Measurement Of Biomass Concentration Research Articles

Determining the linear correlation between dielectric spectroscopy and viable biomass concentration in filamentous fungal fermentations

ObjectivesDielectric spectroscopy is commonly used for online monitoring of biomass growth. It is however not utilized for biomass concentration measurements due to poor correlation with Cell Dry Weight (CDW). A calibration methodology is developed that can directly measure viable biomass concentration in a commercial filamentous process using dielectric values, without recourse to independent and challenging viability determinations.ResultsThe methodology is applied to samples from the industrial scale fermentation of a filamentous fungus, Acremonium fusidioides. By mixing fresh and heat-killed samples, linear responses were verified and sample viability could be fitted with the dielectric Delta C values and total solids concentration. The study included a total of 26 samples across 21 different cultivations, with a legacy at-line viable cell analyzer requiring 2 ml samples, and a modern on-line probe operated at-line with 2 different sample presentation volumes, one compatible with the legacy analyzer, a larger sample volume of 100 ml being compatible with calibration for on-line operation. The linear model provided an {R}^{2} value of 0.99 between Delta C and viable biomass across the sample set using either instrument. The difference in ∆C when analyzing 100 mL and 2 mL samples with an in-line probe can be adjusted by a scalar factor of 1.33 within the microbial system used in this study, preserving the linear relation with {R}^{2} of 0.97.ConclusionsIt is possible to directly estimate viable biomass concentrations utilizing dielectric spectroscopy without recourse to extensive and difficult to execute independent viability studies. The same method can be applied to calibrate different instruments to measure viable biomass concentration. Small sample volumes are appropriate as long as the sample volumes are kept consistent.

Bayesian calibration, process modeling and uncertainty quantification in biotechnology.

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Microalgae Monitoring in Microscale Photobioreactors via Multivariate Image Analysis

Microscale photobioreactors for microalgae growth represent an interesting technology for fast data production and biomass characterization; however, the small scale poses severe monitoring challenges, as traditional methods cannot be used. Non-invasive techniques are therefore needed to quantify biomass concentration and other culture properties, for example, pigment composition. To this purpose, a soft sensing approach based on multivariate image regression is proposed to exploit RGB images and/or PAM-imaging chlorophyll fluorescence. Different PLS (Partial Least Squares) regression models are used to estimate: (a) biomass concentration from the features extracted by RGB indices and/or PAM-imaging chlorophyll fluorescence measurements; and (b) Chlorophyll a content per cell from the features extracted by RGB indices and biomass concentration measurements. Every single model is aimed at characterizing the microalgae culture at different light intensities during batch growth. Results show that the proposed monitoring approach is as accurate as traditional measurement approaches and may represent a promising methodology for fast and inexpensive monitoring of microscale photobioreactors.

Control of Specific Growth Rate in Fed-Batch Bioprocesses: Novel Controller Design for Improved Noise Management

Accurate control of the specific growth rate (µ) of microorganisms is dependent on the ability to quantify the evolution of biomass reliably in real time. Biomass concentration can be monitored online using various tools and methods, but the obtained signal is often very noisy and unstable, leading to inaccuracies in the estimation of μ. Furthermore, controlling the growth rate is challenging as the process evolves nonlinearly and is subject to unpredictable disturbances originating from the culture’s metabolism. In this work, a novel feedforward-feedback controller logic is presented to counter the problem of noise and oscillations in the control variable and to address the exponential growth dynamics more effectively. The controller was tested on fed-batch cultures of Kluyveromyces marxianus, during which μ was estimated in real time from online biomass concentration measurements obtained with dielectric spectroscopy. It is shown that the specific growth rate can be maintained at different setpoint values with an average root mean square control error of 23 ± 6%.

