Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

Abstract

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.

The Potential of Bacilli-Derived Biosurfactants as an Additive for Biocontrol against Alternaria alternata Plant Pathogenic Fungi

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Rapid screening of bacteriostatic and bactericidal antimicrobial agents against Escherichia coli by combining machine learning (artificial intelligence) and UV-VIS spectroscopy

“…20,21 Acquiring a broader UV-VIS spectrum for each sample and coupling the analytical method with chemometric techniques allows one to create a rapid method for extracting biological and chemical information from the system. 21,22 Pre-processing of the spectra and the utilization of multivariate statistical analysis can provide qualitative and quantitative data about the biological systems and rapidly show differences between samples that single wavelength (univariate) analysis cannot. 23 Furthermore, the incorporation of machine learning methods or algorithms can also provide the potential to solve complex classification, regression, and outlier detection issues by performing optimal data transformations that determine boundaries between data points based on predefined spectra pertaining to the microbe/matrix/antibiotic.…”

Single-use, Metabolite Absorbing, Resonant Transducer (SMART) culture vessels for label-free, continuous cell culture progression monitoring

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