Master of Science / Department of Grain Science and Industry / David L. Wetzel / During germination, biochemical changes occur in the wheat kernel by stimulation of enzymes and hormones, and the seed reserves are mobilized. Infrared microspectroscopy and imaging enables a localized chemical inventory, upon germination, to study the process. Frozen sections of germinated wheat mounted onto BaF[subscript]2 were mapped to produce functional group images for comparison with corresponding sections of ungerminated kernels. Relative functional group populations in the scutellum and embryonic axis were assessed before and after germination. An average 23% reduction in lipid to protein ratio was observed in the scutellum based on the comparison of 53,733 spectra. As a result of the early germination process, lipid in the scutellum was depleted to provide energy for the growing embryo. Germination of the kernels while in the field before harvest due to high humidity is known as preharvest sprouting. Preharvest sprouting has detrimental effects on the end use quality of the wheat (sprout damage) and cause economic loses. Tolerance to preharvest sprouting is highly desirable. To assist breeding program, a nondestructive near-IR chemical imaging method has been developed to test new lines for resistance to preharvest sprouting. The higher sensitivity of subsurface chemical imaging, compared with visual detection, alpha amylase determination, or viscosity testing, permits germination detection at early stages. A near-IR chemical imaging system with an InGaAs focal plane array (FPA) detector in the 1100 nm-1700 nm range was used. Kernels from six different cultivars, including HRW and HWW wheat, were exposed to moist conditions for 6, 12, 24, 36, and 48 hours. Images of each 90 kernel group were examined; kernels exposed to moisture for 36 hours were compared with kernels treated for 3 hours as a control. Each kernel was classified as sprouted or not sprouted with the criteria of log 1/R intensity at select wavelengths or select factors of principle component analysis (PCA) treatment of reflectance intensity data. Imaging wavelength range was expanded beyond 1700 nm to 2400 nm with the use of InSb FPA. Study for the potential for unsupervised determination in nondestructive near-IR imaging with detection wavelengths 1200-2400 is ongoing. Some preliminary results presented are encouraging.

The interplay of rhizosphere components such as root exudates, microbes, and minerals results in small-scale gradients of organic molecules in the soil around roots. The current methods for the direct chemical imaging of plant metabolites in the rhizosphere often lack molecular information or require labeling with fluorescent tags or isotopes. Here, we present a novel workflow using laser desorption ionization (LDI) combined with mass spectrometric imaging (MSI) to directly analyze plant metabolites in a complex soil matrix. Undisturbed samples of the roots and the surrounding soil of Zea mays L. plants from either field- or laboratory-scale experiments were embedded and cryosectioned to 100 mm thin sections. The target metabolites were detected with a spatial resolution of 25 mm in the root and the surrounding soil based on accurate masses using ultra-high mass resolution laser desorption ionization Fourier-transform ion cyclotron resonance mass spectrometry (LDI-FT-ICR-MS). Using this workflow, we could determine the rhizosphere gradients of a dihexose (e.g., sucrose) and other plant metabolites (e.g., coumaric acid, vanillic acid). The molecular gradients for the dihexose showed a high abundance of this metabolite in the root and a strong depletion of the signal intensity within 150 mm from the root surface. Analyzing several sections from the same undisturbed soil sample allowed us to follow molecular gradients along the root axis. Benefiting from the ultra-high mass resolution, isotopologues of the dihexose could be readily resolved to enable the detection of stable isotope labels on the compound level. Overall, the direct molecular imaging via LDI-FT-ICR-MS allows for the first time a nontargeted or targeted analysis of plant metabolites in undisturbed soil samples, paving the way to study the turnover of root-derived organic carbon in the rhizosphere with high chemical and spatial resolution.