Soft-Sensing Estimation of Optical Density for PHA Production Using Multilayer Perceptron Neural Network

Biomass concentration is an important indicator of production rate in polyhydroxyalkanoates (PHA) fermentation process. In current practice, measurement of biomass concentration is done off-line by laboratory analysis that is unsuitable for online process monitoring and control. Soft-sensor is often used as an alternative that provides an estimate of hard to measure parameters from easy to measure process data. However, most of these studies use simulated data or data generated from mathematical model that was developed without full consideration of process and measurement uncertainty. In this study, a soft-sensor is developed from real production data for PHA fermentation in pilot-scale bioreactor with the appropriate data pre-processing techniques applied to process data that was obtained from this system. Multilayer perceptron (MLP) neural network is used to estimate biomass concentration using secondary process parameters such as dissolved oxygen (DO), temperature, pH and agitation speed as inputs. Different models are developed based on different batches of production data and various network architecture in order to study the appropriate integration of process data and network topology that gives the best model accuracy. Results indicate that the biomass soft-sensor developed using MLP-ANN provides a better estimate of biomass in comparison to radial basis function (RBF) neural network and support vector regression (SVR) methods. The developed soft-sensor can be further used in monitoring and control of production output.

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases.

Photobioreactors are illuminated cultivation systems for experiments on phototrophic microorganisms. These systems provide a sterile environment for microalgal cultivation with temperature, pH, and gas composition and flow rate control. At bench-scale, photobioreactors are advantageous to researchers studying microalgal properties, productivity, and growth optimization. At industrial scales, photobioreactors can maintain product purity and improve production efficiency. The video describes the preparation and use of a bench-scale photobioreactor for microalgal cultivation, including the safe use of corrosive gas inputs, and details relevant biomass measurements and biomass productivity calculations. Specifically, the video illustrates microalgal culture storage and preparation for inoculation, photobioreactor assembly and sterilization, biomass concentration measurements, and a logistic model for microalgal biomass productivity with rate calculations including maximum and overall biomass productivities. Additionally, since there is growing interest in experiments to cultivate microalgae using simulated or real waste gas emissions, the video will cover the photobioreactor equipment adaptations necessary to work with corrosive gases and discuss safe sampling in such scenarios.

Using Colour Images for Online Yeast Growth Estimation.

Automatisation and digitalisation of laboratory processes require adequate online measurement techniques. In this paper, we present affordable and simple means for non-invasive measurement of biomass concentrations during cultivation in shake flasks. Specifically, we investigate the following research questions. Can images of shake flasks and their content acquired with smartphone cameras be used to estimate biomass concentrations? Can machine vision be used to robustly determine the region of interest in the images such that the process can be automated? To answer these questions, 18 experiments were performed and more than 340 measurements taken. The relevant region in the images was selected automatically using K-means clustering. Statistical analysis shows high fidelity of the resulting model predictions of optical density values that were based on the information embedded in colour changes of the automatically selected region in the images.

Preventing Overflow Metabolism in Crabtree-Positive Microorganisms through On-Line Monitoring and Control of Fed-Batch Fermentations

At specific growth rates above a particular critical value, Crabtree-positive microorganisms exceed their respiratory capacity and enter diauxic growth metabolism. Excess substrate is converted reductively to an overflow metabolite, resulting in decreased biomass yield and productivity. To prevent this scenario, the cells can be cultivated in a fed-batch mode at a growth rate maintained below the critical value, µcrit. This approach entails two major challenges: accurately estimating the current specific growth rate and controlling it successfully over the course of the fermentation. In this work, the specific growth rate of S. cerevisiae and E. coli was estimated from enhanced on-line biomass concentration measurements obtained with dielectric spectroscopy and turbidity. A feedforward-feedback control scheme was implemented to maintain the specific growth rate at a setpoint below µcrit, while on-line FTIR measurements provided the early detection of the overflow metabolites. The proposed approach is in line with the principles of Bioprocess Analytical Technology (BioPAT), and provides a means to increase the productivity of Crabtree-positive microorganisms.

Closed-loop growth-rate regulation in fed-batch dual-substrate processes with additive kinetics based on biomass concentration measurement

This paper deals with the design of feeding rates for dual-substrate fed-batch processes where the main control objective is the regulation of the microbial growth rate. To this end, feedback of the growth rate error is incorporated to a biomass proportional dual feeding law. A second-order sliding mode observer is used to estimate the growth rate, so that no additional sensors are required. Stability conditions are derived and robustness against several disturbances such as yield uncertainty, measurement errors and kinetic model mismatch is analytically and numerically evaluated. The advantages of the proposal include: minimal measurement requirements, regulation with fast convergence to the desired growth rate and reduced regulation error in the presence of disturbances.

