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

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