Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations
Artikel i vetenskaplig tidskrift, 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.

Principal variables

GENETIC ALGORITHMS

iPLS

ARTIFICIAL

SACCHAROMYCES-CEREVISIAE CULTIVATIONS

LWR

Multiwavelength

WEIGHTED REGRESSION

PICHIA-PASTORIS

SITU MULTIWAVELENGTH FLUORESCENCE

Genetic algorithm

ACTIVATED-SLUDGE

PLS

NEAR-INFRARED SPECTROSCOPY

LOCALLY

PROTEIN-PRODUCTION

CULTURE FLUORESCENCE

fluorescence

Bioprocess monitoring

NEURAL-NETWORKS

Författare

Peter Ödman

Danmarks Tekniske Universitet (DTU)

Claus L. Johansen

Danisco AS

Lisbeth Olsson

Chalmers, Kemi- och bioteknik, Industriell Bioteknik

Krist V. Gernaey

Danmarks Tekniske Universitet (DTU)

A. E. Lantz

Danmarks Tekniske Universitet (DTU)

Applied Microbiology and Biotechnology

0175-7598 (ISSN) 1432-0614 (eISSN)

Vol. 86 6 1745-1759

Ämneskategorier

Industriell bioteknik

DOI

10.1007/s00253-009-2412-y