Glycerine as a carbon source in nitrite removal and sludge production

The study examined how the COD/N ratio (2.0, 2.5, 3.0, 3.5, 4.0) and cycle length (24h and 12h), influence the effectiveness and the rate of nitrite reduction, and the final gaseous products of denitritation, when glycerine was a carbon source. In addition, daily sludge production (ΔX) was both estimated by calculating heterotrophic sludge yield (YHD) (ΔXYHD), and determined by making daily measurements of biomass concentration (ΔXexp). The results indicated that there were discrepancies between ΔXexp and ΔXYHD, which shows that estimating daily biomass production by calculating heterotrophic sludge yield gives estimates which are not particularly accurate. Therefore, to find the optimal COD/N ratio, ΔXexp was taken into consideration.The study showed that at a COD/N ratio of 2.0, complete nitrite elimination was not observed (nitrite effectiveness removal 68%); at COD/N ratios of 2.5–4.0, denitritation effectiveness was around 100%. However, at a COD/N ratio of 2.5, N2O concentration was 2 times higher than at the higher COD/N ratios (3.0–4.0). With a 12h cycle length, the denitritation rate was highest (18.85mgN-NO2/gVSSh) at a COD/N ratio of 3.5, whereas the rate was 1.61-times lower (9.0mgN-NO2/gVSSh) at a ratio of 3.0. Although the denitritation rates differed at these COD/N ratios, nitrites were completely reduced in the first 2h of the cycle. However, ΔXexp was 0.66gVSS/d at a COD/N ratio of 3.5, which was 30% higher than at a ratio of 3.0. This means that, taking into consideration the effectiveness and the rate of denitritation, its final gaseous products and sludge production, the most optimal COD/N ratio was 3.0 when glycerine was a carbon source.

Nonlinear control of continuous cultures of Porphyridium purpureum in a photobioreactor

Advanced control strategies proved to be promising tools to improve the performances of microalgae production systems, especially in the perspective of large scale cultivation plants. This paper proposes the validation of a nonlinear control strategy with experimental results. Additionally, the on-line estimation of the biomass concentration in a photobioreactor is presented. The proposed control law maintains the biomass concentration at a targeted level. This is achieved by a state feedback linearizing control law in an inner loop, in addition to a Proportional Integral regulator with an anti-windup compensation in an outer loop. To cope with the lack of on-line biomass concentration measurements, this variable is estimated on-line by an Extended Kalman Filter, based on available on-line measurements (pH, incident light intensity and dissolved carbon dioxide concentration). Performance and robustness of the proposed control strategy are assessed through experimental results obtained with cultures of the microalgae Porphyridium purpureum in a laboratory-scale continuous photobioreactor.

Behavior comparison for biomass observers in batch processes

ABSTRACTOn‐line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on‐line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ‐endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression‐based estimator, an artificial neural networks‐based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.

Nitrification kinetics and modified model for the Rideau River, Canada

Improving kinetic modeling of nitrification in rivers is of growing importance due to yearly increases in the anthropogenic release of nitrogen into rivers around the world. The use of water quality models can abate the expense of water quality monitoring while enabling the user to predict trends of variation. Data collected from a series of laboratory kinetic experiments were used to calculate the rate of nitrification in the Rideau River, Canada, and modify nitrification algorithms used in the traditional water quality model Qual2E. The modified model relates the reaction rate coefficients with a simple biomass concentration measurement of volatile suspended solids (VSS) in the river and subsequently introduces biomass growth functions directly into the kinetic algorithm. Furthermore, this modified model includes a nitrate-nitrogen assimilation pathway. The modified model demonstrates an improved correlation to nitrogen parameters observed in river water samples compared with the classical water quality model Qual2E. The inclusion of bacterial concentrations based upon the simple measurement of VSS plays a critical role in the reactions of the nitrification system and nitrate-nitrogen assimilation is an important pathway at low ammonia-nitrogen concentrations.

