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Near infrared spectroscopy (NIRS) was used to monitor an industrial bioprocess for the production of the antibiotic, tylosin, using a segmented modelling approach. Models were built over the entire time course of the fermentation from 0 to 150 h, and also in two distinct phases or segments of the bioprocess from 50 to 100 h (synthetic phase) and from 100 to 150 h (stationary phase). All models were validated externally and the performance of the full range and segmented models compared. The standard error of prediction (SEP) of the segmented models was less in both 50–100 h and 100–150 h and the correlation highest in the 50–100 h range. This would suggest that data segmentation is potentially a useful method of accommodating the impact of the pronounced matrix changes which occur in some bioprocesses in NIRS models for key analytes. While there are many reports on bioprocess monitoring using NIRS, there have been no previous studies on the use of segmented NIR models within a bioprocess as a means of accommodating matrix change. [ABSTRACT FROM AUTHOR]

Abstract: The need for effective bioprocess (fermentation) monitoring is growing in importance due to the rapid pace of change in the fermentation industry, and attendant financial pressures. Vibrational spectroscopy has shown great promise in bioprocess monitoring. In particular, recently attention has been focused on the capability of mid infrared spectroscopy (MIRS) to monitor multiple analytes in highly complex fermentation fluids. The potential of this powerful analytical technique is critically evaluated by discussion of relevant studies. The advantages and limitations of MIR are discussed in the context of “rival” technologies, such as near infrared, focusing especially on employing such techniques in bioprocesses for real time (either in situ or ex situ) measurements. The potential barriers to the development of MIRS for real time monitoring are identified and further research directions highlighted. [Copyright &y& Elsevier]