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SIPBS is a major research centre in Scotland focusing on 'new medicines', 'better medicines' and 'better use of medicines'. This includes the exploration of nanoparticles and nanomedicines within the wider research agenda of bionanotechnology, in which the tools of nanotechnology are applied to solve biological problems. At SIPBS multidisciplinary approaches are also pursued to improve bioscience understanding of novel therapeutic targets with the aim of developing therapeutic interventions and the investigation, development and manufacture of drug substances and products.

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At-line monitoring of ammonium, glucose, methyl oleate and biomass in a complex antibiotic fermentation process using attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy

Roychoudhury, P. and Harvey, L.M. and McNeil, B. (2006) At-line monitoring of ammonium, glucose, methyl oleate and biomass in a complex antibiotic fermentation process using attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy. Analytica Chimica Acta, 561 (1-2). pp. 218-224. ISSN 0003-2670

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Abstract

The utility of attenuated total reflectance-mid-infrared spectroscopy (ATR-MIR) to monitor the concentrations of the key analytes ammonium, glucose, methyl oleate and biomass in a Streptomyces clavuligerus bioprocess for the synthesis of clavulanic acid was investigated. The cultivation medium was complex and the process fluid (matrix) underwent profound changes as culture growth proceeded, representing a considerable challenge from a spectroscopic viewpoint. Quantitative models were developed using the multivariate statistical technique, partial least square (PLS) for the key analytes over the entire time course of the bioprocess, which were validated externally using samples not incorporated in the original modelling exercise. The reasoning behind the choice of modelling strategy for each analyte is discussed, with particular focus on biomass modelling in order to generate clearer insight into the modelling process in this complex matrix. Despite the heterogeneous nature of the sample matrix, and the complexity of the spectral information arising, at-line models were developed giving low prediction error values for the analytes: ammonium 0.013 g/l; glucose 0.56 g/l; methyl oleate 0.38 g/l and biomass 0.39 g/l, respectively. These findings represent a considerable advance on previous studies using NIR in antibiotic processes, as all key analytes have been successfully modelled here, and the use of ATR-MIR in complex bioprocess fluids has been shown to be a viable method of near real-time monitoring.