Near-infrared (NIR) spectroscopy is a widely used technique in quantitative analytical applications. Near-infrared spectroscopy is commonly used in clinical, environmental and industrial applications because of its compatibility with aqueous samples and with relatively thick samples. However, NIR spectra typically contain weak and highly overlapped spectral features which require multivariate data analysis techniques (chemometrics) to yield meaningful and chemically relevant information. This dissertation consists of two main themes which include applications of NIR spectroscopy combined with chemometrics to (1) model temperatures in clinically relevant aqueous-based samples and (2) model temperature and moisture content in nylon-6,6 polymers. This research employed overtone and combination bands of C-H, O-H and N-H bonds situated in the 4000 - 5000 cm-1 region to develop partial least-squares (PLS) regression models to predict analyte properties such as temperature, concentration and moisture content. The research described in the first part of this dissertation includes the development of a spectral preprocessing strategy based on the standard variate transform (SNV) and discrete wavelet transform (DWT) to isolate the low-frequency baseline information which carries the spectral features due to temperature fluctuations in aqueous-based samples. This approach was used to develop calibration models to determine the temperature of aqueous-based samples directly from their NIR spectra. This is an important development due to the fact that extreme temperature sensitivity of the underlying water bands can lead to poor quantitative analyte prediction results. These temperature models were developed using pH 7.4 phosphate buffer solutions spanning the range of 20 to 40.5 °C. Following the temperature models, a temperature-correction strategy based on the systematic pattern of concentration residuals was successfully developed to improve quantitative analyte predictions in aqueous-based samples. These analyte prediction models included glucose solutions and glucose-lactate mixture solutions prepared in pH 7.4 phosphate buffer. The computed temperature models gave excellent long-term prediction results. The temperature correction strategy gave promising results with the glucose solutions as well as the glucose-lactate mixture solutions. The research presented in the second part of this dissertation includes the development of calibration models to determine the temperature and moisture content of a piece of nylon-6,6 polymer directly from its NIR spectrum combining SNV and DWT procedures followed by PLS regression. Both models gave good long-term prediction results and predicted well across different nylon-6,6 sheets. Computed moisture model provides a reliable and fast method to determine the moisture content of a nylon polymer when compared to existing techniques. Extended research towards polymer characterization including preliminary investigations of inhomogeneous nature of nylon polymers using infrared microscopy is documented in the latter part of this dissertation.

This thesis focuses on the development of quantitative structure-activity relationship (QSPR) models for physicochemical properties, e.g., vapor pressure and partitioning coefficients. Such models can be used to estimate environmental distribution and transformation of the pollutants or to characterize solvents properties. Here, chemoinformatics was used as an efficient tool for modeling to produce safe chemicals based on green chemistry principles. Experimental determinations are only available for a limited number of the chemicals; however, theoretical molecular descriptors can be used for modeling of all organic compounds. In this thesis, we developed and validated a global and local QSPR model for vapor pressure of liquid and subcooled liquid organic compounds, in which perfluorinated compounds (PFCs) as outliers appeared in the model due to their molecular properties. Subsequently, after the update of the previous model, the vapor pressure of perfluorinated compounds (PFCs) for which no reliable experimental data are available was successfully predicted. At the same time, we used partitioning between n-octanol/water (Kow) and water solubility (Sw) to investigate the similarities and differences between linear solvation energy relationship (LSER) and partial least square projection to latent structures (PLS) models. Further, we developed QSPR model for prediction of melting points and boiling points of PFCs using multiple linear regression (MLR), PLS and associative neural networks (ASNN) approaches, meanwhile, the applicability domain of PFCs was also investigated. Experimental, semi-empirical and theoretical quantitative structure-retention relationship (QSRR) models were used to accurately predict retention factors (logk) in reversed-phase liquid chromatography (RPLC). These models are useful to characterize solvents for determination of the behavior and interactions of molecular structure and develop chromatographic methods. In both of QSPR and QSRR models using the PLS method, the first and second components captured main information which is related to van der Waals forces and polar interactions, and their results coincide with those from LSER. The results showed that the models of physicochemical properties and retention factors (logk) in chromatographic system can be successfully developed by the PLS method. PLS models were able to predict physicochemical properties of organic compounds directly from theoretical descriptors without prior synthesis, measurement or sampling. Further, the PLS method could overcome colinearity in data sets, and it is therefore a rapid, cheap and highly efficient approach

