Combination of multivariate data analysis and 2D-fluorescence spectroscopy in high-throughput cultivation experiments

Abstract
Unsupervised and supervised multivariate data analysis approaches are applied to evaluate the potential of 2D-fluorescence monitoring in microtiter plates. Principal component analysis (PCA) allows for an improved description of growth behavior for non-carbon-related substrate limitation cultivations of Hansenula polymorpha. Partial least square regression (PLS-R) methods are used to predict laborious and cost-intensive offline measurement of parameters such as carbon sources or pH value.