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<p dir="ltr">This thesis focusses on developing novel data driven oral drug formulation analysis methods by employing technologies such as Fourier transform analysis and generative adversarial learning. Data driven measurements have been addressing challenges in advanced manufacturing and analysis for pharmaceutical development for the last two decade. Data science combined with analytical chemistry holds the future to solving key problems in the next wave of industrial research and development. Data acquisition is expensive in the realm of pharmaceutical development, and how to leverage the capability of data science to extract information in data deprived circumstances is a key aspect for improving such data driven measurements. Among multiple measurement techniques, chemical imaging is an informative tool for analyzing oral drug formulations. However, chemical imaging can often fall into data deprived situations, where data could be limited from the time-consuming sample preparation or related chemical synthesis. An integrated imaging approach, which folds data science techniques into chemical measurements, could lead to a future of informative and cost-effective data driven measurements. In this thesis, the development of data driven chemical imaging techniques for the analysis of oral drug formulations via Fourier transformation and generative adversarial learning are elaborated. Chapter 1 begins with a brief introduction of current techniques commonly implemented within the pharmaceutical industry, their limitations, and how the limitations are being addressed. Chapter 2 discusses how Fourier transform fluorescence recovery after photobleaching (FT-FRAP) technique can be used for monitoring the phase separated drug-polymer aggregation. Chapter 3 follows the innovation presented in Chapter 1 and illustrates how analysis can be improved by incorporating diffractive optical elements in the patterned illumination. While previous chapters discuss dynamic analysis aspects of drug product formulation, Chapter 4 elaborates on the innovation in composition analysis of oral drug products via use of novel generative adversarial learning methods for linear analyses.</p>

Roll compaction is a continuous process commonly used in the pharmaceutical industry for dry granulation of moisture and heat sensitive powder blends. It is intended to increase bulk density and improve flowability. Roll compaction is a complex process that depends on many factors, such as feed powder properties, processing conditions and system layout. Some of the variability in the process remains unexplained. Accordingly, modeling tools are needed to understand the properties and the interrelations between raw materials, process parameters and the quality of the product. It is important to look at the whole manufacturing chain from raw materials to tablet properties. The main objective of this thesis was to investigate the impact of raw materials, process parameters and system design variations on the quality of intermediate and final roll compaction products, as well as their interrelations. In order to do so, we have conducted a series of systematic experimental studies and utilized chemometric tools, such as design of experiments, latent variable models (i.e. PCA, OPLS and O2PLS) as well as mechanistic models based on the rolling theory of granular solids developed by Johanson (1965). More specifically, we have developed a modeling approach to elucidate the influence of different brittle filler qualities of mannitol and dicalcium phosphate and their physical properties (i.e. flowability, particle size and compactability) on intermediate and final product quality. This approach allows the possibility of introducing new fillers without additional experiments, provided that they are within the previously mapped design space. Additionally, this approach is generic and could be extended beyond fillers. Furthermore, in contrast to many other materials, the results revealed that some qualities of the investigated fillers demonstrated improved compactability following roll compaction. In one study, we identified the design space for a roll compaction process using a risk-based approach. The influence of process parameters (i.e. roll force, roll speed, roll gap and milling screen size) on different ribbon, granule and tablet properties was evaluated. In another study, we demonstrated the significant added value of the combination of near-infrared chemical imaging, texture analysis and multivariate methods in the quality assessment of the intermediate and final roll compaction products. Finally, we have also studied the roll compaction of an intermediate drug load formulation at different scales and using roll compactors with different feed screw mechanisms (i.e. horizontal and vertical). The horizontal feed screw roll compactor was also equipped with an instrumented roll technology allowing the measurement of normal stress on ribbon. Ribbon porosity was primarily found to be a function of normal stress, exhibiting a quadratic relationship. A similar quadratic relationship was also observed between roll force and ribbon porosity of the vertically fed roll compactor. A combination of design of experiments, latent variable and mechanistic models led to a better understanding of the critical process parameters and showed that scale up/transfer between equipment is feasible.

<div>Vibrational spectroscopic imaging has become an emerging platform for chemical visualization of biomolecules and materials in complex systems. For over a century, both Raman and infrared spectroscopy have demonstrated the capability to recognize molecules of interest by harnessing the characteristic features from molecular fingerprints. With the recent development of hyperspectral vibrational spectroscopy imaging, which records the chemical information without sacrificing the spatial-temporal resolution, numerous discoveries has been achieved in the field of molecular and cellular biology. Despite the ability to provide complimentary chemical information to Raman-based approaches, infrared spectroscopy has not been extensively applied in routine studies due to several fundamental limitations: 1). the poor spatial resolution; 2). inevitable strong water absorption; 3). lack of depth resolution.</div><div>Mid-infrared photothermal (MIP) microscopy overcame all the above mentioned problems and for the first time, enabled depth-resolved in vivo infrared imaging of live cells, microorganisms with submicrometer spatial resolution. The development of epi-detected MIP microscopy further extends its application in pharmaceutical and materials sciences. With the deployment of difference frequency generation and other nonlinear optical techniques, the spectral coverage of the MIP microscopy was significantly enhanced to enable chemical differentiation in complex systems across the broad mid-infrared region. In addition to the efforts to directly improve the performance of MIP microscopy, a novel quantitative phase imaging approach based on polarization wavefront shaping via custom-designed micro-retarder arrays was developed to take advantage of the highly sensitive phase measurement in combination with the photothermal effect. Besides, the extended depth-of-field and multifocus imaging enabled by polarization wavefront shaping could both improve the performance of MIP microscopy for volumetric imaging.</div>

