Chemometrics is a discipline of chemistry which uses mathematical and statistical tools to help in the extraction of chemical information from measured data. With the assistance of chemometric methods, infrared (IR) spectroscopy has become a widely applied quantitative analysis tool. This dissertation explores two challenging applications of IR spectroscopy facilitated by chemometric methods: (1) passive Fourier transform (FT) remote sensing and (2) process monitoring by near-infrared (NIR) spectroscopy. Passive FT-IR remote sensing offers a measurement method to detect gaseous species in the outdoor environment. Two major obstacles limit the application of this method in quantitative analysis: (1) the effect of both temperature and concentration on the measured spectral intensities and (2) the difficulty and cost of collecting reference data for use in calibration. To address these problems, a quantitative analysis protocol was designed based on the use of a radiance model to develop synthetic calibration data. The synthetic data served as the input to partial least-squares (PLS) regression in order to construct models for use in estimating ethanol and methanol concentrations. The methodology was tested with both laboratory and field remote sensing data. Near-infrared spectroscopy has attracted significant interest in process monitoring because of the simplicity in sample preparation and the compatibility with aqueous solutions. For use in process monitoring, the need exists for robust calibrations. A challenge in the NIR region is that weak, broad and highly overlapped spectral bands make it difficult to extract useful chemical information from measured spectra. In this case, signal processing methods can be helpful in removing unwanted signals and thereby uncovering useful information. When applying signal processing as a spectral preprocessing tool and regression analysis for building a quantitative calibration model, optimizing the parameters that specify the details of the methods is crucial. In this research, particle swarm optimization, a population-based optimization method was applied. Digital filtering and wavelet processing methods were evaluated for their utility as spectral preprocessing tools. Both a pump-controlled flowing system and bioreactor runs involving the yeast, Pichia pastoris, were studied in this work. In investigating the bioreactor runs, insufficient reference data resulted in difficulties in employing the PLS calibration method. Instead, the augmented classical least-squares modeling technique was applied since it requires only pure-component or composite spectra of the analyte and background matrix rather than a large set of mixture samples of known analyte concentration.

In this dissertation, application of multi-way analysis is extended into new areas of environmental chemistry, microbiology, electrochemistry and organometallic chemistry. Additionally new practical aspects of some of the multi-way analysis methods are discussed. Parallel Factor Analysis Two (PARAFAC2) is used to classify a wide range of weathered petroleum oils using GC-MS data. Various chemical and data analysis issues exist in the current methods of oil spill analysis are discussed and the proposed method is demonstrated to have potential to be employed in identification of source of oil spills. Two important practical aspects of PARAFAC2 are exploited to deal with chromatographic shifts and non-diagnostic peaks.GEneralized Multiplicative ANalysis Of VAriance (GEMANOVA) is applied to assess the bactericidal activity of new natural antibacterial extracts on three species of bacteria in different structure and oxidation forms and different concentrations. In this work while the applicability of traditional ANOVA is restricted due to the high interaction amongst the factors, GEMANOVA is shown to return robust and easily interpretable models which conform to the actual structure of the data. Peptide-modified electrochemical sensors are used to determine three metal cations of Cu2+, Cd2+ and Pb2+ simultaneously. Two sets of experiments are performed using a four-electrode system returning a three-way array of size (sample ?? current ?? electrode) and a single electrode resulting in a two-way data set of size (sample ?? current). The data of former is modeled by N-PLS and that latter using PLS. Despite the presence of highly overlapped voltammograms and several sources of non-linearity N-PLS returns reasonable models while PLS fails. An intramolecular hydroamination reaction is catalyzed by several organometallic catalysts to identify the most effective catalysts. The reaction of starting material in the presence of 72 different catalysts is monitored by UV-Vis at two time points, before and after heating the mixtures in an oven. PARAFAC is applied to the three-way data set of (sample ?? wavelength ?? time) to resolve the overlapped UV-Vis peaks and to identify the effective catalysts using the estimated relative concentration of product (loadings plot of the sample mode).

