Search Results (16)

Hydrogels exhibit significant promise for advanced flexible sensing applications owing to their intrinsic softness, biocompatibility, and customizable functionalities. Nevertheless, their limited mechanical strength poses a critical barrier to practical implementation. In this study, we engineered a mechanically robust alginate/chitosan (SA/CS) hydrogel reinforced with meta-aramid (PMIA) nanofibers. The resulting composite hydrogel achieves a tensile strength of 16.8 MPa, substantially exceeding the performance of conventional biomass-derived hydrogels. When employed as a flexible sensor, the hydrogel demonstrates exceptional pressure-sensing capabilities, featuring high sensitivity (178.41 MΩ/MPa below 5 kPa), rapid response kinetics (0.4–0.8 s), and sustained stability (>200 cycles). Leveraging these properties, we successfully monitored vocal cord vibrations and finger motion trajectories, highlighting their potential for biomechanical sensing applications. Full article

Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting the urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, for in situ non-invasive estimation of biomass. In our experiment, the hardware of the Edge CV system includes the Jetson Nano with 4 GB RAM, 64 GB ROM, and a 128-core Maxwell GPU for executing intelligent machine vision tasks, along with embedded cameras for image data acquisition. Furthermore, a cascaded machine vision model was developed to enable biomass evaluation on the Edge CV system. The cascaded machine vision model mainly consists of three steps: first, the object detection task to locate the observation window, achieving a mean Average Precision (mAP50:95) of 82.3% with 78.7 GFLOPs; then, the segmentation task to extract liquid strain data within the observation window, yielding a mean intersection over union (MIoU) of 85.9% with 110.4 GFLOPs; and finally, calculating mycelium biomass indices via the morphological image processing task. The correlation between Edge CV inference and manual measurement showed an R² of 0.963 and an RMSE of 0.027 for normalized biomass indices, demonstrating a robust and consistent trend. Therefore, this study illustrates the practical application of edge computing-based machine vision for biomass soft sensing during the fermentation process. Full article

Plant-based meat alternatives, exemplified by Impossible Foods’ Impossible Burger, offer a sustainable, ethical substitute for traditional meat, closely mimicking the taste and appearance of meat by utilizing soy leghemoglobin (LegH), a 16 kDa holoprotein found in soy plants structurally similar to heme in animal meat. Cultivation medium plays an important role in bioprocess development; however, medium development or optimization can be labor intensive, and thus the use of previously reported media can be enticing. In this study, we explored the expression of recombinant LegH in Pichia pastoris in various reported cultivation media (BSM, BMGY, FM22, D’Anjou, BSM/2, and RDM) and using different feeding approaches (µ-stat and mixed feed with sorbitol). Our findings indicate that optimization techniques tailored to the specific process did not increase LegH yields, highlighting the need to investigate strain-specific strategies. We also utilized the collected process data to create and train a novel artificial neural network-based soft sensor for estimating cell biomass, relying solely on standard bioreactor measurements (such as stirrer speed, dissolved oxygen, O₂ enrichment, base feed, glycerol feed, methanol feed, and reactor volume). This soft sensor proved to be robust and exhibited a strong correlation (3.72% WCW) with experimental data. Full article

A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%). Full article

Adaptive laboratory evolution (ALE) is a valuable complementary tool for modern strain development. Insights from ALE experiments enable the improvement of microbial cell factories regarding the growth rate and substrate utilization, among others. Most ALE experiments are conducted by serial passaging, a method that involves large amounts of repetitive manual labor and comes with inherent experimental design flaws. The acquisition of meaningful and reliable process data is a burdensome task and is often undervalued and neglected, but also unfeasible in shake flask experiments due to technical limitations. Some of these limitations are alleviated by emerging automated ALE methods on the μL and mL scale. A novel approach to conducting ALE experiments is described that is faster and more efficient than previously used methods. The conventional shake flask approach was translated to a parallelized, L scale stirred-tank bioreactor system that runs controlled, automated, repeated batch processes. The method was validated with a growth optimization experiment of E. coli K-12 MG1655 grown with glycerol minimal media as a benchmark. Off-gas analysis enables the continuous estimation of the biomass concentration and growth rate using a black-box model based on first principles (soft sensor). The proposed method led to the same stable growth rates of E. coli with the non-native carbon source glycerol 9.4 times faster than the traditional manual approach with serial passaging in uncontrolled shake flasks and 3.6 times faster than an automated approach on the mL scale. Furthermore, it is shown that the cumulative number of cell divisions (CCD) alone is not a suitable timescale for measuring and comparing evolutionary progress. Full article

Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−r_O2), the specific growth rate (µ), and the rate of lipid synthesis (r_L) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (k_La) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different k_La values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0. Full article

This paper focuses on the design of soft sensors for on-line monitoring of the biotechnological process of biopolymer production, in which biopolymers are accumulated in bacteria as an intracellular energy storage material. The proposed soft sensors for on-line estimation of the biopolymer concentration represent an interesting alternative to the traditional off-line analytical techniques of limited applicability for real-time process control. Due to the complexity of biochemical reactions, which make it difficult to create reasonably complex first-principle mathematical models, a data-driven approach to the design of soft sensors has been chosen in the presented study. Thus, regression methods were used in this design, including multivariate statistical methods (PLS, PCR). This approach enabled the creation of soft sensors using historical process data from fed-batch cultivations of the Pseudomonas putida KT2442 strain used for the production of medium-chain-length polyhydroxyalkanoates (mcl-PHAs). Specifically, data from on-line measurements of off-gas composition analysis and culture medium capacitance were used as input to the soft sensors. The resulting soft sensors allow not only on-line estimation of the biopolymer concentration, but also the concentration of the cell biomass of the production bacterial culture. For most of these soft sensors, the estimation error did not exceed 5% of the measurement range. In addition, soft sensors based on capacitance measurement were able to accurately detect the end of the production phase. This study thus offers an innovative and practically relevant contribution to the field of monitoring of bioprocesses used for the production of medium-chain-length biopolymers. Full article

A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity. Full article

In mammalian cell culture, especially in pharmaceutical manufacturing and research, biomass and metabolic monitoring are mandatory for various cell culture process steps to develop and, finally, control bioprocesses. As a common measure for biomass, the viable cell density (VCD) or the viable cell volume (VCV) is widely used. This study highlights, for the first time, the advantages of using VCV instead of VCD as a biomass depiction in combination with an oxygen-uptake- rate (OUR)-based soft sensor for real-time biomass estimation and process control in single-use bioreactor (SUBs) continuous processes with Chinese hamster ovary (CHO) cell lines. We investigated a series of 14 technically similar continuous SUB processes, where the same process conditions but different expressing CHO cell lines were used, with respect to biomass growth and oxygen demand to calibrate our model. In addition, we analyzed the key metabolism of the CHO cells in SUB perfusion processes by exometabolomic approaches, highlighting the importance of cell-specific substrate and metabolite consumption and production rate qS analysis to identify distinct metabolic phases. Cell-specific rates for classical mammalian cell culture key substrates and metabolites in CHO perfusion processes showed a good correlation to qOUR, yet, unexpectedly, not for qGluc. Here, we present the soft-sensoring methodology we developed for qPyr to allow for the real-time approximation of cellular metabolism and usage for subsequent, in-depth process monitoring, characterization and optimization. Full article

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO. Full article

Microbial biomass concentration is a key bioprocess parameter, estimated using various labor, operator and process cross-sensitive techniques, analyzed in a broad context and therefore the subject of correct interpretation. In this paper, the authors present the results of P. pastoris cell density estimation based on off-line (optical density, wet/dry cell weight concentration), in-situ (turbidity, permittivity), and soft-sensor (off-gas O₂/CO₂, alkali consumption) techniques. Cultivations were performed in a 5 L oxygen-enriched stirred tank bioreactor. The experimental plan determined varying aeration rates/levels, glycerol or methanol substrates, residual methanol levels, and temperature. In total, results from 13 up to 150 g (dry cell weight)/L cultivation runs were analyzed. Linear and exponential correlation models were identified for the turbidity sensor signal and dry cell weight concentration (DCW). Evaluated linear correlation between permittivity and DCW in the glycerol consumption phase (<60 g/L) and medium (for Mut⁺ strain) to significant (for Mut^S strain) linearity decline for methanol consumption phase. DCW and permittivity-based biomass estimates used for soft-sensor parameters identification. Dataset consisting from 4 Mut⁺ strain cultivation experiments used for estimation quality (expressed in NRMSE) comparison for turbidity-based (8%), permittivity-based (11%), O₂ uptake-based (10%), CO₂ production-based (13%), and alkali consumption-based (8%) biomass estimates. Additionally, the authors present a novel solution (algorithm) for uncommon in-situ turbidity and permittivity sensor signal shift (caused by the intensive stirrer rate change and antifoam agent addition) on-line identification and minimization. The sensor signal filtering method leads to about 5-fold and 2-fold minimized biomass estimate drifts for turbidity- and permittivity-based biomass estimates, respectively. Full article

