Monitoring of complex industrial bioprocesses for
metabolite concentrations using modern spectroscopies
and machine learning: Application to gibberellic acid
production
Biotechnology and Bioengineering, 78 (5):
527--538 (5 June 2002)DOI: doi:10.1002/bit.10226
Abstract
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 <10% over a very wide titer range from 0 to
4925 ppm. However, although PLSR and ANNs are very
powerful techniques they are often described as black
box methods because the information they use to
construct the calibration model is largely
inaccessible. Therefore, a variety of novel
evolutionary computation-based methods, including
genetic algorithms and genetic programming, were used
to produce models that allowed the determination of
those input variables that contributed most to the
models formed, and to observe that these models were
predominantly based on the concentration of gibberellic
acid itself. This is the first time that these three
modern analytical spectroscopies, in combination with
advanced chemometric data analysis, have been compared
for their ability to analyze a real commercial
bioprocess. The results demonstrate unequivocally that
all methods provide very rapid and accurate estimates
of the progress of industrial fermentations, and
indicate that, of the three methods studied, Raman
spectroscopy is the ideal bioprocess monitoring method
because it can be adapted for on-line analysis. C 2002
Wiley Periodicals, Inc.