Advanced multivariate data evaluation for Fourier transform infrared spectroscopy

The objective of the presented dissertation was the evaluation, application and further development of advanced multivariate data evaluation methods for qualitative and quantitative Fourier transform infrared (FT-IR) measurements, especially of aqueous samples. The focus was set on 'evolving systems'; i.e. chemical systems that change gradually with a master variable, such as pH, reaction time, elution time, etc. and that are increasingly encountered in analytical chemistry. FT-IR measurements on such systems yield 2-way and 3-way data sets, i.e. data matrices and cubes. The chemometric methods used were soft-modeling techniques, like multivariate curve resolution - alternating least squares (MCR-ALS) or principal component analysis (PCA), hard modeling of equilibrium systems and two-dimensional correlation spectroscopy (2D-CoS). The research results are presented in six publications and comprise: A new combination of FT-IR flow titrations and second-order calibration by MCR-ALS for the quantitative analysis of mixture samples of organic acids and sugars. A novel combination of MCR-ALS with a hard-modeled equilibrium constraint for second-order quantitation in pH-modulated samples where analytes and interferences show very similar acid-base behavior. A detailed study in which MCR-ALS and 2D-CoS are directly compared for the first time. From the analysis of simulated and experimental acid-base equilibrium systems, the performance and interpretability of the two methods is evaluated. Investigation of the binding process of vancomycin, an important antibiotic, to a cell wall analogue tripeptide by time-resolved FT-IR spectroscopy and detailed chemometric evaluation. Determination of red wine constituents by liquid chromatography with FT-IR detection and MCR-ALS for resolution of overlapped peaks. Classification of red wine cultivars from FT-IR spectroscopy of phenolic wine extracts with hierarchical clustering and soft independent modeling of class analogy (SIMCA). (author)