Abstract. The goal of this case study is to further the understanding of the social and economic structure, such as trade routes and/or prehistoric man’s movement, during prehistory in the inland of Västerbotten, Sweden. This is achieved by studying the sets of lithic tools found in six archaeological sites (RAÄ 977:1 Vilhelmina, RAÄ 553:1 Vilhelmina, RAÄ 132:1 Vilhelmina, RAÄ 519:1 Vilhelmina, RAÄ 399:1 Vilhelmina och RAÄ 129:1 Åsele) using NIR-spectroscopic (Near InfraRed-spectroscopy), statistical and archaeological methods. By using PCA-models (Principal Component Analysis-models) and the classification method SIMCA (Soft Independent Modelling of Class Analogies) on NIR-spectroscopic data collected over the course of this study, it was possible to show signs that prehistoric man in the studied area, might have deposited quartzite materials, not naturally occurring at the RAÄ 519:1 Vilhelmina, Sweden, site. Four geographic areas (the vicinity of the sea Vojm, the North and South part of the sea Malgomaj and the vicinity of the Southwest part of the Ångerman river in the studied area) could also be shown to display distinct patterns in the PCA-models, related to the use of particular combinations of quartzites. These findings lead to the conclusion that prehistoric man in this area, typically used locally available materials for toolmaking. Furthermore, this study resulted in a large NIR-spectroscopic dataset from the archaeological sites that makes up the main material for this study, that may be beneficial to future NIR-spectroscopic studies in archaeology and/or further studies of NIR-spectroscopy applied to lithic materials.

Mikroplastik (1–1000 µm) kommt praktisch überall in der Umwelt vor, aber immer noch ist die Iden-tifizierung und Quantifizierung eine anspruchsvolle und zeitintensive Aufgabe. Erste analytisch Metho¬den beginnen sich zu etablieren, jedoch sind die benötigten Instrumente komplex und der Probendurchsatz für Routineuntersuchungen in den meisten Fällen noch zu gering. Diese Arbeit widmet sich zunächst dem Potenzial der Nahinfrarot (NIR)-Spektroskopie diese Lücke zu schließen. Exemplarisch wird ein günstiges Verfahren mit großem Probendurchsatz zur Bestimmung von Mikro¬plastik-Gesamtgehalten der verbreiteten Verpackungskunststoffe Polyethylen (PE), Polystyrol (PS) und Polypropylen (PP) in Böden und Kompost entwickelt. Neben der Untersuchung von Mikroplastik-Gesamtgehalten einer Probe ist auch die Charakterisierung individueller Partikel von großer Bedeutung. Die bildgebende Fourier-Transform-Infrarot (FTIR)-Mikrospektroskopie ist hierfür sehr gut geeignet. Allerdings ist es eine Herausforderung Mikroplastik in den aus mehreren Million Spektren bestehenden hyperspektralen Bildern zu identifizieren. Eine schnelle und zuverlässige Mikroplastikerkennung wird hier durch eine explorative Analyse und automatisierte Klassifizierung der Spektren erreicht. Zusammenfassend zeigt diese Arbeit, dass die optische Spektroskopie im mittleren und nahen Infrarot über ihre bisherige Anwendung hinaus ein großes Potenzial besitzen, die Mikroplastik-Analytik kostengünstiger, einfacher und schneller zu gestalten. / Microplastics (1-1000 µm) are ubiquitous in the environment, but their identification and quantification is still a challenging and time-consuming task. The first established methods require complex instruments and the sample throughput is still too low for routine analysis in most cases. This work first addresses the potential of near-infrared (NIR) spectroscopy to fill this gap. A low-cost method with large sample throughput is developed for the determination of total microplastic contents of the common packaging plastics polyethylene (PE), polystyrene (PS) and polypropylene (PP) in soils and compost. In addition to the investigation of total microplastic levels in a sample, the characterization of individual particles is also of great importance. Fourier transform infrared (FTIR) imaging microspectroscopy is well suited for this purpose. However, it is challenging to identify microplastics in hyperspectral images consisting of several million spectra. Fast and reliable microplastic detection is achieved by exploratory analysis and automated classification of the spectra. In summary, this work shows that mid- and near-infrared optical spectroscopy have great potential beyond their current application to make microplastics analysis cheaper, easier, and faster.

