Evaluating online sampling probes for substrate concentration and protein production by a Design of Experiments screening approach

A polypropylene sampling probe and ceramic sampling probe were tested for the online measurement of substrate and protein concentrations in fed batch cultivations of a recombinant Pichia pastoris strain overexpressing the enzyme HRP. Although small substrate molecules could be determined precisely under process conditions, online and offline data for enzyme activity and protein content showed offsets for both sampling probes. An easy‐to‐do multivariate Design of Experiments (DoE) screening approach revealed the limitations for both sampling probes. Online and offline determined data for enzymatic activity were fitted to a suitable exponential equation. A direct correlation of these equations showed a linear relation between online and offline data for the polypropylene probe and a quadratic relation for the ceramic probe. Using the resulting formulas, observed offsets could be compensated for by mathematical transformation, which consequently could allow the use of online calculator tools to determine the enzymatic activity and the protein content in quasi real‐time. This study demonstrates the usefulness of a DoE approach to evaluate online sampling probes in a short time and introduces a strategy to obtain data with two different online sampling probes also for large target molecules despite observed offsets.     

Multivariate statistical monitoring as applied to clean‐in‐place (CIP) and steam‐in‐place (SIP) operations in biopharmaceutical manufacturing

Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning‐in‐place (CIP) and steaming‐in‐place (SIP, also known as sterilization‐in‐place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real‐time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:505–515, 2014     

Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: a case study with rifamycin B fermentation

Industrial production of antibiotics, biopharmaceuticals and enzymes is typically carried out via a batch or fed-batch fermentation process. These processes go through various phases based on sequential substrate uptake, growth and product formation, which require monitoring due to the potential batch-to-batch variability. The phase shifts can be identified directly by measuring the concentrations of substrates and products or by morphological examinations under microscope. However, such measurements are cumbersome to obtain. We present a method to identify phase transitions in batch fermentation using readily available online measurements. Our approach is based on Dynamic Principal Component Analysis (DPCA), a multivariate statistical approach that can model the dynamics of non-stationary processes. Phase-transitions in fermentation produce distinct patterns in the DPCA scores, which can be identified as singular points. We illustrate the application of the method to detect transitions such as the onset of exponential growth phase, substrate exhaustion and substrate switching for rifamycin B fermentation batches. Further, we analyze the loading vectors of DPCA model to illustrate the mechanism by which the statistical model accounts for process dynamics. The approach can be readily applied to other industrially important processes and may have implications in online monitoring of fermentation batches in a production facility.     

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.     

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.     

Online analysis of microendoscopic 1-photon calcium imaging data streams

In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop.     

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called ‘particle’. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.     

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

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

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

Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.     

Fault detection and diagnosis in an industrial fed-batch cell culture process

A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of fault detection and diagnosis. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process faults.     

基于傅里叶变换近红外光谱实时分析1,3-丙二醇发酵过程生物量的在线监测方法

生物量是反映生物发酵过程进展的重要参数,对生物量进行实时监测可用于对发酵过程的调控优化。为克服目前主要采用的离线方法检测生物量时间滞后和人工测量误差较大等缺点,本研究针对1,3-丙二醇发酵过程设计了一个基于傅里叶变换近红外光谱实时分析技术的生物量在线监测实验平台,通过对实时采集光谱预处理以及敏感光谱段分析,应用偏最小二乘算法,建立了1,3-丙二醇发酵过程生物量变化的动态预测模型。以底物甘油浓度为60 g/L和40 g/L的发酵过程作为外部验证实验,分析得到模型的预测均方根误差分别为0.341 6和0.274 3,结果表明所建立的模型具有较好的实时预测能力,能够实现对1,3-丙二醇发酵过程中生物量的有效在线监测。     

Dynamic optimization of hybridoma growth in a fed-batch bioreactor

This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process-model mismatch at each sampling time, updated the model parameters, and re-optimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single offline optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization online increased yield of cell mass per batch by 44% over the single offline optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture.     

Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis

On-line monitoring of penicillin cultivation processes is crucial to the safe production of high-quality products. In the past, multiway principal component analysis (MPCA), a multivariate projection method, has been widely used to monitor batch and fed-batch processes. However, when MPCA is used for on-line batch monitoring, the future behavior of each new batch must be inferred up to the end of the batch operation at each time and the batch lengths must be equalized. This represents a major shortcoming because predicting the future observations without considering the dynamic relationships may distort the data information, leading to false alarms. In this paper, a new statistical batch monitoring approach based on variable-wise unfolding and time-varying score covariance structures is proposed in order to overcome the drawbacks of conventional MPCA and obtain better monitoring performance. The proposed method does not require prediction of the future values while the dynamic relations of data are preserved by using time-varying score covariance structures, and can be used to monitor batch processes in which the batch length varies. The proposed method was used to detect and identify faults in the fed-batch penicillin cultivation process, for four different fault scenarios. The simulation results clearly demonstrate the power and advantages of the proposed method in comparison to MPCA.     

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

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

Online monitoring applying the anaerobic respiratory monitoring system reveals iron(II) limitation in YTF medium for Clostridium ljungdahlii

Online monitoring of microbial cultures is an effective approach for studying both aerobic and anaerobic microorganisms. Especially in small‐scale cultivations, several parallel online monitored experiments can generate a detailed understanding of the cultivation, compared to a situation where a few data points are generated from time course sampling and offline analysis. However, the availability of small‐scale online monitoring devices for acetogenic organisms is limited. In this study, the previously reported anaerobic Respiration Activity MOnitoring System (anaRAMOS) device was adapted for online monitoring of Clostridium ljungdahlii (C. ljungdahlii) cultures with fructose as the carbon source. The anaRAMOS was applied to identify conversion of different carbon sources present in commonly used YTF medium. An iron(II) deficiency was discovered in this medium for C. ljungdahlii. Addition of iron(II) to the YTF medium reduced the cultivation time and increased biomass yield of C. ljungdahlii cultures by 50% and 40%, respectively. The measurement of the carbon dioxide transfer rate was used to calculated the iron(II) contained in complex components. By demonstrating the application of the anaRAMOS device for medium optimization, it is proven that the described online monitoring device has potential for use in process development.     