<p>An intrinsic mathematical model for simulation of fixed bed chromatography was demonstrated and compared to more simplified models. The former model was shown to describe variations in the physical, kinetic, and operating parameters better than the latter ones. This resulted in a more reliable prediction of the chromatography process as well as a better understanding of the underlying mechanisms responsible for the separation. A procedure based on frontal liquid chromatography and a detailed mathematical model was developed to determine effective diffusion coefficients of proteins in chromatographic gels. The procedure was applied to lysozyme, bovine serum albumin, and immunoglobulin γ in Sepharose™ CL-4B. The effective diffusion coefficients were comparable to those determined by other methods.</p><p>Molecularly imprinted polymers (MIPs) are traditionally prepared as irregular particles by grinding monoliths. In this thesis, a suspension polymerization providing spherical MIP beads is presented. Droplets of pre-polymerization solution were formed in mineral oil with no need of stabilizers by vigorous stirring. The droplets were transformed into solid spherical beads by free-radical polymerization. The method is fast and the performance of the beads comparable to that of irregular particles. Optimizing a MIP formulation requires a large number of experiments since the possible combinations of the components are huge. To facilitate the optimization, chemometrics was applied. The amounts of monomer, cross-linker, and porogen were chosen as the factors in the model. Multivariate data analysis indicated the influence of the factors on the binding and an optimized MIP composition was identified. The combined use of the suspension polymerization method to produce spherical beads with the application of chemometrics was shown in this thesis to drastically reduce the number of experiments and the time needed to design and optimize a new MIP.</p>

<p>A new approach to analysis of protein-ligand interactions, termed proteochemometrics, has been developed. Contrary to traditional quantitative structure-activity relationship (QSAR) methods that aim to correlate a description of ligands to their interactions with one particular target protein, proteochemometrics considers many targets simultaneously.</p><p>Proteochemometrics thus analyzes the experimentally determined protein-ligand interaction activity data by correlating the data to a complex description of all interaction partners and; in a more general case even to interaction environment and assaying conditions, as well. In this way, a proteochemometric model analyzes an “interaction space,” from which only one cross-section would be contemplated by any one QSAR model.</p><p>Proteochemometric models reveal the physicochemical and structural properties that are essential for protein-ligand complementarity and determine specificity of molecular interactions. From a drug design perspective, models may find use in the design of drugs with improved selectivity and in the design of drugs for multiple targets, such as mutated proteins (e.g., drug resistant mutations of pathogens).</p><p>In this thesis, a general concept for creating of proteochemometric models and approaches for validation and interpretation of models are presented. Different types of physicochemical and structural description of ligands and macromolecules are evaluated; mathematical algorithms for proteochemometric modeling, in particular for analysis of large-scale data sets, are developed. Artificial chimeric proteins constructed according to principles of statistical design are used to derive high-resolution models for small classes of proteins.</p><p>The studies of this thesis use data sets comprising ligand interactions with several families of G protein-coupled receptors. The presented approach is, however, general and can be applied to study molecular recognition mechanisms of any class of drug targets.</p>