This project utilised a novel solvent-free continuous cocrystallisation (SFCC) method to manufacture pharmaceutical co-crystals. The objectives were to optimize the process towards achieving high co-crystal yields and to understand the behaviour of co-crystals under different conditions. Particular attention was paid to the development of near infrared (NIR) spectroscopy as a process analytical technology (PAT). Twin screw, hot melt extrusion was the base technique of the SFCC process. Changing parameters such as temperature, screw speed and screw geometry was important for improving the co-crystal yield. The level of mixing and shear was directly influenced by the screw geometry, whilst the screw speed was an important parameter for controlling the residence time of the material during hot melt extrusion. Ibuprofen – nicotinamide 1:1 cocrystals and carbamazepine – nicotinamide 1:1 co-crystals were successfully manufactured using the SFCC method. Characterisation techniques were important for this project, and NIR spectroscopy proved to be a convenient, accurate analytical technique for identifying the formation of co-crystals along the extruder barrel. Separate thermal and model shear deformation studies were also carried out to determine the effect of temperature and shear on co-crystal formation for several different pharmaceutical co-crystal pairs. Finally, NIR spectroscopy was used to create two partial least squares regression models, for predicting the 1:1 co-crystal yield of ibuprofen – nicotinamide and carbamazepine – nicotinamide, when in a powder mixture with the respective pure API. It is believed that the prediction models created in this project can be used to facilitate future in-line PAT studies of pharmaceutical co-crystals during different manufacturing processes.

This thesis presents a number of studies, where a multivariate approach was taken to construct models that predict wood species and thermo mechanical pulp brightness from roundwood of Norway spruce and Scots pine. The first and second studies produced multivariate prediction models for wood species from the bark of spruce and pine. These models can be used for wood species classification and would replace the manual log assessment that takes place today. Principal Component Analysis, PCA, and Partial least squares projections to Latent Structures, PLS, were used to predict the wood species from multivariate measurements recorded from the bark of spruce and pine. Two different kinds of measurements were employed, near-infrared spectroscopy and digital imaging. Both methods showed that it was possible to predict the wood species with a high accuracy. The third and fourth studies of the thesis are related to the wood storage of roundwood and the deterioration of wood that occurs during the storage. The third study used an experimental design with five storage factors that provided different conditions for the analysed wood. The experimental design made it possible to identify the factors and the interaction between factors, which were important for the ISO brightness of peroxide and dithionite bleached thermo mechanical pulp, TMP. The final study of the thesis used NIR spectroscopy for predicting the ISO brightness of bleached TMP. Spectra recorded from stored wood were used to construct PLS prediction models.

