OH-stretching spectra of various vapour phase species were recorded to investigate hydrogen bonding. The species studied include 1,2-ethanediol, 1,3-propanediol, 1,4-butanediol, acetylacetone, hexafluoroacetylacetone and the complex formed in the heterogeneous mixture of methanol and trimethylamine. The spectra range from the infrared, near infrared to visible wavelengths. The main focus of this study is in the near infrared region, in which the OH-stretching overtones are dominant. The near infrared and visible spectrum of formic acid has been recorded to investigate coupling across bonds, specifically a resonance occurring between OH- and CH-stretching vibrations. The same resonance was also observed in the spectrum of 1,2-ethanediol. The spectra of deuterated isotopomers of formic acid and 1,2-ethanediol were recorded to experimentally verify the resonance. The inherently weak nature of the vibrational overtone transitions required sensitive spectroscopic techniques to observe the spectra. The spectra were recorded with conventional long path length absorption spectroscopy and intracavity laser photoacoustic spectroscopy. Anharmonic oscillator local mode calculations of the OH-stretching transitions were performed to simulate the observed spectra. These calculations require calculation of potential energy surfaces and dipole moment functions. Simulated spectra obtained with highly correlated ab initio methods and large basis sets have yielded the best agreement with observation.

Thesis (MSc)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

The Andromeda Galaxy (M31) is an ideal target for detailed studies of galaxy structure and tests of stellar population models. This thesis presents deep Canada-France-Hawaii Telescope WIRCam near-infrared J- and Ks-band photometric maps of M31. These near-infrared data alleviate the age-metallicity-dust degeneracy that plagues stellar population analysis of optical-only maps. For the sake of calibrating stellar population models, a detailed reconstruction of the M31 near-infrared surface brightness and a study of sky subtraction uncertainties is needed. The analysis of our 2007 and 2009 WIRCam data has revealed unexpected spatial variations in the sky background shapes over the width of the WIRCam fields. In order to solve for the offset caused by such fluctuations, we have used couplings between images. Scalar sky offsets are optimized to produce a mosaic that is seamless within 0.02% of the sky background. These offsets are solved hierarchically, to reduce the dimensionality of optimizations, and an adaptation of the Nelder Mead downhill simplex ensures a globally optimal solution. Variations in sky shape are well-characterised in median sky images built by nodding to a random ring of sky fields every 1.2 minutes. Sky shape appears consistent across the 3˚ ring of sky fields, while levels do change by ~2%, suggesting that the dominant sky structures extend beyond the M31 survey region. Planar sky offset optimization was tested and promises to significantly improve continuity across the outer disk of M31. Our near-infrared data are part of an effort to assemble a multi-wavelength data set for M31 to study a broad suite of topics in stellar and galaxy evolution. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2010-12-07 15:34:00.279

A platform technology is identified for grain handling facilities to improve grading and determine non-destructively different quality parameters of wheat. In this study, a near-infrared (NIR) hyperspectral imaging system was used to scan four wheat classes namely, Canada Western Red Spring (CWRS), Canada Prairie Spring Red (CPSR), Canada Western Hard White Spring (CWHWS), and Canada Western Soft White Spring (CWSWS) that were collected from across various growing regions in Manitoba, Saskatchewan, and Alberta in 2007, 2008, and 2009 crop years. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of four wheat classes at three moisture levels for each class was created. These image cubes were acquired in the wavelength region of 960-1700 nm with 10 nm intervals. Wheat classification was done using the non-parametric statistical and a four-layer back propagation neural network (BPNN) classifiers. Average classification accuracies of 93.1 and 83.9% for identifying wheat classes using the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), respectively, were obtained for two-class identification models that included variations of moisture levels, growing locations, and crop years of samples. In the pair-wise moisture discrimination study, near-perfect classifications were achieved for wheat samples which had difference in moisture levels of about 6%. The NIR wavelengths of 1260-1380 nm had the highest factor loadings for the first principal component using the principal components analysis (PCA). A four-layer BPNN classifier was used for two-class identification of wheat classes and moisture levels. Overall average pair-wise classification accuracies of 83.7% were obtained for discriminating wheat samples based on their moisture contents. Classification accuracies of 83.2, 75.4, 73.1%, on average, were obtained for identifying wheat classes for samples with 13, 16, and 19% moisture content (m.c.), respectively. Ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models were developed using a ten-fold cross validation for prediction. Prediction performances of PLSR and PCR models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Overall, PLSR models demonstrated better prediction performances than the PCR models for predicting protein contents and hardness of wheat.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique for monitoring of brain functional activity. It uses near-infrared (NIR) light to get the information related to brain hemodynamic response as most of the tissues in the brain are transparent to NIR light. The main goal of this study was to design, implement and evaluate a continuous-wave near-infrared spectroscopy (CW-NIRS) system for human’s brain cognitive functions. This system is portable, and works with a small rechargeable battery; thus, it may be used for bedside monitoring. In our CW-NIRS system, we used 3 multi-wavelength LEDs and 8 photodiodes (with built-in amplifiers) resulting in 12 channels (voxels). The collected signals of these 12 channels, at a sampling rate of 15 Hz, can be used for 2D image reconstruction to monitor functional brain activity. All LEDs and photodiodes are placed on a flexible printed circuit board (PCB), which covers the forehead to measure hemodynamic response of the prefrontal cortex. We also developed a software in MATLAB for analysis of optical signals recorded by our CW-NIRS system. This software provides 2D image reconstruction and monitoring of changes in concentration of oxygenated ([HbO2]) and deoxygenated ([HbR]) hemoglobin as well as the total hemoglobin ([HbT]) for the 12 channels over the prefrontal cortex (forehead). The software has also an embedded statistical analysis option for analyzing the collected signals and displaying the results. The developed CW-NIRS system was evaluated on 14 individuals (24±3 years old) on two common cognitive tasks: verbal fluency task (VFT) and color distinction task (CDT). In both tests, we observed that as the cognitive task begins [HbO2] and [HbT] increase and [HbR] decreases, after a few seconds delay. Furthermore, at the end of the tasks as subjects close their eyes in the second rest state, all three hemodynamic signals converge toward baseline ([HbO2] and [HbT] decrease and [HbR] increases). Also, the difference between hemodynamic signals at the rest state and task state was highly significant (p < 9.95e-11) in all 12 channels and in both cognitive tasks. The results confirm the ability of the designed CW-NIRS system to detect functional brain activities.

