The Choice of Suitable Online Analytical Techniques and Data Processing for Monitoring of Bioprocesses

Abstract

With increasing pressure from regulatory authorities on industry to develop processes embracing process analytical technology (PAT) initiatives, there is a growing demand to establish reliable tools and systems capable of meeting this need. With regard to monitoring and control of bioprocesses, this need translates to a search for robust instrumentation capable of monitoring the critical process parameters in real time. The application of such technologies at all stages of the process, from the initial R&D phase to process optimisation and production, enhances process understanding and paves the way for the development of control platforms. An examination of the PAT concept and selected tools (NIR, MIR, Raman, dielectric spectroscopy and calorimetry) are presented here. A description of each tool is given, with particular emphasis on the nature of the signal produced and how these relate to measurements of biomass, metabolites and product. A description of the signal processing that is necessary to gain meaningful results from the different tools is also given, together with online data reconciliation techniques based on mass and energy balances. Many techniques such as those based on vibrational spectroscopy are of particular interest, since they are capable of monitoring several critical process parameters which are typically controlled in a bioprocess. A window of application for each of the techniques, when used in the area of bioprocessing, is suggested based on their uses and inherent limitations.

A robust flow cytometry-based biomass monitoring tool enables rapid at-line characterization of S. cerevisiae physiology during continuous bioprocessing of spent sulfite liquor

“…In an industrial setting, physical techniques capable of real time measurement are preferred [19]. Common methods for in situ measurement of viable biomass include dielectric spectroscopy, infrared spectroscopy and fluorescence spectroscopy, NIR spectroscopy, and Raman spectroscopy as well as microscopy combined with image analysis [19][20][21][22][23]. However, inline sensors are prone to high measurement noise and require chemometric knowledge to establish meaningful measurement techniques or display limitations in other fields [20].…”

Monitoring online biomass with a capacitance sensor during scale-up of industrially relevant CHO cell culture fed-batch processes in single-use bioreactors

“…Several online methods to monitor the cell concentration of mammalian cell cultures have been investigated and developed in the last years (e.g. radio frequency impedance, Raman spectroscopy or near-infrared spectroscopy) [16][17][18][19][20][21][22].…”

Real‐time monitoring of ethanol production during Pichia stipitis NRRL Y‐7124 alcoholic fermentation using transflection near infrared spectroscopy

“…Saccharomyces cerevisiae is the most widely used yeast in alcoholic fermentation because Correspondance: Dr. María Guadalupe Aguilar-Uscanga (gaguilar@itver.edu.mx), Bioengineering Laboratory, Food Research and Development Unit, Veracruz Institute of Technology, Av. Development and bioprocess optimization are highly dependent on accurate real-time monitoring of chemical and physical process variables [11][12][13]. Some investigators have published studies using xylose-fermenting recombinant S. cerevisiae strains to solve this problem [3][4][5][6].…”

“…Furthermore, reliable monitoring can help to improve fundamental understanding of cellular metabolism and thus be able to optimize the bioprocess [9,10]. Development and bioprocess optimization are highly dependent on accurate real-time monitoring of chemical and physical process variables [11][12][13]. Hence, near infrared spectroscopy (NIRS) can be applied as a real-time fermentation monitoring methodology using multiconstituent, rapid and nondestructive analyses, without involving sample pretreatment that leads to effective bioprocess control, a tool for increased yield, productivity, and reproducibility [14][15][16].…”