Gesunde sowie qualitativ hochwertige Nahrungsmittel sind wichtige Voraussetzungen, um einerseits die Lebensmittelsicherheit entlang der Wertschöpfungskette (Wachstum, Verarbeitung, Lagerung und Transport) und andererseits einen vorbeugenden Verbraucherschutz zu gewährleisten. Die Belastung der Nahrung mit Rückständen jeglicher Art war in letzter Zeit häufig Mittelpunkt heftiger öffentlicher Diskussionen. Zahlreiche Untersuchungen haben gezeigt, dass unter bestimmten Bedingungen durch Pilze gebildete Giftstoffe, so genannte Mykotoxine, die Ernteprodukte belasten und es bei deren Verzehr durch Menschen oder Tiere zu Vergiftungen kommen kann. Die FAO schätzt, dass etwa 25% der Weltproduktion von Nahrungsmitteln mit Mykotoxinen kontaminiert und in 20% der Getreideernte der Europäischen Union messbare Konzentrationen an Mykotoxinen detektierbar sind. Damit die Lebensmittelsicherheit weiterhin gewährleistet bleibt, werden neben den Routinemethoden schnellere und zerstörungsfreie Methoden benötigt, um die Lebensmittel direkt vor Ort auf Schimmelpilze und deren Mykotoxine zu untersuchen. In dieser Arbeit wird das Potenzial von ausgewählten optisch-basierten spektroskopischen Methoden für die in-situ bzw. in-line Detektion von Schimmelpilzen sowie Mykotoxinen in Getreide(produkten) untersucht. Es werden Absorptions- und Reflexionsmethoden einerseits und Fluoreszenztechniken andererseits eingesetzt. Die spektroskopischen Methoden werden dabei auf Proben unterschiedlicher Komplexität angewandt - beginnend mit der Untersuchung der photophysikalischen Eigenschaften der reinen Mykotoxine in Lösung über künstlich mit verschiedenen Mykotoxinen kontaminierten Getreideproben bis hin zu mit Pilzen befallenem Getreide und hochveredelte Lebensmittel (Wein und Bier) als Proben höchster Komplexität. / Problems of food safety have led to an increasing concern regarding contamination of foods and feeds with mycotoxins and the relevant toxigenic fungi, mainly Aspergillus, Penicillium and Fusarium genera. There is a real need for rapid, sensitive and inexpensive sensors for the detection of toxigenic fungi and mycotoxins, both in the field and after harvest in order to obtain real-time monitoring data on contamination and this assist in food safety assessment. This will result in an enormous cost saving to the farmers as well as to agro-food industry through the prevention and reduction of product recalls and reduced treatment costs. The German Ministry of Education and Research (BMBF) has provided funding of more than 1.9 million Euros from July 2006 to December 2009 for the large joint project "ProSeso.net2" on the development of innovative sensor-based techniques and processes in the field of food quality and safety. In this research-project "Exploration of sustainability potentials by use of sensor-based technologies and integrated assessment models in the production chain of plant related food" 13 partners from universities, non-university institutions and industry cooperate within seven subprojects. The expected results shall contribute to maintain freshness and improve safety of the food production chain. In the subproject “Indicators and sensor technology for the identification of mycotoxin producing fungi in the processing of grain” spectroscopic methods are tested for in-situ and in-line detection of moulds and/or mycotoxins. This presentation focuses on some possible spectroscopic methods for the rapid detection of mycotoxins and fungi on grains. Methods based on one- and two-photon-induced fluorescence spectroscopy are highly attractive because of their outstanding sensitivity and selectivity. In order to utilize a fluorescence technique for the analysis of the mycotoxins in food and feedstuff as well as for basic research on the fungal metabolism, the photochemistry and photophysics of the mycotoxins and fungi need to be elucidated in detail, especially the influence of solvent parameters such as polarity and pH value. Consequently, for a sensitive and selective spectroscopic analysis, it is indispensable to take the specific photophysic of the known mycotoxins into account in order to minimize serious limitations upon sensitivity, selectivity, and accuracy of a potential fluorescence-based sensing application. The spectroscopic techniques are complemented by chemometric tools (Principle Component Analysis) to extract the desired chemical information, e.g. with respect to presence of contaminations. The combination of data obtained from different spectroscopic methods (such as optimal excitation and emission wavelength, fluorescence decay times, and fluorescence quantum efficiency) on the one hand side and NIR spectroscopy on the other side shows promising results for the qualitative as well as quantitative identification of mycotoxins grains. Moreover, NIR reflectance spectra yield additional information on ingredients, moisture content, and the presence (or absence) of fungi in the sample.

