No description available.

O principal parâmetro de avaliação da qualidade de carvões para uso na geração de energia termoelétrica é o percentual de cinzas. Em uma planta de beneficiamento, saber o percentual de cinzas da alimentação, concentrado e rejeito é de suma importância para os ajustes nos equipamentos de concentração. No caso do carvão da camada I1 do depósito mineral do Leão a janela de tempo entre o ponto mais adequado para amostragem e o fim da campanha de beneficiamento não propicia que se conheça o teor de cinzas da alimentação do processo de beneficiamento. Em virtude disso, este trabalho tem como objetivo de fornecer uma estimativa confiável do teor de cinzas da alimentação para propiciar a aplicação na planta de beneficiamento. Para esse intento utilizou-se a teoria de Kubelka-Munk que por meio de sua expressão matemática transforma a reflectância em uma grandeza aditiva que propicia a estimativa das porcentagens de matéria orgânica e mineral contida no carvão devido a diferença de reflectância entre esses materiais. Como resultado final foram gerados diversos modelos com intuito de verificar qual deles se adaptava melhor aos dados. Por fim, foi possível concluir que há possibilidade de estimar o teor de cinzas de carvão por meio da reflectância e com confiabilidade de 96,78% que toda a leitura agrega um erro máximo de ± 5% para o melhor modelo. / The main parameter in quality evaluation of coals employed in power generation is the ash content. In a beneficiation plant, knowing the ash content of the feed, concentrate an tailings is mandatory to set up the concentration equipment. In the case of the I1 coal seam of Leão’s deposit the time window between sampling routine and beneficiation routine does not allow to know the feed ash content before the beneficiation process. By this reason, this work aims to provide a reliable estimative of the feed ash content to propitiate its application in the beneficiation plant. For this purpose it was used Kubelka-Munk’s theory that, by using a mathematic expression, transforms reflectance into a additive quantity which allows to estimate mineral and organic matter present in coal by the difference of reflectance between these materials. As result, it was generated several models in order to analyze which one is better suited to the data. Finally, as a conclusion, it is possible to infer the ash content of coal by reflectance with confidence of 96,78% that all measure brings up a maximum error of ± 5% for the best model.

O principal parâmetro de avaliação da qualidade de carvões para uso na geração de energia termoelétrica é o percentual de cinzas. Em uma planta de beneficiamento, saber o percentual de cinzas da alimentação, concentrado e rejeito é de suma importância para os ajustes nos equipamentos de concentração. No caso do carvão da camada I1 do depósito mineral do Leão a janela de tempo entre o ponto mais adequado para amostragem e o fim da campanha de beneficiamento não propicia que se conheça o teor de cinzas da alimentação do processo de beneficiamento. Em virtude disso, este trabalho tem como objetivo de fornecer uma estimativa confiável do teor de cinzas da alimentação para propiciar a aplicação na planta de beneficiamento. Para esse intento utilizou-se a teoria de Kubelka-Munk que por meio de sua expressão matemática transforma a reflectância em uma grandeza aditiva que propicia a estimativa das porcentagens de matéria orgânica e mineral contida no carvão devido a diferença de reflectância entre esses materiais. Como resultado final foram gerados diversos modelos com intuito de verificar qual deles se adaptava melhor aos dados. Por fim, foi possível concluir que há possibilidade de estimar o teor de cinzas de carvão por meio da reflectância e com confiabilidade de 96,78% que toda a leitura agrega um erro máximo de ± 5% para o melhor modelo. / The main parameter in quality evaluation of coals employed in power generation is the ash content. In a beneficiation plant, knowing the ash content of the feed, concentrate an tailings is mandatory to set up the concentration equipment. In the case of the I1 coal seam of Leão’s deposit the time window between sampling routine and beneficiation routine does not allow to know the feed ash content before the beneficiation process. By this reason, this work aims to provide a reliable estimative of the feed ash content to propitiate its application in the beneficiation plant. For this purpose it was used Kubelka-Munk’s theory that, by using a mathematic expression, transforms reflectance into a additive quantity which allows to estimate mineral and organic matter present in coal by the difference of reflectance between these materials. As result, it was generated several models in order to analyze which one is better suited to the data. Finally, as a conclusion, it is possible to infer the ash content of coal by reflectance with confidence of 96,78% that all measure brings up a maximum error of ± 5% for the best model.

