Temporally segmented modelling: a route to improved bioprocess monitoring using near infrared spectroscopy?

Near infrared spectroscopy (NIRS) was used to monitor an industrial bioproc ess for the production of the antibiotic, tylosin, using a segmented modell ing approach. Models were built over the entire time course of the fermenta tion from 0 to 150 h, and also in two distinct phases or segments of the bi oprocess from 50 to 100 h (synthetic phase) and from 100 to 150 h (stationa ry 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 t he 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 mode ls 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.