The human being and other animals contain immensely complex biochemical processes that govern their function on a cellular level. It is estimated that several thousand small molecules (metabolites) are produced by various biochemical pathways in humans. Pathological processes can introduce perturbations in these biochemical pathways which can lead to changes in the amounts of some metabolites.Developments in analytical chemistry have made it possible measure a large number metabolites in a single blood sample, which gives a metabolic profile. In this thesis I have worked on establishing and understanding metabolic profiles from patients with rheumatoid arthritis (RA) and from animal models of the autoimmune diseases diabetes mellitus type 1 (T1D) and RA.Using multivariate statistical methods it is possible to identify differences between metabolic profiles of different groups. As an example we identified differences between patients with RA and healthy volunteers. This can be used to elucidate the biochemical processes that are active in a given pathological condition.Metabolite concentrations are affected by a many other things than the presence or absence of a disease. Both genomic and environmental factors are known to influence metabolic profiles. A main focus of my work has therefore been on finding strategies for ensuring that the results obtained when comparing metabolic profiles were valid and relevant. This strategy has included repetition of experiments and repeated measurement of individuals’ metabolic profiles in order to understand the sources of variation.Finding the most stable and reproducible metabolic effects has allowed us to better understand the biochemical processes seen in the metabolic profiles. This makes it possible to relate the metabolic profile differences to pathological processes and to genes and proteins involved in these.The hope is that metabolic profiling in the future can be an important tool for finding biomarkers useful for disease diagnosis, for identifying new targets for drug design and for mapping functional changes of genomic mutations. This has the potential to revolutionize our understanding of disease pathology and thus improving health care.

A new approach to analysis of protein-ligand interactions, termed proteochemometrics, has been developed. Contrary to traditional quantitative structure-activity relationship (QSAR) methods that aim to correlate a description of ligands to their interactions with one particular target protein, proteochemometrics considers many targets simultaneously. Proteochemometrics thus analyzes the experimentally determined protein-ligand interaction activity data by correlating the data to a complex description of all interaction partners and; in a more general case even to interaction environment and assaying conditions, as well. In this way, a proteochemometric model analyzes an “interaction space,” from which only one cross-section would be contemplated by any one QSAR model. Proteochemometric models reveal the physicochemical and structural properties that are essential for protein-ligand complementarity and determine specificity of molecular interactions. From a drug design perspective, models may find use in the design of drugs with improved selectivity and in the design of drugs for multiple targets, such as mutated proteins (e.g., drug resistant mutations of pathogens). In this thesis, a general concept for creating of proteochemometric models and approaches for validation and interpretation of models are presented. Different types of physicochemical and structural description of ligands and macromolecules are evaluated; mathematical algorithms for proteochemometric modeling, in particular for analysis of large-scale data sets, are developed. Artificial chimeric proteins constructed according to principles of statistical design are used to derive high-resolution models for small classes of proteins. The studies of this thesis use data sets comprising ligand interactions with several families of G protein-coupled receptors. The presented approach is, however, general and can be applied to study molecular recognition mechanisms of any class of drug targets.

An intrinsic mathematical model for simulation of fixed bed chromatography was demonstrated and compared to more simplified models. The former model was shown to describe variations in the physical, kinetic, and operating parameters better than the latter ones. This resulted in a more reliable prediction of the chromatography process as well as a better understanding of the underlying mechanisms responsible for the separation. A procedure based on frontal liquid chromatography and a detailed mathematical model was developed to determine effective diffusion coefficients of proteins in chromatographic gels. The procedure was applied to lysozyme, bovine serum albumin, and immunoglobulin γ in Sepharose™ CL-4B. The effective diffusion coefficients were comparable to those determined by other methods. Molecularly imprinted polymers (MIPs) are traditionally prepared as irregular particles by grinding monoliths. In this thesis, a suspension polymerization providing spherical MIP beads is presented. Droplets of pre-polymerization solution were formed in mineral oil with no need of stabilizers by vigorous stirring. The droplets were transformed into solid spherical beads by free-radical polymerization. The method is fast and the performance of the beads comparable to that of irregular particles. Optimizing a MIP formulation requires a large number of experiments since the possible combinations of the components are huge. To facilitate the optimization, chemometrics was applied. The amounts of monomer, cross-linker, and porogen were chosen as the factors in the model. Multivariate data analysis indicated the influence of the factors on the binding and an optimized MIP composition was identified. The combined use of the suspension polymerization method to produce spherical beads with the application of chemometrics was shown in this thesis to drastically reduce the number of experiments and the time needed to design and optimize a new MIP.

