Phenotype analysis of cultivation processes via unsupervised machine learning: Demonstration for .

A novel approach of phenotype analysis of fermentation-based bioprocesses based on unsupervised learning (clustering) is presented. As a prior identification of phenotypes and conditional interrelations is desired to control fermentation performance, an automated learning method to output reference phenotypes (defined as vector of biomass-specific rates) was developed and the necessary computing process and parameters were assessed. For its demonstration, time series data of 90 cultivations were used which feature a broad spectrum of solventogenic and acidogenic phenotypes, while 14 clusters of phenotypic manifestations were identified.

Conversion of biodiesel-derived glycerol into biotechnological products of industrial significance by yeast and fungal strains.

Oleochemical activities (e.g. biodiesel production, fat saponification) generate annually very high amounts of concentrated glycerol-containing waters (called crude glycerol) as the principal residues of these processes.