Made available in DSpace on 2015-05-14T13:21:38Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 6875549 bytes, checksum: 3697064e0b5c3d3ac90181f954575bc7 (MD5) Previous issue date: 2013-06-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Tea has an economic and cultural importance, not only for producers and consumers, but also for a scientific interest. The organoleptic quality of the Camellia sinensis infusion depends on the nature and amount of several secondary metabolites (such as polyphenols, caffeine, amino acids, etc.), which can be directly related to the geographical origin of the tea plants. These components are the basis of the economic value of teas and its beneficial effects on human health. Therefore, there is a growing consumer s interest in high quality teas with a distinct geographical identity. In last decades, the analytical methods employing modern instrumental techniques have become more sensitive, reliable and fast. However, these techniques have advantages and limitations for the application in the analyses of the tea quality and their geographic origins. Thus, a combination of different techniques could be more useful than relying on a single method. Following these principles, we propose three new strategies for simultaneous classification of teas according to both the type (green and black) and geographic origin (Argentina, Brazil and Sri Lanka). The proposed methodologies employ the use of (1) digital images, (2) NIR spectroscopy, and (3) chemical composition (moisture, ash, caffeine, total polyphenols, fluoride and fifteen metals (Na, Mg, Al, P, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd and Pb) in both tea leaves and infusions). A correct classification of all tea samples (100% of correct classification) was always obtained using the Linear Discriminant Analysis associated with the variable selection technique taken by the Successive Projections Algorithm. Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were also used. The proposed strategies might be useful for the development of legislation for the quality control of teas in Brazil, which is still lacking / O chá tem uma importância econômica e cultural, não só para produtores e consumidores, mas também por um interesse científico. A qualidade organoléptica da infusão da Camellia sinensis depende da natureza e da quantidade de vários metabólitos secundários (tais como polifenóis, cafeína, aminoácidos, etc.), os quais podem ser relacionados diretamente com a origem geográfica das plantas. Estes componentes são a base do valor econômico do chá e de seus efeitos benéficos sobre a saúde humana. Por isso, há um crescente interesse dos consumidores por chás de alta qualidade com uma clara identidade geográfica. Durante as últimas décadas, as metodologias analíticas que empregam técnicas instrumentais modernas tornaram-se mais sensíveis, confiáveis e rápidas. Entretanto, tais técnicas têm vantagens e limitações para a aplicação da análise da qualidade do chá e de suas origens geográficas. Assim, uma combinação de diferentes técnicas analíticas pode ser mais útil do que depender de um único método. Seguindo estes preceitos, nós propusemos três novas estratégias para a classificação simultânea de chás de acordo com o tipo (verde e preto) e a origem geográfica (Argentina, Brasil e Sri Lanka). As metodologias propostas empregam o uso de (1) imagens digitais, (2) espectroscopia NIR e (3) composição química (umidade, cinza total, cafeína, polifenóis totais, fluoreto e quinze metais (Na, Mg, Al, P, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd e Pb) nas folhas e infusões dos chás). Uma classificação correta de todas as amostras de chás (100% de acerto) foi sempre obtida utilizando Análise Discriminante Linear associada à técnica de seleção de variáveis feita pelo Algoritmo das Projeções Sucessivas (SPA-LDA). Modelagem Independente e Flexível por Analogia de Classe (SIMCA) e Análise Discriminante por Mínimos Quadrados Parciais (PLS-DA) também foram utilizadas. Tais estratégias podem ser úteis para a elaboração de normas para o controle de qualidade de chás no Brasil, que ainda é inexistente

