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

• Published in: Biotechnology letters 2001-01, Vol.23 (2), p.143-147
• Main Authors: Alison Arnold, S, Matheson, Liliana, Harvey, Linda M, McNeil, Brian
• Format: Article
• Language: English
• Subjects: Antibiotics; Biological and medical sciences; bioprocessing; Biotechnology; fermentation; Fundamental and applied biological sciences. Psychology; Health. Pharmaceutical industry; Industrial applications and implications. Economical aspects; Methods. Procedures. Technologies; Microbial engineering. Fermentation and microbial culture technology; monitoring; near-infrared spectroscopy; prediction; Production of active biomolecules; tylosin
• ISSN: 0141-5492; 1573-6776
• DOI: 10.1023/A:1010343828947

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.