A platform technology is identified for grain handling facilities to improve grading and determine non-destructively different quality parameters of wheat. In this study, a near-infrared (NIR) hyperspectral imaging system was used to scan four wheat classes namely, Canada Western Red Spring (CWRS), Canada Prairie Spring Red (CPSR), Canada Western Hard White Spring (CWHWS), and Canada Western Soft White Spring (CWSWS) that were collected from across various growing regions in Manitoba, Saskatchewan, and Alberta in 2007, 2008, and 2009 crop years. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of four wheat classes at three moisture levels for each class was created. These image cubes were acquired in the wavelength region of 960-1700 nm with 10 nm intervals. Wheat classification was done using the non-parametric statistical and a four-layer back propagation neural network (BPNN) classifiers. Average classification accuracies of 93.1 and 83.9% for identifying wheat classes using the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), respectively, were obtained for two-class identification models that included variations of moisture levels, growing locations, and crop years of samples. In the pair-wise moisture discrimination study, near-perfect classifications were achieved for wheat samples which had difference in moisture levels of about 6%. The NIR wavelengths of 1260-1380 nm had the highest factor loadings for the first principal component using the principal components analysis (PCA). A four-layer BPNN classifier was used for two-class identification of wheat classes and moisture levels. Overall average pair-wise classification accuracies of 83.7% were obtained for discriminating wheat samples based on their moisture contents. Classification accuracies of 83.2, 75.4, 73.1%, on average, were obtained for identifying wheat classes for samples with 13, 16, and 19% moisture content (m.c.), respectively. Ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models were developed using a ten-fold cross validation for prediction. Prediction performances of PLSR and PCR models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Overall, PLSR models demonstrated better prediction performances than the PCR models for predicting protein contents and hardness of wheat.

The potential of different spectroscopic techniques for evaluating potato (Solanum tuberosum L.) quality was investigated. Spectral data in the wavelength range of 400-1750 nm were used to develop quality prediction models. The Partial Least Squares (PLS) regression was used for predicting the water content in potato samples. Water content was predicted with R2 ≥ 0.938. / A further study was conducted to find the best wavelengths for predicting water content using two methods, PLS and multiple linear regression. Wavelength ranges of 910-1020, 1129-1211, 1363-1403 nm were selected for samples without skin, while 700-900, 930-1050, 1100-1300, 1400-1550 nm were selected for samples with-skin. Weight prediction models were established using the predicted water content. / Visible spectroscopy was used for classifying shriveled and non-shriveled potatoes. The wavelength ranges best suited to such a classification were those of 442-452, 456-466, 641-651, and 684-694 nm, with accuracies as high as 94.28% and as low as 80%.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 194-200). / Microfiche. / xiv, 200 leaves, bound ill. 29 cm

Thesis (MAppSc)--University of South Australia, 2000

Thesis (MAppSc)--University of South Australia, 2000