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-09-02T13:38:23Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Rafaella de Figueiredo Sales.pdf: 2788155 bytes, checksum: 95e4d0c2ca7494069abdc945b186152e (MD5) / Made available in DSpace on 2016-09-02T13:38:23Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Rafaella de Figueiredo Sales.pdf: 2788155 bytes, checksum: 95e4d0c2ca7494069abdc945b186152e (MD5) Previous issue date: 2016-02-19 / PETROBRÁS / PRH - 28 / O biodiesel é considerado um potencial substituto para os combustíveis de origem fóssil. Geralmente, esse biocombustível é produzido através da transesterificação de óleos vegetais ou gordura animal utilizando um álcool de cadeia curta. A vasta aplicação industrial da transesterificação para produção de biodiesel requer um cuidadoso monitoramento do processo e modelagem dessa reação, orientados para um melhor entendimento e para a otimização do processo. Além disso, o controle do processo é de fundamental importância para garantir a qualidade do biodiesel de forma constante, uniforme e atendendo às especificações necessárias para a sua comercialização. Dentre as técnicas existentes para o monitoramento de reações, a espectroscopia no infravermelho próximo permite uma análise rápida e não destrutiva, podendo ser utilizada para o monitoramento in-line de processos. Nesse contexto, o presente trabalho descreve o monitoramento in-line da produção de biodiesel por transesterificação de óleo de soja com metanol utilizando a espectroscopia na região do infravermelho próximo. As reações foram conduzidas em batelada com controle de temperatura e velocidade de agitação. Dois estudos diferentes foram então realizados a partir dos dados espectroscópicos coletados ao longo das reações. A primeira abordagem consistiu na implementação de estratégias de controle estatístico multivariado e de qualidade, baseadas em métodos de projeção multivariada. Nesse estudo, modelos de referência baseados em dois métodos diferentes (tempo real e após o término da batelada) foram desenvolvidos usando bateladas sob condições normais de operação (concentração de catalisador NaOH de 0,75% em massa em relação à massa de óleo de soja, temperatura de 55°C e velocidade de agitação de 500 rpm). Foram avaliadas diferentes técnicas de pré-processamento espectral, bem como estratégias para desdobramento da matriz de dados espectrais. Posteriormente, bateladas submetidas a diferentes perturbações (relacionadas à adição de água e a mudanças na temperatura, na velocidade de agitação, na concentração de catalisador e na matéria prima utilizada) foram produzidas para avaliar o desempenho dos diferentes modelos em relação às suas capacidades de detectar falhas de operação. De um modo geral, as cartas de controle desenvolvidas foram capazes de detectar a maioria das falhas intencionalmente produzidas ao longo do processo. Apenas pequenas perturbações na velocidade de agitação não foram identificadas. A segunda abordagem baseou-se no estudo do comportamento cinético da metanólise do óleo de soja em diferentes condições de temperatura (20, 45 e 55°C) e de concentração de catalisador (0,75 e 1,0% m/m de NaOH). Nesse estudo, a modelagem cinética foi realizada a partir dos perfis de concentração do éster metílico estimados utilizando o método de Resolução multivariada de curvas com mínimos quadrados alternantes com restrição de correlação. Os perfis de concentração obtidos permitiram o desenvolvimento de um estudo cinético simplificado da reação, a qual foi considerada como seguindo uma cinética de pseudo-primeira ordem baseada na concentração do éster metílico para a reação global de transesterificação. O modelo cinético incluiu apenas o regime pseudo-homogêneo, em que é observada uma rápida taxa de reação e a cinética está controlada pela reação química. Para esse regime, obteve-se uma energia de ativação de 32,29 kJ.mol-1, com base na equação de Arrhenius. / Biodiesel is considered a potential substitute for fossil fuels. In general, this biofuel is produced by transesterification of vegetable oils or animal fats using a short-chained alcohol. The wide industrial application of transesterification for biodiesel production requires a thorough process monitoring and modeling of this reaction, oriented to a better understanding and optimization of the process. Moreover, process control is of utmost importance to ensure in a constant and uniform way the biodiesel quality in order to meet commercialization requirements. Among the existing techniques applied for reaction monitoring, near infrared spectroscopy allows a fast and non-destructive analysis, being able to be used for in-line process monitoring. In this context, the present work describes the in-line monitoring of biodiesel production by transesterification of soybean oil with methanol using near infrared spectroscopy. Reactions were carried out in batch