O principal parâmetro de avaliação da qualidade de carvões para uso na geração de energia termoelétrica é o percentual de cinzas. Em uma planta de beneficiamento, saber o percentual de cinzas da alimentação, concentrado e rejeito é de suma importância para os ajustes nos equipamentos de concentração. No caso do carvão da camada I1 do depósito mineral do Leão a janela de tempo entre o ponto mais adequado para amostragem e o fim da campanha de beneficiamento não propicia que se conheça o teor de cinzas da alimentação do processo de beneficiamento. Em virtude disso, este trabalho tem como objetivo de fornecer uma estimativa confiável do teor de cinzas da alimentação para propiciar a aplicação na planta de beneficiamento. Para esse intento utilizou-se a teoria de Kubelka-Munk que por meio de sua expressão matemática transforma a reflectância em uma grandeza aditiva que propicia a estimativa das porcentagens de matéria orgânica e mineral contida no carvão devido a diferença de reflectância entre esses materiais. Como resultado final foram gerados diversos modelos com intuito de verificar qual deles se adaptava melhor aos dados. Por fim, foi possível concluir que há possibilidade de estimar o teor de cinzas de carvão por meio da reflectância e com confiabilidade de 96,78% que toda a leitura agrega um erro máximo de ± 5% para o melhor modelo. / The main parameter in quality evaluation of coals employed in power generation is the ash content. In a beneficiation plant, knowing the ash content of the feed, concentrate an tailings is mandatory to set up the concentration equipment. In the case of the I1 coal seam of Leão’s deposit the time window between sampling routine and beneficiation routine does not allow to know the feed ash content before the beneficiation process. By this reason, this work aims to provide a reliable estimative of the feed ash content to propitiate its application in the beneficiation plant. For this purpose it was used Kubelka-Munk’s theory that, by using a mathematic expression, transforms reflectance into a additive quantity which allows to estimate mineral and organic matter present in coal by the difference of reflectance between these materials. As result, it was generated several models in order to analyze which one is better suited to the data. Finally, as a conclusion, it is possible to infer the ash content of coal by reflectance with confidence of 96,78% that all measure brings up a maximum error of ± 5% for the best model.

Melanomas account for more than 1.7% of global cancer diagnoses and about 1% of all skin cancer diagnoses in the United States. This type of cancer occurs in the melanin-producing cells in the epidermis and exhibits distinctive variations in melanin and blood concentration values in the form of skin lesions. The current approach for evaluating skin cancer lesions involves visual inspection with a dermatoscope, typically followed by biopsy and histopathological analysis. However, this process, to decrease the risk of misdiagnosis, results in unnecessary biopsies, contributing to the emotional and financial distress of patients. The implementation of a non-invasive imaging technique to aid the analysis of skin lesions in the early stages can potentially mitigate these consequences. Hyperspectral imaging (HSI) has shown promise as a non-invasive technique to analyze skin lesions. Images taken of human skin using a hyperspectral camera are a result of numerous elements in the skin. Being a turbid, inhomogeneous material, the skin has chromophores and scattering agents, which interact with light and produce characteristic back-scattered energy that can be harnessed and examined with an HSI camera. In this study, a mathematical model of the skin is used to extract meaningful information from the hyperspectral data in the form of melanin concentration, blood volume fraction and blood oxygen saturation in the skin. The human skin is modelled as a bi-layer planar system, whose surface reflectance is theoretically calculated using the Kubelka-Munk theory and absorption laws by Beer and Lambert. Hyperspectral images of the dorsal portion of three volunteer subjects' hands 400 - 1000 nm range, were used to estimate the contributing parameters. The mean and standard deviation of these estimates are reported compared with theoretical values from the literature. The model is also evaluated for its sensitivity with respect to these parameters, and then fitted to measured hyperspectral data of three volunteer subjects in different conditions. The wavelengths and wavelength groups which were identified to result in the maximum change in percentage reflectance calculated from the model were 450 and 660 nm for melanin, 500 - 520 nm and 590 - 625 nm for blood volume fraction and 606, 646 and 750 nm for blood oxygen saturation. / Master of Science / Melanoma, the most serious type of skin cancer, develops in the melanin-producing cells in the epidermis. A characteristic marker of skin lesions is the abrupt variations in melanin and blood concentration in areas of the lesion. The present technique to inspect skin cancer lesions involves dermatoscopy, which is a qualitative visual analysis of the lesion's features using a few standardized techniques such as the 7-point checklist and the ABCDE rule. Typically, dermatoscopy is followed by a biopsy and then a histopathological analysis of the biopsy. To reduce the possibility of misdiagnosing actual melanomas, a considerable number of dermoscopically unclear lesions are biopsied, increasing emotional, financial, and medical consequences. A non-invasive imaging technique to analyze skin lesions during the dermoscopic stage can help alleviate some of these consequences. Hyperspectral imaging (HSI) is a promising methodology to non-invasively analyze skin lesions. Images taken of human skin using a hyperspectral camera are a result of numerous elements in the skin. Being a turbid, inhomogeneous material, the skin has chromophores and scattering agents, which interact with light and produce characteristic back-scattered energy that can be harnessed and analyzed with an HSI camera. In this study, a mathematical model of the skin is used to extract meaningful information from the hyperspectral data in the form of melanin concentration, blood volume fraction and blood oxygen saturation. The mean and standard deviation of these estimates are reported compared with theoretical values from the literature. The model is also evaluated for its sensitivity with respect to these parameters, and then fitted to measured hyperspectral data of six volunteer subjects in different conditions. Wavelengths which capture the most influential changes in the model response are identified to be 450 and 660 nm for melanin, 500 - 520 nm and 590 - 625 nm for blood volume fraction and 606, 646 and 750 nm for blood oxygen saturation.

