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

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  author =       "Aoife C. McGovern and David Broadhurst and 
                 Janet Taylor and Naheed Kaderbhai and Michael K. Winson and 
                 David A. Small and Jem J. Rowland and 
                 Douglas B. Kell and Royston Goodacre",
  title =        "Monitoring of complex industrial bioprocesses for
                 metabolite concentrations using modern spectroscopies
                 and machine learning: Application to gibberellic acid
  journal =      "Biotechnology and Bioengineering",
  year =         "2002",
  volume =       "78",
  number =       "5",
  pages =        "527--538",
  month =        "5 " # jun,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computing, Fourier transform infrared spectroscopy,
                 dispersive Raman spectroscopy, pyrolysis mass
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1002/bit.10226",
  size =         "12 pages",
  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.",
  notes =        "PMID: 12115122",

Genetic Programming entries for Aoife C McGovern David I Broadhurst Janet Taylor Naheed Kaderbhai Michael K Winson David A Small Jem J Rowland Douglas B Kell Royston Goodacre