A Novel Approach to Assess Environmental Changes in Marine Ecosystems via Spectroscopic Analyses of Microalgae

Chemical analyses for environmental monitoring encounter many challenges which are imposed by a multitude of chemically complex and interrelated processes. For such investigations, innovative analytical methodologies must be developed which characterize chemical shifts of key environmental parameters in order to deduce insights into their ecological relevance. This dissertation is driven by an analytical chemistry perspective to develop chemical sensing techniques with the ultimate goal of gaining a deeper understanding of environmental changes and their chemical origins.

In order to overcome limitations inherent to any chemical sensor designed for a specific task, new paths are pursued which are based on the idea of utilizing microalgae cells as ‘biological probes’. It has been observed that microalgae cells sensitively respond to changes in their environment through changes in intracellular chemical composition. Thus, the research hypothesis of this dissertation is to utilize microalgae cells as in-situ ‘measurement mediators’ to study environmental changes of selected environmental parameters. To develop such analytical methodologies, two complementary research projects were performed:

The first topic focused on quantifying the change in the microalgae cells’ chemical composition as a measure of shifts in ambient conditions. To accomplish this, intracellular concentrations of selected lipids, amino acids, proteins, carboxylic acids, mono- and polysaccharides were determined based on FT-IR spectroscopy. This goal required progress in sample preparation methods for solids as well as innovations in multivariate data preprocessing.

The second research topic was geared from an empirical angle and developed prediction models for relating the microalgae’s infrared spectroscopic signatures to selected environmental parameters. For this purpose, three algae species were cultured under well-defined and carefully varied conditions such as ambient carbon and nitrogen concentrations. From resulting spectroscopic data sets, nonlinear models of these biological systems were derived by which the ambient growing conditions could be predicted.