This thesis describes bioanalytical methods for generating fingerprints of biological systems for extracting relevant information with (protein) drugs in focus. Similarities and differences between samples can reveal the hidden relevant information, which can be used to optimize the production and facilitate the quality control of such protein drugs during their development and manufacture. Metabolic fingerprinting and multivariate data analysis (MVDA) can also facilitate early diagnosis of diseases and the effects and toxicity of drugs. Currently, several protein drugs are available on the global market. Nevertheless, despite, the success of such biotherapeutics significant challenges remain to be overcome in maintaining their stability and efficacity throughout their production cycle and long-term storage. The native structure and functional activity of therapeutic proteins is affected by many variables from production to delivery, incl. variables assoc. with conditions in bioreactors, purification, storage and delivery. Thus, part of the work underlying this thesis focused on structural analysis of a protein drug using chemical labeling, peptide mapping, and evaluation of the charge state distributions of the whole protein generated by ESI. The other part focuses on non-targeted metabolomics with a view to optimizing the cell cultivation process and assessment of the drug’s toxicity. A combination of appropriate analytical methods and MVDA is needed to find markers that can facilitate optimization of the cultivation system and expression of the target proteins in early stages of process development. Rapid methods for characterizing the protein drugs in different stages of the process are also required for quality control. In order to obtain high quality fingerprints analytical separation techniques with high resolution (such as HPLC or UHPLC) and sensitive analytical detection techniques (such as ESI, quadrupole or TOF MS) have been used, singly or in combination. / At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript.

El objeto de la presente tesis es el desarrollo de metodologías analíticas rápidas para optimizar los procesos de degradación de colorantes orgánicos presentes en aguas residuales. Se ha llevado a cabo una revisión bibliográfica acerca de dichos procesos y las técnicas analíticas utilizadas para su monitorización y la identificación de posibles intermedios. Se han establecido nuevas metodologías analíticas basadas en herramientas quimiométricas tales como diseño de experimentos para optimizar procesos de fotodegradación con y sin catalizador. Se ha empleado resolución de curvas multivariante para la determinación simultánea de colorantes e intermedios durante estos procesos. Dado que la velocidad de degradación es el parámetro mas utilizado para evaluar la eficiencia de las fotodegradaciones se han evaluado todas las constantes involucradas en la degradación y/o la adsorción sobre el catalizador mediante HS-MCR-ALS. Se ha establecido una metodología analítica rápida para la determinación de colorantes basada en cromatografía de inyección secuencial (SIC). / The main aim of this thesis is the development of rapid analytical methodologies to optimize the photodegradation processes of organic azo dyes from industrial wastewater. A bibliographic review has been done concerning the most employed processes for dye removal and the analytical techniques used for the monitoring and the identification of intermediates. New analytical methodologies based on chemometric tools such as experimental designs have been established to optimize photodegradation processes with and without using heterogeneous catalysts. Multivariate curve resolution has been employed to determine simultaneously different dyes and intermediates along degradation processes. Since the degradation rate is the most employed parameter to evaluate the photodegradation efficiency all the kinetic constants involved in the degradation and/or the adsorption of the dye onto the catalyst have been evaluated by HS-MCR-ALS. Furthermore a rapid analytical methodology for dye determination based on sequential injection chromatography (SIC) has been established.