The research contained in the following dissertation spans a diverse range of scientific scholarship, including; chemometrics for integrated sensing and processing (ISP), near infrared and acoustic resonance spectrometry for analyte quantification and classification, and an ISP acoustic sensor as an alternative to conventional acoustic spectrometry. These topics may at first seem disjointed; however, closer inspection reveals that chemometrics, spectrometry, and sensors taken together form the umbrella under which applied spectrometry and analytical chemistry fall. The inclusion of each of these three serves to paint the complete portrait of the role of applied spectrometry for the advancement of process analytical technology. To illustrate the totality of this portrait, this research seeks to introduce and substantiate three key claims. (1) When applicable, optical spectrometry and acoustic spectrometry are preferred alternatives to slower and more invasive methods of analysis. (2) Chemometrics can be implemented directly into the physical design of spectrometers, thus sparing the need for computationally demanding post-collection multivariate analyses. (3) Using this principle, ISP sensors can be developed specifically for use in highly applied situations, making possible automatic analyte quantification or classification without the computational burden and extensive data analysis typically associated with conventional spectrometry. More concisely, these three claims can be stated as follows: spectrometry has a broad range of uses, chemometrics for ISP makes spectrometry more efficient, and for all analytical problems with a spectrometric solution, an ISP sensor, specifically tailored to the needs of the experiment, can more effectively solve the same analytical problem.

The quality of machinery liquids plays a critical role in ensuring safe and cost-effective operation of engines. Especially in the aviation industry, there is a great need for real-time and online monitoring of the purity, lubricity and age of machinery fluids. In this work, two optical techniques, excitation-emission matrix spectroscopy (EEMS) and cavity ring-down spectroscopy (CRDS), are used for monitoring of degradation and contamination of aero-turbine lubricants and jet fuels using optical fiber probes. We implement EEMS combined with a modified fiber probe design to characterize lubricant quality through the characteristic fluorescence of antioxidant additives. Multi-way analysis procedures, such as parallel factor analysis, are applied to correlate spectral features to antioxidant concentration, oxidative stability, and lubricant age. The spectroscopic data are then correlated to commonly used, off-line parameters such as the induction time and the breakdown number. It is shown that the decrease in fluorescence intensities of antioxidants coincides with the decomposition of the oil base stock. The induction times of synthetic jet turbine oil degraded at 150ºC, 195ºC and 215ºC are found to be at about 10,000, 3,500 and 400 min respectively. Simple kinetic models are developed that are capable of describing antioxidant reactions as pseudo first-order processes. We also demonstrate that with fluorescence detection it is possible to determine the concentration of oil contamination in jet fuel from about 10 to 1000 ppmv. In addition, a fiber-loop cavity ring-down spectrometer has been developed to quantitatively identify oil contamination of jet fuel by measuring optical absorption in the UV region. CRDS is a very sensitive, path-enhanced absorption technique that may be used for trace-species measurements in gas and liquids. The absorption measurements on samples with small volumes are characterized by measuring the concentration of turbine oil in jet fuel from 100 000 ppmv to a limit of detection of 400 ppmv. In summary, the obtained results permit us to specify the life time of lubrication oil and to determine the contamination of jet fuel with turbine oil qualitatively and quantitatively. In a simple optical configuration the fiber-coupled EEM and CRD methods permit in situ sampling of the machinery fluids. / Thesis (Ph.D, Chemistry) -- Queen's University, 2014-04-25 13:24:37.761

One of the many recorded adaptive responses in respiratory chain complex I deficient cells is the over-expression of the small metal binding proteins, metallothioneins (MTs). The antioxidant properties of MTs putatively protect the deficient cells against oxidative damage, thus limiting further damage and impairment of enzymes involved in energy production. Moreover, the role of metallothioneins in supplying metal cofactors to enzymes and transcription factors in order to promote energy metabolism was previously proposed, which could accompany their role as antioxidants. This view is supported by the observations that MT knockout mice tend to become moderately obese, implying a lower energy metabolic rate. Hence, the involvement of metallothioneins in mitochondrial function and disease cannot be ignored. However, this association is still very vague due to the diversity of their functions and the complexity of the mitochondrion. The use of systems biology technology and more specifically metabolomics technology was thus employed to clarify this association by investigating the metabolic differences between wild type and MT knockout mice in unchallenged conditions as well as when mitochondrial function (energy metabolism) was challenged with exercise and/or a high-fat diet. The metabolic differences between these mice were also studied when complex I of the respiratory chain was inhibited with rotenone. The metabolome content of different tissues and bio-fluids were examined in an untargeted fashion using three standardized analytical platforms and the data mined using modern metabolomics and related statistical methods. Clear metabolic differences were found between the wild type and MT knockout mice during unchallenged conditions. These metabolic differences were persisted and were often amplified when mitochondrial metabolism was specifically challenged through exercise, high-fat intake or complex I inhibition. The data pointed to an overall reduced metabolic rate in the MT knockout mice and possible insulin resistance after the interventions which imply (and confirm) the involvement of MTs in promoting energy metabolism in the wild type mice. / Thesis (Ph.D. (Biochemistry))--North-West University, Potchefstroom Campus, 2012

Thesis (Ph. D.)--University of Washington, 1988. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--Baylor University, 2007. / Includes bibliographical references (p. 225-231).

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2006. / Härtill 7 uppsatser.

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 24, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Renee Jiji. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 186-201).