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

ABSTRACT: Abstract 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 Clostridium pasteurianum cultivations were used which feature a broad spectrum of solventogenic and acidogenic phenotypes, while 14 clusters of phenotypic manifestations were identified. The analysis of reference phenotypes showed distinct differences, where potential conditionalities were exemplary isolate

SUBMITTER: Hong Y 

PROVIDER: S-EPMC8811730 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature