Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production

Awduron Sefydliadau
Math Erthygl
Iaith wreiddiolSaesneg
Tudalennau (o-i)527-538
Nifer y tudalennau12
CyfnodolynBiotechnology and Bioengineering
Cyfrol78
Rhif y cyfnodolyn5
Dangosyddion eitem ddigidol (DOIs)
StatwsCyhoeddwyd - 2002
Cysylltiadau
Cysylltiad parhaol
Gweld graff cysylltiadau
Fformatau enwi

Crynodeb

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was