Nonlinear predictive control for maximization of CO2 bio-fixation by microalgae in a photobioreactor

In the framework of environment preservation, microalgae biotechnology appears as a promising alternative for CO₂ mitigation. Advanced control strategies can be further developed to maximize biomass productivity, by maintaining these microorganisms in bioreactors at optimal operating conditions. This article proposes the implementation of Nonlinear Predictive Control combined with an on-line estimation of the biomass concentration, using dissolved carbon dioxide concentration measurements. First, optimal culture conditions are determined so that biomass productivity is maximized. To cope with the lack of on-line biomass concentration measurements, an interval observer for biomass concentration estimation is built and described. This estimator provides a stable accurate interval for the state trajectory and is further included in a nonlinear model predictive control framework that regulates the biomass concentration at its optimal value. The proposed methodology is applied to cultures of the microalgae Chlorella vulgaris in a laboratory-scale continuous photobioreactor. Performance and robustness of the proposed control strategy are assessed through experimental results.

Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms

This paper deals with the estimation of unknown signals in bioreactors using sliding observers. Particular attention is drawn to estimate the specific growth rate of microorganisms from measurement of biomass concentration. In a recent article, notions of high-order sliding modes have been used to derive a growth rate observer for batch processes. In this paper we generalize and refine these preliminary results. We develop a new observer with a different error structure to cope with other types of processes. Furthermore, we show that these observers are equivalent, under coordinate transformations and time scaling, to the classical super-twisting differentiator algorithm, thus inheriting all its distinctive features. The new observers' family achieves convergence to time-varying unknown signals in finite time, and presents the best attainable estimation error order in the presence of noise. In addition, the observers are robust to modeling and parameter uncertainties since they are based on minimal assumptions on bioprocess dynamics. In addition, they have interesting applications in fault detection and monitoring. The observers performance in batch, fed-batch and continuous bioreactors is assessed by experimental data obtained from the fermentation of Saccharomyces Cerevisiae on glucose.

Metal-metabolomics of microalga Chlorella sorokiniana growing in selenium- and iodine-enriched media

AbstractThe microalga Chlorella sorokiniana has been used to accumulate selenium and iodine from culture media enriched with these elements as a first stage in the production of supplemented foods. The microalgal colony was grown in a conventional culture medium containing iodine (KI) at concentrations in the range of 150–4000 μg mL−1. Similar experiments were performed with selenium (SeO42−) at concentrations in the range of 20–500 μg mL−1. The concentration of iodine and selenium in the culture medium was analytically monitored daily and the viability of the colony was checked by biomass concentration measurement and by evaluation of the total content of chlorophyll and carotenoids. In addition, photosynthetic activity and the number of cells were also monitored. Iodine accumulation in the algal biomass increased rapidly with time and reached a steady state after 4 h of exposure. With Se exposure the colony viability decreased, although the culture grew well with concentrations of the element of 50 μg mL−1 in the culture medium; this experiment produced Se-enrichment in the alga (3 μg g−1) within 100 h. Sequential extraction of an algal pellet was performed in order to separate Se compounds according to their affinity with the following solvents: hot water to recover low molecular mass Se species, enzymatic extraction with driselase for species associated with the cell wall, sodium dodecyl sulphate (SDS) for water insoluble selenoproteins and, finally, enzymolysis with lipase and pronase that release and fragment residual selenoproteinsproducing compounds with low molecular mass. Size-exclusion chromatography (SEC) coupled with an ICP-MS detector showed the preponderance of Se-containing molecules with low molecular mass, possibly seleno-amino acids. Only a peak of low intensity located at 10 min was observed in the SDS extract that could be associated with a protein with molecular mass of 67 kDa. Finally, analysis of the aqueous extract of alga by reverse-phase chromatography with inductively-coupled plasma mass-spectrometry (RPC-ICP-MS) detection revealed the presence of selenocysteine (SeCys2), selenomethylselenocysteine (SeMetSeCys), selenomethionine (SeMet), and Se(VI), particularly the last two species.

Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers

Abstract This paper addresses the estimation of specific growth rate of microorganisms in bioreactors using sliding observers. In particular, a second-order sliding observer based on biomass concentration measurement is proposed. Differing from other proposals that only guarantee bounded errors, the proposed observer provides a smooth estimate that converges in finite time to the time-varying parameter. Stability is proved using a Lyapunov approach. The observer exhibits also robustness to process uncertainties since no model of the reaction is used for its design. In addition, the off-surface coordinate of the sliding observer is useful to determine the convergence time as well as to identify sensor faults and unexpected behaviors. Because of the structure of the output error injection, chattering phenomena of conventional sliding mode algorithms are substantially reduced. The features of the proposed observer are assessed by numerical and experimental data.

Interference by pigment in the estimation of microalgal biomass concentration by optical density

Optical density is used as a convenient indirect measurement of biomass concentration in microbial cell suspensions. Absorbance of light by a suspension can be related directly to cell density using a suitable standard curve. However, inaccuracies can be introduced when the pigment content of the cells changes. Under the culture conditions used, pigment content of the microalga Chlorella vulgaris varied between 0.5 and 5.5% of dry weight with age and culture conditions. This led to significant errors in biomass quantification over the course of a growth cycle, due to the change in absorbance. Using a standard curve generated at a single time point in the growth cycle to calculate dry weight (dw) from optical density led to average relative errors across the growth cycle, relative to actual dw, of between 9 and 18% at 680 nm and 5 and 13% at 750 nm. When a standard curve generated under low pigment conditions was used to estimate biomass under normal pigment conditions, average relative errors in biomass estimation relative to actual dw across the growth cycle were 52% at 680 nm and 25% at 750 nm. Similar results were found with Scenedesmus, Spirulina and Nannochloropsis. Suggested strategies to minimise error include selection of a wavelength that minimises absorbance by the pigment, e.g. 750 nm where chlorophyll is the dominant pigment, and generation of a standard curve towards the middle, or across the entire, growth cycle.

Formation and Characterization of Spherelike Bacterial Cellulose Particles Produced by Acetobacter xylinum JCM 9730 Strain

Spherelike cellulose formation as a function of agitated culture rotational speeds and flask sizes for two different cellulose producing Acetobacter xylinum strains, JCM 9730 (ATCC 700178) and NCIMB (ATCC 23769), has been studied in this work. Results showed that the JCM 9730 strain could form spherelike cellulose particles in the agitated culture with a rotational speed above 100 rpm. The NCIMB strain, however, formed no spherelike cellulose particles under any culture condition examined. For the JCM 9730 strain, approximately 10 mm diameter spheres were produced at a rotational speed of 150 rpm in 100 mL of culture solution in a 150 mL Erlenmeyer flask, while 0.5-1 mm diameter particles were produced in 100 mL of agitated culture with a rotational speed of 200 rpm in a 250 mL Erlenmeyer flask. Data from the measurement of biomass concentration and bacterial cellulose concentration revealed that the JCM 9730 strain exhibited higher cellulose yield (up to 6.8 times) as compared to the NCIMB strain. Scanning electron microscopy analysis of lyophilized spherelike cellulose particles indicated that culture rotational speed had an impact on the internal structure of the spherelike particles. Smaller spherelike particles produced at 150 rpm were hollow and the cellulose shell exhibited a layered structure. Larger particles produced at 125 rpm were solid where the cellulose in the central region did not exhibit a layered structure, but the outer layer was similar in structure to the particles produced at 150 rpm.

Specific Growth Rate Estimation in Bioreactors Using Second-Order Sliding Observers

This paper addresses the estimation of specific growth rate of microorganisms in bioreactors. In particular, a second-order sliding observer based on biomass concentration measurement is proposed. Differing from other proposals that only guarantee bounded errors, the observer provides a smooth estimate that converges in finite time to the time-varying parameter. Further, the observer exhibits very robust properties since no model of the kinetics is used for its design. In addition, the off-surface coordinate of the sliding observer is useful to determine the convergence time as well as to identify sensor faults and unexpected behaviors. The features of the proposed observer are assessed by numerical and experimental data.