Infrared microspectroscopy has been used to survey Alzheimer’s diseased brain tissue from a transgenic mouse model of the disease. Alzheimer’s disease is the leading cause of dementia among the elderly and is characterized by β-amyloid plaque deposition,neurofibrillary tangles, inflammation, and disturbed energy metabolism in the brain. Both the TgCRND8 and Tg19959 mouse models of the disease develop Alzheimer’s disease pathology beginning at approximately 3 months of age. Infrared microspectroscopy allows analysis of untreated, flash frozen tissue samples, at micron level spatial resolution, and was used in this study to examine creatine deposits in the Alzheimer’s diseased brain. Creatine is central to cellular energetics and plays an important role in proper brain function. The hippocampi of 7 pairs of transgenic mice and their littermate controls were mapped using infrared microspectroscopy and the results were analyzed for creatine levels and levels of β-sheet, indicative of the presence of β-amyloid plaques. Creatine was found to be focally elevated in the transgenic mice, as compared to their littermate controls but was not co-localized with β-amyloid plaques. Further surveys of serial sections from one transgenic mouse showed the 3-dimensional distribution of creatine within the sample. Focally elevated creatine may be a marker of the disease process, indicative of disturbed energy metabolism or inflammatory response to the disease progression.

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.

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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.

The heating curves for a number of different materials, including metals and non-metals, were observed while being irradiated with a beam from a C02 gas laser. The radiant energy spectrum emitted by the materials was measured and recorded, and from this data the heating and cooling rates of the materials were determined. Infrared measurements in the 3 to 5 micron and 8 to 14 micron regions were performed using two Barnes Spectral-Master Radiometers. Data was recorded with both stationary and moving target materials to determine the effect of airflow on the heating and cooling characteristics of the materials. The observed initial heating curve for the solid materials up to the melting point are compared with a theoretical model. The procedure for calibration of the equipment and for data collection is also contained in this thesis.These measurements are important because the experimental conditions closely approximate realistic environmental conditions for materials which are used in different types of aircraft during flight.