The tobacco growing area is an important aspect for the consistency of cigarette aroma and the control of cigarette quality. The fragrance of tobacco leaves would be different for climates and planting environments, such as soil and rainfalls. Accurately discriminating tobacco growing areas is very important to maintain the specifications of cigarettes. In this thesis, the relationship between tobacco near-infrared (NIR) spectra and growing areas is studied. Soft computing models and statistical classifiers are established, and the performance of the developed classifiers are compared in the prediction accuracy and in evaluations derived from confusion matrix. An artificial neural network (ANN) classifier and a statistical model are firstly developed. The best prediction accuracy of ANN model reaches to 79.3% in 226 training samples and 78.7% in 66 testing samples, respectively, which are 2.2% and 4.5% higher than the best results of the conventional statistical model in training (77.1%) and in testing (74.2%), respectively. A support vector machine (SVM) model is proposed to investigate the characteristics of growing areas based on risk error minimum, and produces a higher classification accuracy than ANN model does, demonstrating the effectiveness and robustness of SVM model. In addition, a genetic algorithm (GA) optimized SVM (GA-SVM) model is proposed for taking the influence of the interaction of individual inputs on the performance of classifiers into account. With the application of GA, the sensitive input subset is identified and used to discrimination models. The simulation results demonstrate that the GA-SVM model has the best performance among the other developed models, and the model complexity is simplified, which is shown by requiring fewer inputs to achieve the equivalent prediction accuracy. The GA-SVM classifier is preferred for solving multi-category problems. / Prof. Simon X. Yang

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The application of visible/near-infrared hyper-spectral analysis to monitor the quality of frying oil and fried breaded chicken nuggets was investigated. / Partial least squares (PLS) calibration models were developed to predict the acid value, total polar components and viscosity of heated oils with different ratios of hydrogenation. Coefficient of determination (R2) and root mean square error (RMSE) were calculated to assess the performance of each model. Results of the study demonstrated good prediction ability of the calibration models for the quality parameters with R2 values of over 0.92. / The second study was based on developing calibration models for prediction of moisture and fat contents of fried breaded chicken nuggets with different levels of moisture and fat contents. Performing the same procedure for evaluation of the PLS calibration models, results of the study demonstrated that moisture and fat contents of fried breaded chicken nuggets could be predicted with R2 values of 0.92.

Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.

Both behavioral and neurophysiological data indicate that many factors contribute to how infants tune to their native language(s) in early infancy. However, substantial debate remains regarding the neural mechanisms that underlie this tuning process. This study was designed to determine whether the behavioral changes in infants' processing of native and non-native speech during the second half of the first year correspond to qualitative neural processing changes that can be measured using near-infrared spectroscopy (NIRS). Specifically, we used NIRS to examine changes in hemodynamic activity in monolingually-exposed infants between the ages of 3 and 14 months while they were exposed to native (English) and non-native (Spanish) speech. In all infants, measurements were taken from the bilateral temporal regions of the cerebral cortex. Three age groups were tested: pre-tuned infants, who should show no sensitivity to phonological differences between the native and non-native speech samples (3-to-6-month-olds), actively tuning infants, who should be beginning to differentiate between the phonology of the native and non-native speech samples (7-to-10-month-olds), and tuned infants, who should readily distinguish between the phonologies of the native and non-native speech samples (11-to-14-month-olds). Results demonstrated significant differences in hemodynamic activity during the processing of native speech compared to non-native speech in each of the three age groups, with qualitatively different patterns of hemispheric lateralization emerging in response to the two types of speech in each of the three groups. These findings point to a potential neural marker of infants' sensitivity to the phonology of their native language as it emerges with increasing age that will be useful in future research.

Gamification is an emerging phenomenon that has been advocated for its potential to improve organisational outcomes. The present study aimed to examine the effect of gamification in a perceptual diagnosis task. Forty participants completed a 22-minute visual search task. To investigate the role of game mechanics participants were divided into four conditions resulting from the factorial combination of the narrative mechanic (narrative and control condition) and the points mechanic (Points and no-points control condition). Attention effort, motivation, and work engagement were measured through performance metrics, functional near-infrared spectroscopy (fNIRS), and self-report questionnaires. The results revealed points significantly increased task performance while narrative significantly increased intrinsic motivation and prefrontal oxygenation. These findings may provide much needed contributions to the literature surrounding gamification. It was also revealed that fNIRS measures of frontal activation may be a reasonable objective indicator of initial cognitive effort. This presents significant real world applications for objectively measuring motivation.

Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.

The agriculture industry experiences severe economic losses each year when wheat crops become infected with Fusarium and the mycotoxin Deoxynivalenol (DON). This research investigated the feasibility of using near infrared hyperspectral imaging to detect Fusarium damage and DON in Canadian Western Red Spring wheat. Four samples were selected from each grain grade resulting in 16 samples and 240 hyperspectral data cubes. The data cubes were calibrated to the system, the consistent spectra was found and then a 1- nearest neighbour classifier was generated. Grade percentages were computed and used to generate two 3- nearest neighbour classifiers, one for identifying Fusarium damage and the other for identifying DON content. The Fusarium damage classifier had an accuracy of 85% and the DON content classifier had an accuracy of 80%. While a single sample image classification will not replace manual testing, the use of multiple samples from one harvest could reduce manual inspections.

Ligand design plays an important role in the development and control of new coordination compounds. A new ligand architecture, Nindigo, has previously been reported and this study represents an expansion of that research to gain better insights into the attributes of this multifunctional ligand family. Mono- and bis-palladium chelates of Nindigo have been synthesized with resulting electrochemical measurements allowing for the reversible redox-active nature of the ligand set to be identified. The electronic absorption properties of these complexes were also studied. The presence of the palladium centre was found to drastically perturb the ligand centered π-π* transition resulting in significant red shifts in the absorption spectra with respect to free Nindigo. The main group coordination chemistry of Nindigo was explored by generating mono- and bis-BF2 Nindigo chelates. The electrochemical and spectral properties of these compounds were investigated with both families displaying weak emission in the NIR region. The bis-BF2 chelates were found to be sensitive in nature and decompose to the mono-BF2 chelates. In addition, heteroleptic complexes of mono-BF2 Nindigo chelates with palladium were also synthesized. The redox chemistry as well as the electronic absorption characteristics of these compounds provides a conceptual bridge between the two homologues. Homoleptic zinc and copper complexes of mono-BF2 Nindigo chelates have been synthesized. The zinc derivative serves as an “innocent” system where all redox and spectral properties are ligand centered and the oxidation states of both the metal and surrounding ligands can be assigned. The copper complexes exhibit more diverse chemistry with the redox and electronic absorption properties differing dramatically from the zinc system. A combination of EPR, XPS and computational analysis suggests the copper systems to be non-innocent in nature. In addition to the bis-bidentate anionic Nindigo ligand system, the fully oxidized neutral analogue has also been synthesized. DehydroNindigo exhibits significantly different chemical behaviour from Nindigo. Bridged ruthenium dimers have been synthesized that are obtained as two isomers, cis and trans (with respect to the bridging ligand). Both isomers exhibit rich electrochemical behaviour. The mixed valence states of both species are found, electrochemically, to be extremely stable with respect to disproportionation. / Graduate / 0485 / 0488 / gnawn@uvic.ca

The application of hyperspectral imaging technique in the wavelength range of 400-1000 nm to estimate some of the maturity parameters of mangoes was investigated. Mangoes with different quality levels were grouped using principle component analysis (PCA). Feature wavelengths were identified to predict total soluble solids content, water content and firmness using simple correlation, first derivative, partial least square (PLS) regression analysis and measured values. Calibration models were developed using the selected wavelengths from correlation coefficients, first derivative, partial least square (PLS) regression analysis and corresponding maturity parameters employing artificial neural network model to predict total soluble solids content, water content and firmness of the fruit. Performance of the models was compared using the correlation coefficient (r) values. Fruit firmness was predicted with high correlation coefficient (r=0.88) followed by water content (r=0.81) and total soluble solids (r=0.78) using wavelengths selected from simple correlation of first derivative data with the parameters and ANN model. The results of the study demonstrated the scope for further research on maturity and quality evaluation of fruits using hyperspectral imaging technique.