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.

Metal nanostructures, with their extraordinary optical properties, have attracted great attention in recent years. Subwavelength-scaled metal elements, without involving array effects, have the unique ability to confine or route light at the nano-scale. In this thesis, we provide three topics relating to the manipulation of light using metal nanostructures. We first present a theory to solve the end-face reflection of a subwavelength metal stripe, which is beneficial to the design of optical resonator antennas. Subsequently, we take the advantage of the destructive interference among triple nano-slits to sharpen the focus beam in the near-field at near-infrared wavelengths, which is of interest to the study of near-field optical phase imaging and lithography. Lastly, we demonstrate a rectangular subwavelength aperture quad to convert linearly polarized radiation to a radially polarized beam, which is useful to create a deep-subwavelength focus and for optical trapping. / Graduate

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

Thesis (PhD) - Swinburne University of Technology, Faculty of Engineering and Industrial Sciences, Centre for Micro-Photonics, 2005. / A thesis submitted for the degree of Doctor of Philosophy, Centre for Micro-Photonics, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, 2005. Typescript. Bibliography: p. 129-142.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008. / Title from title screen (site viewed Mar. 10, 2009). PDF text: xv, 182 p. : ill. (some col.) ; 6 Mb. UMI publication number: AAT 3331444. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (M.S.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (January 11, 2007) Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.