reactors with temperature and agitation speed control. Two different studies were then carried out from spectroscopic data collected throughout the reactions. The first approach consisted of the implementation of multivariate statistic and quality control strategies, based on multivariate projection methods. In this study, reference models based on two different methods (real time and end of batch) were developed using batches under normal operating condition (0.75 w/w% of catalyst NaOH with respect to the amount of oil, temperature of 55ºC and stirring speed of 500 rpm). Different techniques for pre-processing and unfolding the spectral data were evaluated. Afterwards, batches subjected to different disturbances (related with water addition and changes in the temperature, agitation speed, catalyst content and raw material used) were manufactured to assess the performance of the different models in terms of their capability for fault detection. In general, the control charts developed were able to detect most of the failures intentionally produced during the batch runs. Only small variations in the stirring spend were not detected. The second approach was based on the study of the kinetic behavior of the soybean oil methanolysiscarried out with different temperatures (20, 44 and 55°C) and catalyst (NaOH) concentrations (0.75 and 1.0 w/w% based onoil weight). In this study, the kinetic modelling was developed from methyl ester concentration profiles estimated by Multivariate curve resolution alternating least squares with correlation constraint. The obtained concentration profiles allowed the development of a simplified kinetic model of the reaction, which was considered to follow a pseudo-first order overall kinetics based on methyl ester concentration for global transesterification reaction. The kinetic model included only the pseudo-homogeneous regime where the overall process kinetics is under chemical reaction control and a fast methanolysis rate is observed. For this regime, the activation energy was 32.29 kJ.mol-1, based on Arrhenius equation.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-09-02T14:13:49Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Rafaella de Figueiredo Sales.pdf: 2788155 bytes, checksum: 95e4d0c2ca7494069abdc945b186152e (MD5) / Made available in DSpace on 2016-09-02T14:13:49Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Rafaella de Figueiredo Sales.pdf: 2788155 bytes, checksum: 95e4d0c2ca7494069abdc945b186152e (MD5) Previous issue date: 2016-02-19 / PETROBRAS/ PRH 28 / O biodiesel é considerado um potencial substituto para os combustíveis de origem fóssil. Geralmente, esse biocombustível é produzido através da transesterificação de óleos vegetais ou gordura animal utilizando um álcool de cadeia curta. A vasta aplicação industrial da transesterificação para produção de biodiesel requer um cuidadoso monitoramento do processo e modelagem dessa reação, orientados para um melhor entendimento e para a otimização do processo. Além disso, o controle do processo é de fundamental importância para garantir a qualidade do biodiesel de forma constante, uniforme e atendendo às especificações necessárias para a sua comercialização. Dentre as técnicas existentes para o monitoramento de reações, a espectroscopia no infravermelho próximo permite uma análise rápida e não destrutiva, podendo ser utilizada para o monitoramento in-line de processos. Nesse contexto, o presente trabalho descreve o monitoramento in-line da produção de biodiesel por transesterificação de óleo de soja com metanol utilizando a espectroscopia na região do infravermelho próximo. As reações foram conduzidas em batelada com controle de temperatura e velocidade de agitação. Dois estudos diferentes foram então realizados a partir dos dados espectroscópicos coletados ao longo das reações. A primeira abordagem consistiu na implementação de estratégias de controle estatístico multivariado e de qualidade, baseadas em métodos de projeção multivariada. Nesse estudo, modelos de referência baseados em dois métodos diferentes (tempo real e após o término da batelada) foram desenvolvidos usando bateladas sob condições normais de operação (concentração de catalisador NaOH de 0,75% em massa em relação à massa de óleo de soja, temperatura de 55°C e velocidade de agitação de 500 rpm). Foram avaliadas diferentes técnicas de pré-processamento espectral, bem como estratégias para desdobramento da matriz de dados espectrais. Posteriormente, bateladas submetidas a diferentes perturbações (relacionadas à adição de água e a mudanças na temperatura, na velocidade de agitação, na concentração de catalisador e na matéria prima utilizada) foram produzidas para avaliar o desempenho dos diferentes modelos em relação às suas capacidades de detectar falhas de operação. De um modo geral, as cartas de controle desenvolvidas foram capazes de detectar a maioria das falhas intencionalmente produzidas ao longo do processo. Apenas pequenas perturbações na velocidade de agitação não foram identificadas. A segunda abordagem baseou-se no estudo do comportamento cinético da metanólise do óleo de soja em diferentes condições de temperatura (20, 45 e 55°C) e de concentração de catalisador (0,75 e 1,0% m/m de NaOH). Nesse estudo, a modelagem cinética foi realizada a partir dos perfis de concentração do éster metílico estimados utilizando o método de Resolução multivariada de curvas com mínimos quadrados alternantes com restrição de correlação. Os perfis de concentração obtidos permitiram o desenvolvimento de um estudo cinético simplificado da reação, a qual foi considerada como seguindo uma cinética de pseudo-primeira ordem baseada na concentração do éster metílico para a reação global de transesterificação. O modelo cinético incluiu apenas o regime pseudo-homogêneo, em que é observada uma rápida taxa de reação e a cinética está controlada pela reação química. Para esse regime, obteve-se uma energia de ativação de 32,29 kJ.mol-1, com base na equação de Arrhenius. / Biodiesel is considered a potential substitute for fossil fuels. In general, this biofuel is produced by transesterification of vegetable oils or animal fats using a short-chained alcohol. The wide industrial application of transesterification for biodiesel production requires a thorough process monitoring and modeling of this reaction, oriented to a better understanding and optimization of the process. Moreover, process control is of utmost importance to ensure in a constant and uniform way the biodiesel quality in order to meet commercialization requirements. Among the existing techniques applied for reaction monitoring, near infrared spectroscopy allows a fast and non-destructive analysis, being able to be used for in-line process monitoring. In this context, the present work describes the in-line monitoring of biodiesel production by transesterification of soybean oil with methanol using near infrared spectroscopy. Reactions were carried out in batch reactors with temperature and agitation speed control. Two different studies were then carried out from spectroscopic data collected throughout the reactions. The first approach consisted of the implementation of multivariate statistic and quality control strategies, based on multivariate projection methods. In this study, reference models based on two different methods (real time and end of batch) were developed using batches under normal operating condition (0.75 w/w% of catalyst NaOH with respect to the amount of oil, temperature of 55ºC and stirring speed of 500 rpm). Different techniques for pre-processing and unfolding the spectral data were evaluated. Afterwards, batches subjected to different disturbances (related with water addition and changes in the temperature, agitation speed, catalyst content and raw material used) were manufactured to assess the performance of the different models in terms of their capability for fault detection. In general, the control charts developed were able to detect most of the failures intentionally produced during the batch runs. Only small variations in the stirring spend were not detected. The second approach was based on the study of the kinetic behavior of the soybean oil methanolysiscarried out with different temperatures (20, 44 and 55°C) and catalyst (NaOH) concentrations (0.75 and 1.0 w/w% based onoil weight). In this study, the kinetic modelling was developed from methyl ester concentration profiles estimated by Multivariate curve resolution alternating least squares with correlation constraint. The obtained concentration profiles allowed the development of a simplified kinetic model of the reaction, which was considered to follow a pseudo-first order overall kinetics based on methyl ester concentration for global transesterification reaction. The kinetic model included only the pseudo-homogeneous regime where the overall process kinetics is under chemical reaction control and a fast methanolysis rate is observed. For this regime, the activation energy was 32.29 kJ.mol-1, based on Arrhenius equation.