No description available.

Plant-based foods are considered to be more sustainable food source than animal-based foods due to their lower environmental impact. However, there is a challenge in producing plant-based foods with similar taste, texture, and appearance to animal-based products to meet consumers expectations. The appearance, which includes color, is the first sensory attribute consumers experience of foods, and so it influences consumers’ buying decisions. Food colorants are added to match the color of plant-based foods with animal-based ones. The food colorant choice is shifting toward natural colorants due to their perceived health benefits and clean-label aspects. Therefore, this study aimed to optimize the optical properties of plant-based foods using natural food colorants. Initially, the impact of natural pigments that selectively absorb light (turmeric, red beet, and butterfly pea flower) and colloidal particles that selectively scatter light (oil droplets) on the optical properties of model plant-based oil-in-water emulsions was investigated. It was found the pigment and oil droplet characteristics, including pigment type and concentration, and droplet size and concentration, could be adjusted to modulate the chromaticness or lightness of emulsions by altering light absorption and scattering effects. This knowledge is useful to optimizing the appearance of plant-based foods by adjusting these parameters. Then, natural pigments with three primary colors (yellow, red, and blue) were blended to mimic the color of animal-based products (raw meat, cooked meats, egg, and cheese). A color matching model developed from Kubelka-Munk and color theories was then used to determine the optimum ratio of each colorant needed to match the spectral reflectance of animal-based products. The methodology developed was able to optimize the color of model plant-based emulsions to that of different food matrices. The improved understanding of the optical properties of natural pigments and color matching theory developed in this study should help to formulate plant-based foods with more similar appearances to animal-based products, thereby increasing consumers’ acceptance of plant-based foods.

Melanomas account for more than 1.7% of global cancer diagnoses and about 1% of all skin cancer diagnoses in the United States. This type of cancer occurs in the melanin-producing cells in the epidermis and exhibits distinctive variations in melanin and blood concentration values in the form of skin lesions. The current approach for evaluating skin cancer lesions involves visual inspection with a dermatoscope, typically followed by biopsy and histopathological analysis. However, to decrease the risk of misdiagnosis in this process requires invasive biopsies, contributing to the emotional and financial distress of patients. The implementation of a non-invasive imaging technique to aid the analysis of skin lesions in the early stages can potentially mitigate these consequences. Hyperspectral imaging (HSI) has shown promise as a non-invasive technique to analyze skin lesions. Images taken of human skin using a hyperspectral camera are a result of numerous elements in the skin. Being a turbid, inhomogeneous material, the skin has chromophores and scattering agents, which interact with light and produce characteristic back-scattered energy that can be harnessed and examined with an HSI camera. To achieve this in this study, a mathematical model of the skin is used to extract meaningful information from the hyperspectral data in the form of parameters such as melanin concentration, blood volume fraction and blood oxygen saturation in the skin. The human skin is modelled as a bi-layer planar system, whose surface reflectance is theoretically calculated using the Kubelka-Munk theory and absorption laws by Beer and Lambert. The model is evaluated for its sensitivity to the parameters and then fitted to measured hyperspectral data of four volunteer subjects in different conditions. Mean values of melanin, blood volume fraction and oxygen saturation obtained for each of the subjects are reported and compared with theoretical values from literature. Sensitivity analysis revealed wavelengths and wavelength groups which resulted in maximum change in percentage reflectance calculated from the model were 450 and 660 nm for melanin, 500 - 520 nm and 590 - 625 nm for blood volume fraction and 606, 646 and 750 nm for blood oxygen saturation. / Master of Science / Melanoma, the most serious type of skin cancer, develops in the melanin-producing cells in the epidermis. A characteristic marker of skin lesions is the abrupt variations in melanin and blood concentration in areas of the lesion. The present technique to inspect skin cancer lesions involves dermatoscopy, which is a qualitative visual analysis of the lesion’s features using a few standardized techniques such as the 7-point checklist and the ABCDE rule. Typically, dermatoscopy is followed by a biopsy and then a histopathological analysis of the biopsy. To reduce the possibility of misdiagnosing actual melanomas, a considerable number of dermoscopically unclear lesions are biopsied, increasing emotional, financial, and medical consequences. A non-invasive imaging technique to analyze skin lesions during the dermoscopic stage can help alleviate some of these consequences. Hyperspectral imaging (HSI) is a promising methodology to non-invasively analyze skin lesions. Images taken of human skin using a hyperspectral camera are a result of numerous elements in the skin. Being a turbid, inhomogeneous material, the skin has chromophores and scattering agents, which interact with light and produce characteristic back-scattered energy that can be harnessed and analyzed with an HSI camera. In this study, a mathematical model of the skin is used to extract meaningful information from the hyperspectral data in the form of melanin concentration, blood volume fraction and blood oxygen saturation. The mean and standard deviation of these estimates are reported and compared with theoretical values from the literature. The model is also evaluated for its sensitivity with respect to these parameters to identify the most relevant wavelengths.