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<p dir="ltr">Atmospheric aerosols are solid and liquid particles emitted from a range of natural and anthropogenic sources, and that impact Earth’s climate directly by interacting with solar radiation, as well as indirectly through modifications to the properties and lifecycles of clouds. Furthermore, atmospheric particles yield substantial implications on air quality, visibility, and human health. While the impact of aerosols on the planet has been broadly defined, accurate forecasting of atmospheric particle processes remains challenging due to their complex physicochemical properties. Highly variable aerosol characteristics include size, morphology, viscosity, elemental and molecular composition, hygroscopicity, mixing state, and light-absorption. Moreover, aerosols experience transformations as they evolve during transport downwind of the emission source. Aerosol evolution is dictated (between many other factors) by ambient conditions, such as relative humidity, temperature, and sunlight activity. This dissertation aims at providing a comprehensive characterization of atmospheric particles both at the bulk and single-particle level by implementing a unique combination of offline and online instrumentation.</p><p dir="ltr">The first chapter of this dissertation describes sources of atmospheric particles, as well as aerosol properties frequently examined to quantify their impact on our planet, such as chemical composition and light absorption. The second chapter delves into the wide range of techniques implemented in this study to characterize laboratory-generated and field-sampled aerosols. Notably, online measurements of chemical composition and optical properties were acquired with aircraft-deployed mass spectrometers and a particle-into-liquid sampler. These were frequently used to complement single-particle analysis employed with offline electron and X-ray microscopy techniques.</p><p dir="ltr">The third chapter describes a systematic approach to infer the viscosity of organic particles based on their morphology. Specifically, particles deform upon impacting a rigid surface during sampling, and the degree of deformation is highly influenced by the viscoelastic properties. Highly viscous and solid particles will retain their shape after sampling, whereas liquid-like particles will flatten drastically. Hence, we expanded on a semi-quantitative approach to infer the viscosity of particles based on their measured height-to-width aspect ratios. The fourth chapter discussed bulk measurements of chemical composition of smoke plumes emitted during wildfires in Western United States. An aerosol mass spectrometer was employed to quantify the mass concentration of key chemical species and their subsequent evolution during plume transport. Analyzed samples corresponded to daytime and nighttime particulate, which provided valuable insights on the impact of photochemical reactions on the composition and evolution of biomass burning particles. The fifth chapter serves as a follow-up study for that discussed in Chapter 4. Biomass burning particles were deposited onto substrates and taken for further chemical imaging. Scanning electron microscopy coupled with X-ray microanalysis provided single-particle information on the size, morphology, and elemental composition of aerosols sampled at different locations of the smoke plume. More detailed chemical information was acquired using synchrotron-based X-ray microscopy coupled with near-edge X-ray absorption fine structure spectroscopy. This technique distinguished between organic carbon, soot, and inorganic species, while also determining the contribution of functional groups, including alkenes, aliphatic, and carboxyl groups. Chemical imaging measurements were examined with respect to real-time optical data acquired onboard research aircraft. This facilitated correlating the chemical and light-absorbing properties of particles. The sixth chapter discusses a multi-modal, novel approach to distinguish between sources of soot-containing particles. Atomic force microscopy, integrated with Raman spectroscopy, was implemented for a screening of the morphological and spectral features of individual particles. Subsequently, automated μ-Raman was used to acquire the spectra of large ensembles of particles that are considered representative of the whole particle population. Emission sources of soot particles were then determined following two curve-fitting approaches previously established.</p><p dir="ltr">Overall, the studies discussed in this dissertation provide a comprehensive understanding of aerosol characteristics at the single-particle level, which is often overlooked by atmospheric model when predicting the impact of atmospheric particles on climate.</p>

No description available.