Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations
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Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l−1 biomass and 0.8 g l−1 total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.
KeywordsBioprocess monitoring Genetic algorithm iPLS LWR Multiwavelength fluorescence PLS Principal variables
The Ph.D. project of Peter Ödman is supported by a grant from the Innovative Bioprocess Technology Research Consortium, financed by the Danish Research Council for Technology and Production Sciences, Chr. Hansen A/S, Danisco A/S, and Novozymes A/S.
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