.     

三级综合医院护理质量实时监控方法探讨

传统的护理质量管理以终末质量控制为主,简单的事后检查和评比,缺乏可靠性和科学性,难以保证护理质量。文章通过对护理质量关键环节监控内容的分析,建立护理质量实时监控指标体系,并探讨了护理过程质量实时监控的方法,包括组件库、规则库的构建,监控信息实时获取,以及护理过程质量实时监测与预警。