Orientadores: Celio Pasquini, Mario Cesar Ugulino de Araujo / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-09T13:24:36Z (GMT). No. of bitstreams: 1 DantasFilho_HeronidesAdonias_D.pdf: 2337564 bytes, checksum: b5a44bf3eec3ce95ab683c5b2621b012 (MD5) Previous issue date: 2007 / Resumo: Neste trabalho foram desenvolvidas e aplicadas técnicas de seleção de amostras e de variáveis espectrais para calibração multivariada a partir do Algoritmo das Projeções Sucessivas (APS). Também foi utilizada a transformada wavelet para resolver problemas de redução de ruído associado a dados de espectroscopia NIR (Infravermelho Próximo), na construção de modelos de calibração multivariada baseados em Regressão Linear Múltipla (MLR) para estimativa de parâmetros de qualidade de óleo diesel combustível e também de madeira de eucalipto. Os espectros NIR de transmitância para óleo diesel e de reflectância para madeira de eucalipto foram registrados empregando-se um equipamento NIR-Bomem com detector de Arseneto de Gálio e Índio. Para a aplicação em óleo diesel, foram estudadas as regiões espectrais: 850 - 1.100 nm, 1.100 - 1.570 nm e 1.570 - 2.500 nm. Para as amostras de madeira de eucalipto foi empregada a região de 1.100 - 2.500 nm. Os resultados do uso de técnicas de seleção de variáveis e amostras por MLR comprovaram sua simplicidade frente os modelos de regressão por mínimos quadrados parciais (PLS) que empregam toda a região espectral e transformação em variáveis latentes e são mais complexos de interpretar. O emprego de seleção de amostras demonstrou ainda a possibilidade de procedimentos de recalibrações e transferência de calibração que utilizam um número reduzido de amostras, sem a perda significativa da capacidade preditiva dos modelos MLR. O uso de filtragem wavelet também teve sua eficiência comprovada tanto no contexto da calibração multivariada quanto na filtragem de espectros NIR a partir de varreduras individuais. Na maioria dos casos de que trata esta tese e por conseqüência das técnicas quimiométricas empregadas, melhorias quanto à minimização do erro (RMSEP) associado à quantificação dos parâmetros de qualidade, bem como redução do tempo empregado na aquisição de varreduras de espectros NIR foram as principais contribuições fornecidas / Abstract: This work describes two techniques for spectral variable and sample selection based on the Successive Projections Algorithm (SPA), aiming the construction of multivariate regression models. Also, the wavelet transform was employed to solve problems related to noise reduction associated with spectroscopic data in the near infrared spectral region (NIR), and employed in the construction of multivariate calibration models based in Linear Multiple Regression (LMR) to estimate the quality parameters of diesel fuel and eucalyptus wood. The NIR transmission spectra for diesel samples and the reflectance spectra obtained for wood samples were acquired by using a NIR-Bomen equipment with AsGaIn detector. For application in diesel, the following spectral regions have been investigated: 850 - 1100 nm, 1100 - 1570 nm and 1570 - 2500 nm. For wood samples the spectral region employed was from 1100 - 2500 nm. The results obtained by using the variable selection techniques and LMR demonstrate their simplicity when compared with its counterpart Partial Least Square (PLS) which employs full spectral region and latent variables, being, therefore, more difficult to be interpreted. The use of wavelet filtering also demonstrates its efficiency both for multivariate calibration and NIR spectral data filtering. In most of the cases approached in this work, and inconsequence for the chemometric techniques employed, improvements in the error (RMSEP) associated with the quality parameters as well a decrease in the analysis time were the main achievements of this work / Doutorado / Quimica Analitica / Doutor em Ciências

Organometal halide perovskite absorbers such as methylammonium lead iodide chloride (CH3NH3PbI3-xClx), have emerged as an exciting new material family for photovoltaics due to its appealing features that include suitable direct bandgap with intense light absorbance, band gap tunability, ultra-fast charge carrier generation, slow electron-hole recombination rates, long electron and hole diffusion lengths, microsecond-long balanced carrier mobilities, and ambipolarity. The standard method of preparing CH3NH3PbI3-xClx perovskite precursors is a tedious process involving multiple synthesis steps and, the chemicals being used (hydroiodic acid and methylamine) are quite expensive. This work describes a novel, single-step, simple, and cost-effective solution approach to prepare CH3NH3PbI3-xClx thin films by the direct reaction of the commercially available CH3NH3Cl (or MACl) and PbI2. A detailed analysis of the structural and optical properties of CH3NH3PbI3-xClx thin films deposited by aerosol assisted chemical vapor deposition is presented. Optimum growth conditions have been identified. It is shown that the deposited thin films are highly crystalline with intense optical absorbance. Charge