In-situ imaging sensors for bioprocess monitoring: state of the art.

Over the last two decades, more and more applications of sophisticated sensor technology have been described in the literature on upstreaming and downstreaming for biotechnological processes (Middendorf et al. J Biotechnol 31:395-403, 1993; Lausch et al. J Chromatogr A 654:190-195, 1993; Scheper et al. Ann NY Acad Sci 506:431-445, 1987), in order to improve the quality and stability of these processes. Generally, biotechnological processes consist of complex three-phase systems--the cells (solid phase) are suspended in medium (liquid phase) and will be streamed by a gas phase. The chemical analysis of such processes has to observe all three phases. Furthermore, the bioanalytical processes used must monitor physical process values (e.g. temperature, shear force), chemical process values (e.g. pH), and biological process values (metabolic state of cell, morphology). In particular, for monitoring and estimation of relevant biological process variables, image-based inline sensors are used increasingly. Of special interest are sensors which can be installed in a bioreactor as sensor probes (e.g. pH probe). The cultivation medium is directly monitored in the process without any need for withdrawal of samples or bypassing. Important variables for the control of such processes are cell count, cell-size distribution (CSD), and the morphology of cells (Höpfner et al. Bioprocess Biosyst Eng 33:247-256, 2010). A major impetus for the development of these image-based techniques is the process analytical technology (PAT) initiative of the US Food and Drug Administration (FDA) (Scheper et al. Anal Chim Acta 163:111-118, 1984; Reardon and Scheper 1995; Schügerl et al. Trends Biotechnol 4:11-15, 1986). This contribution gives an overview of non-invasive, image-based, in-situ systems and their applications. The main focus is directed at the wide application area of in-situ microscopes. These inline image analysis systems enable the determination of indirect and direct cell variables in real time without sampling, but also have application potential in crystallization, material analysis, polymer research, and the petrochemical industry.