Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations
Journal article, 2010
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.
iPLS
SACCHAROMYCES-CEREVISIAE CULTIVATIONS
Principal variables
fluorescence
NEURAL-NETWORKS
PICHIA-PASTORIS
PROTEIN-PRODUCTION
ARTIFICIAL
PLS
Multiwavelength
Genetic algorithm
NEAR-INFRARED SPECTROSCOPY
WEIGHTED REGRESSION
ACTIVATED-SLUDGE
Bioprocess monitoring
SITU MULTIWAVELENGTH FLUORESCENCE
CULTURE FLUORESCENCE
LWR
GENETIC ALGORITHMS
LOCALLY
Author
Peter Ödman
Danmarks Tekniske Universitet
Claus L. Johansen
Danisco AS
Lisbeth Olsson
Chalmers, Chemical and Biological Engineering, Industrial biotechnology
Krist V. Gernaey
Danmarks Tekniske Universitet
A. E. Lantz
Danmarks Tekniske Universitet
Applied Microbiology and Biotechnology
0175-7598 (ISSN) 1432-0614 (eISSN)
Vol. 86 6 1745-1759Subject Categories (SSIF 2011)
Industrial Biotechnology
DOI
10.1007/s00253-009-2412-y