Physiological state control of fermentation processes

In this article a novel approach to the control of fermentation processes is introduced. A "physiological state control approach" has been developed using the concept of representing fermentation processes through the current physiological state of the cell culture. No conventional mathematical model is required for the synthesis of such a control system.The main idea is based on the fact that during batch, feed-batch, or even continuous cultivation the physiological characteristics of the cell population, jointly expressed by the term "physiological state", are not constant but rather variable, which is reflected in expected or unexpected changes in the behavior of the control plant, and which requires flexible alteration of the current control strategy. The proposed approach involves decomposition of the physiological state space into several subspaces called "physiological situations." In every physiological situation the cell population expresses stable characteristics, and therefore an invariant control strategy can be effectively applied. The on-line functions of the physiological state control system consist of the calculation of physiological state variables, determination of the current physiological situation as an element of a previously defined set of known physiological situations, switching of the relevant control strategy, and calculation of the control action. Attention is focused on the synthesis of the novel and nonstandard part of the control system - the algorithm for online recognition of the current physiological state. To this end an effective approach, based on artificial intelligence methods, particularly fuzzy sets theory and pattern recognition theory, was developed. Its practical realization is demonstrated using data from a continuous fermentation process for single cell protein production.     

A novel approach for using dielectric spectroscopy to predict viable cell volume (VCV) in early process development

Online monitoring of viable cell volume (VCV) is essential to the development, monitoring, and control of bioprocesses. The commercial availability of steam‐sterilizable dielectric‐spectroscopy probes has enabled successful adoption of this technology as a key noninvasive method to measure VCV for cell‐culture processes. Technological challenges still exist, however. For some cell lines, the technique's accuracy in predicting the VCV from probe‐permittivity measurements declines as the viability of the cell culture decreases. To investigate the cause of this decrease in accuracy, divergences in predicted vs. actual VCV measurements were directly related to the shape of dielectric frequency scans collected during a cell culture. The changes in the shape of the beta dispersion, which are associated with changes in cell state, are quantified by applying a novel “area ratio” (AR) metric to frequency‐scanning data from the dielectric‐spectroscopy probes. The AR metric is then used to relate the shape of the beta dispersion to single‐frequency permittivity measurements to accurately predict the offline VCV throughout an entire fed‐batch run, regardless of cell state. This work demonstrates the possible feasibility of quantifying the shape of the beta dispersion, determined from frequency‐scanning data, for enhanced measurement of VCV in mammalian cell cultures by applying a novel shape‐characterization technique. In addition, this work demonstrates the utility of using changes in the shape of the beta dispersion to quantify cell health. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:479–487, 2014     

An algorithm for online detection of temporal changes in operator cognitive state using real-time psychophysiological data

We consider the problem of on-the-fly detection of temporal changes in the cognitive state of human subjects due to varying levels of difficulty of performed tasks using real-time EEG and EOG data. We construct the Cognitive State Indicator (CSI) as a function that projects the multidimensional EEG/EOG signals onto the interval [0,1] by maximizing the Kullback–Leibler distance between distributions of the signals, and whose values change continuously with variations in cognitive load. During offline testing (i.e., when evolution in time is disregarded) it was demonstrated that the CSI can serve as a statistically significant discriminator between states of different cognitive loads. In the online setting, a trend detection heuristic (TDH) has been proposed to detect real-time changes in the cognitive state by monitoring trends in the CSI. Our results support the application of the CSI and the TDH in future closed-loop control systems with human supervision.     

Passive wireless MEMS microphones for biomedical applications

This paper introduces passive wireless telemetry based operation for high frequency acoustic sensors. The focus is on the development, fabrication, and evaluation of wireless, battery-less SAW-IDT MEMS microphones for biomedical applications. Due to the absence of batteries, the developed sensors are small and as a result of the batch manufacturing strategy are inexpensive which enables their utilization as disposable sensors. A pulse modulated surface acoustic wave interdigital transducer (SAW-IDT) based sensing strategy has been formulated. The sensing strategy relies on detecting the ac component of the acoustic pressure signal only and does not require calibration. The proposed sensing strategy has been successfully implemented on an in-house fabricated SAW-IDT sensor and a variable capacitor which mimics the impedance change of a capacitive microphone. Wireless telemetry distances of up to 5 centimeters have been achieved. A silicon MEMS microphone which will be used with the SAW-IDT device is being microfabricated and tested. The complete passive wireless sensor package will include the MEMS microphone wire-bonded on the SAW substrate and interrogated through an on-board antenna. This work on acoustic sensors breaks new ground by introducing high frequency (i.e., audio frequencies) sensor measurement utilizing SAW-IDT sensors. The developed sensors can be used for wireless monitoring of body sounds in a number of different applications, including monitoring breathing sounds in apnea patients, monitoring chest sounds after cardiac surgery, and for feedback sensing in compression (HFCC) vests used for respiratory ventilation. Another promising application is monitoring chest sounds in neonatal care units where the miniature sensors will minimize discomfort for the newborns.