carrier separation of these thin films can be enhanced by establishing a local internal electric field that can reduce electron-hole recombination resulting in increased photo current. The intrinsic ferroelectricity in nanoparticles of Barium Titanate (BaTiO3 -BTO) embedded in the solar absorber can generate such an internal field. A hybrid structure of CH3NH3PbI3-xClx perovskite and ferroelectric BTO nanocomposite FTO/TiO2/CH3NH3PbI3-xClx: BTO/P3HT/Cu as a new type of photovoltaic device is investigated. Aerosol assisted chemical vapor deposition process that is scalable to large-scale manufacturing was used for the growth of the multilayer structure. TiO2 and P3HT with additives were used as ETL and HTL respectively. The growth process of the solar absorber layer includes the nebulization of a mixture of PbI2 and CH3NH3Cl perovskite precursors and BTO nanoparticles dissolved in DMF, and injection of the aerosol into the growth chamber and subsequent deposition on TiO2. While high percentage of BTO in the film increases the carrier separation, it also leads to reduced carrier generation. A model was developed to guide the optimum BTO nanoparticle concentration in the nanocomposite films. Characterization of perovskite solar cells indicated that ferroelectric polarization of BTO nanoparticles leads to the increase of the width of depletion regions in the perovskite layer hence the photo current was increased by one order of magnitude after poling the devices. The ferroelectric polarization of BTO nanoparticles within the perovskite solar absorber provides a new perspective for tailoring the working mechanism and photovoltaic performance of perovskite solar cells.

Postmortem interval (PMI) estimation is a necessary but often difficult task that must completed during a death investigation. The level of difficulty rises as time since death increases, especially with the case of skeletonized remains (long PMI). While challenging, a reliable PMI estimate may be of great importance for investigative direction and cost-savings (e.g. suspect identification, tailoring missing persons searches, non-forensic remains exclusion). Long PMI can be estimated by assessing changes in the organic content of bone (i.e. collagen), which degrades and is lost as the PMI lengthens. Visible-near infrared (VNIR) spectroscopy is one method that can be used for analyzing organic constituents, including proteins, in solid specimens. A 2013 preliminary investigation using a limited number of human cortical bone samples suggested that VNIR spectroscopy could provide a fast, reliable technique for assessing PMI in human skeletal remains. Clear separation was noted between "forensic" and "archaeological" specimen spectra within the near-infrared (NIR) bands. The goal of this research was to develop reliable multivariate classification models that could assign skeletal remains to appropriate PMI classes (e.g. "forensic" and "non-forensic"), based on NIR spectra collected from human cortical bone. Working with a large set of cortical samples (n=341), absorbance spectra were collected with an ASD/PANalytical LabSpec® 4 full range spectrometer. Sample spectra were then randomly assigned to training and test sets, where training set spectra were used to build internally cross-validated models in Camo Unscrambler® X 10.4; external validations of the models were then performed on test set spectra. Selected model algorithms included soft independent modeling of class analogy (SIMCA), linear discriminant analysis on principal components (LDA-PCA), and partial least squares discriminant analysis (PLSDA); an application of support vector machines on principal components (SVM-PCA) was attempted as well. Multivariate classification models were built using both raw and transformed spectra (standard normal variate, Savitzky-Golay) that were collected from the longitudinally cut cortical surfaces (Set A models) and the superficial cortical surface following light grinding (Set B models). SIMCA models were consistently the poorest performers, as were many of the SVM-PCA models; LDA-PCA models were generally the best performers for these data. Transformed-spectra model classification accuracies were generally the same or lower than corresponding raw spectral models. Set A models out-performed Set B counterparts in most cases; Set B models often yielded lower classification accuracy for older forensic and non-forensic spectra. A limited number of Set B transformed-spectra models out-performed the raw model counterparts, suggesting that these transformations may be removing scattering-related noise, leading to improvements in model accuracy. This study suggests that NIR spectroscopy may represent a reliable technique for assessing the PMI of unknown human skeletal remains. Future work will require identifying new sources of remains with established extended PMI values. Broadening the number of spectra collected from older forensic samples would allow for the determination of how many narrower potential PMI classes can be discriminated within the forensic time-frame.