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In this study, we analyzed mutants of  Clostridium acetobutylicum , an organism used in a broad range of industrial processes related to biofuel production, to facilitate future studies of bioreactor and bioprocess design and scale-up, which are very important research projects for industrial microbiology applications. To accomplish this, we generated 329 mutant strains and applied principal component analysis (PCA) to fermentation data gathered from these strains to identify a core set of independent features for comparison. By doing so, we were able to explain the differences in the mutant strains’ fermentation expression states and simplify the analysis and visualization of the multidimensional datasets related to the strains. Our study has produced a high-efficiency PCA application based on a data analytics tool that is designed to visualize screening results and to support several hundred sets of data on fermentation interactions to assist researchers in more precisely screening and capturing high-quality mutant strains. More importantly, although this study focused on the use of PCA in microbial fermentation engineering, its results are broadly applicable.

Simplifying multidimensional fermentation dataset analysis and visualization: One step closer to capturing high-quality mutant strains