Quantification of microbial productivity via multi-angle light scattering and supervised learning

Label-free identification of bacterial microcolonies via elastic scattering

Label-free microcolony identification via elastic light scattering was investigated for three different genera: Salmonella enterica serovar Montevideo, Listeria monocytogenes F4244, and Escherichia coli DH5α. Microcolonies were defined as bacterial colonies in their late-lag phase to early-exponential phase with the diameter range of 100-200 µm. To link biophysical characteristics with corresponding scattering patterns, a phase contrast microscope and a confocal displacement meter were used to measure the colony diameter and its 3D height profile. The results indicated that the growth characteristics of microcolonies were encoded in their morphologies which correlated to the characteristic diffraction patterns. Proposed methodology was able to classify three genera based on comprehensive phenotypic map which incorporated growth speed, ring count, and colony diameter. While the proposed method illustrated the possibility of discriminating microcolonies in their early growth stage, more thorough biophysical understanding is needed to expand the technology to other species.

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 <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.