In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO₂ and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD_600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

Buried explosive material, e.g., landmines, represent a severe issue for human safety all over the world. Most explosives consist of environmentally hazardous chemicals like 2,4,6-trinitrotoluene (TNT), carcinogenic 2,4-dinitrotoluene (2,4-DNT) and related compounds. Vapors leaking from buried landmines offer a detection marker for landmines, presenting an option to detect landmines without relying on metal detection. 2,4-Dinitrotoluene (DNT), an impurity and byproduct of common TNT synthesis, is a feasible detection marker since it is extremely volatile. We report on the construction of a wireless, handy and cost effective 2,4-dinitrotoluene biosensor combining recombinant bioluminescent bacterial cells and a compact, portable optical detection device. This biosensor could serve as a potential alternative to the current detection technique. The influence of temperature, oxygen and different immobilization procedures on bioluminescence were tested. Oxygen penetration depth in agarose gels was investigated, and showed that aeration with molecular oxygen is necessary to maintain bioluminescence activity at higher cell densities. Bioluminescence was low even at high cell densities and 2,4-DNT concentrations, hence optimization of different prototypes was carried out regarding radiation surface of the gels used for immobilization. These findings were applied to sensor construction, and 50 ppb gaseous 2,4-DNT was successfully detected.

The development of upstream bioprocesses necessitates small, instrumented bioreactors for investigating and optimizing production processes in a cost-effective manner. Due to advances in both the equipment and the materials used in additive manufacturing, 3D printing of customized bioreactors is now in the realm of possibilities. In this study, a small-scale 3D printed bioreactor suitable for mammalian and microbial cultivations was developed, featuring a working volume of 90 mL, inline pH and dissolved oxygen probes and a levitating magnetic stirrer. Aeration channels and a sampling port were printed directly into the vessel walls. Additionally, the vessel was equipped with a 3D printed customizable optical biomass-sensor. The bioreactor’s performance was evaluated through technical characterization and proof of concept cultivations, demonstrating that mixing time and oxygen mass transfer were sufficient for cultivating mammalian as well as microbial cells at high cell densities. Specifically, an Escherichia coli fed-batch cultivation achieved a maximum OD₆₀₀ of 204. Furthermore, a fed-batch cultivation of an IgG antibody-producing Chinese hamster ovary cell line reached a peak viable cell density of 10.2 × 10⁶ cells mL⁻¹ and a maximum product titer of 2.75 g L⁻¹. Using a three-parameter fit, the inline biomass signal could be correlated to the corresponding offline values with satisfactory accuracy, making it possible to monitor cell growth in real-time.

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of Escherichia coli (E. coli) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development.