## Abstract Nearβinfrared (NIR) spectroscopy can potentially provide onβline information on substrate, biomass, product, and metabolite concentrations in fermentation processes, which could be useful for improved monitoring or control. However, several factors can negatively influence the quality o
Near-infrared spectroscopy for bioprocess monitoring and control
β Scribed by Ken S. Y. Yeung; Mike Hoare; Nina F. Thornhill; Tom Williams; Jeetendra D. Vaghjiani
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 193 KB
- Volume
- 63
- Category
- Article
- ISSN
- 0006-3592
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β¦ Synopsis
This article describes the calibration of a spectroscopic scanning instrument for the measurement of selected contaminants in a complex biological process stream. Its use is for the monitoring of a process in which contaminants are to be removed selectively by flocculation from yeast cell homogenate. The main contaminants are cell debris, protein, and RNA. A low-cost instrument has been developed for sensitivity in the region of the NIR spectrum (from 1900 to 2500 nm) where preliminary work found NIR signatures from cell debris, protein, and RNA. Calibration models have been derived using a multivariate method for concentrations of these contaminants, such as would be found after the flocculation process. Two strategies were compared for calibrating the NIR instrument. In one case, samples were prepared by adding materials representative of the contaminants to clarified yeast homogenate so the contaminant levels were well known but outside the range of interest. In the other case, where samples were like those from the process stream after flocculation and floc removal, there was uncertainty of analysis of contaminant level, but the calibration was in the range of interest. Calibration using process stream samples gave results close to those derived from traditional assays. When the calibration models were used to predict the contaminant concentrations in previously unseen samples, the correlation coefficients between measurements and predictions were above 90% in all cases but one. The prediction errors were similar to the errors in the traditional assays.
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