Facile detection methods for mycotoxins with high sensitivity are of great significance to prevent potential harm to humans. Herein, a label-free amperometric immunosensor based on a 3-D interconnected carbon nanofibers (CNFs) network coupled with well-dispersed Au nanoparticles (AuNPs) is proposed for the quantitative determination of aflatoxin B₁ (AFB₁) in wheat samples. In comparison to common carbon nanotubes (CNTs), the CNFs network derived from bacterial cellulose biomass possesses a unique hierarchically porous structure for fast electrolyte diffusion and a larger electrochemical active area, which increases the peak current of differential pulse voltammetry curves for an immunosensor. Combined with AuNPs that are incorporated into CNFs by using linear polyethyleneimine (PEI) as a soft template, the developed Au@PEI@CNFs-based immunosensor showed a good linear response to AFB₁ concentrations in a wide range from 0.05 to 25 ng mL⁻¹. The limit of detection was 0.027 ng mL⁻¹ (S/N = 3), more than three-fold lower than that of an Au@PEI@CNTs-based sensor. The reproducibility, storage stability and selectivity of the immunosensor were proved to be satisfactory. The developed immunosensor with appropriate sensitivity and reliable accuracy can be used for the analysis of wheat samples. Full article

Second generation ethanol faces challenges before profitable implementation. Biomass hydrolysis is one of the bottlenecks, especially when this process occurs at high solids loading and with enzymatic catalysts. Under this setting, kinetic modeling and reaction monitoring are hindered due to the conditions of the medium, while increasing the mixing power. An algorithm that addresses these challenges might improve the reactor performance. In this work, a soft sensor that is based on agitation power measurements that uses an Artificial Neural Network (ANN) as an internal model is proposed in order to predict free carbohydrates concentrations. The developed soft sensor is used in a Moving Horizon Estimator (MHE) algorithm to improve the prediction of state variables during biomass hydrolysis. The algorithm is developed and used for batch and fed-batch hydrolysis experimental runs. An alteration of the classical MHE is proposed for improving prediction, using a novel fuzzy rule to alter the filter weights online. This alteration improved the prediction when compared to the original MHE in both training data sets (tracking error decreased 13%) and in test data sets, where the error reduction obtained is 44%. Full article

Continuous bioprocessing using cell retention allows the achievement of high space-time yields for slow-growing organisms such as halophiles. However, the lack of efficient methods for monitoring and control limits the application of biotechnological processes in the industry. The aim of this study was to implement a control and online monitoring strategy for biomass in continuous cultures. For the first time, a feedforward cultivation strategy in a membrane-based cell retention system allowed to control the biomass concentration of the extreme halophilic Haloferax mediterranei at defined levels. Moreover, soft sensor-based biomass estimation allowed reliable monitoring of biomass online. Application of the combined monitoring and control strategy using industrial process water containing formate, phenol, aniline and 4,4′-methylenedianiline could for the first time demonstrate high throughput degradation in this extremophilic bioremediation process, obtaining degradation efficiencies of up to 100%. This process demonstrates the usefulness of continuous halophilic cultures in a circular economy application. Full article

The Bacterial Ghost (BG) platform technology evolved from a microbiological expression system incorporating the ϕX174 lysis gene E. E-lysis generates empty but structurally intact cell envelopes (BGs) from Gram-negative bacteria which have been suggested as candidate vaccines, immunotherapeutic agents or drug delivery vehicles. E-lysis is a highly dynamic and complex biological process that puts exceptional demands towards process understanding and control. The development of a both economic and robust fed-batch production process for BGs required a toolset capable of dealing with rapidly changing concentrations of viable biomass during the E-lysis phase. This challenge was addressed using a transfer function combining dielectric spectroscopy and soft-sensor based biomass estimation for monitoring the rapid decline of viable biomass during the E-lysis phase. The transfer function was implemented to a feed-controller, which followed the permittivity signal closely and was capable of maintaining a constant specific substrate uptake rate during lysis phase. With the described toolset, we were able to increase the yield of BG production processes by a factor of 8–10 when compared to currently used batch procedures reaching lysis efficiencies >98%. This provides elevated potentials for commercial application of the Bacterial Ghost platform technology. Full article