O desenvolvimento e aperfeiçoamento de métodos de otimização são pontos de profundo interesse em todas as áreas de pesquisa. Tais técnicas muitas vezes envolvem a aquisição de métodos de controle novos ou melhores, o que está diretamente ligado a duas tarefas importantes: a escolha de formas eficientes de monitoramento do processo e a obtenção de modelos confiáveis para a variável de interesse a partir de dados experimentais. Graças às suas diversas vantagens, os sensores óticos vêm sendo amplamente aplicados na primeira tarefa. Uma vez que é possível a utilização de vários tipos de espectroscopia através deste tipo de sensor, modelos capazes de lidar com dados espectrais estão se tornando cada vez mais atraentes. A segunda tarefa, por sua vez, depende não só de quais preditores são utilizados na construção do modelo, mas também de quantos. Como a qualidade do modelo depende também do número de variáveis selecionadas, é importante desenvolver métodos que identifiquem aqueles que explicam o máximo possível da variabilidade dos dados. O método de otimização Colônia de Formigas (ACO) aparece como uma ferramenta bastante útil na seleção de variáveis, podendo-se encontrar muitas variações desse algoritmo na literatura. O propósito deste trabalho é desenvolver métodos de seleção de variáveis com base no algoritmo ACO, conceitos estatísticos e testes de hipóteses. Para isso, diversos critérios de decisão foram implementados nas etapas do algoritmo referentes à atualização de trilha de feromônios (C1) e à seleção de modelos (C2). A fim de estudar estas modificações, foram realizados dois estudos de caso: o primeiro na área de bioprocessos e o segundo na área de caracterização de alimentos. Ambos os estudos mostraram que, em geral, os modelos com menores erros são obtidos utilizando-se métricas dos componentes do modelo, tal como o tamanho do intervalo de confiança de cada parâmetro e o teste-t de hipóteses. Além disso, a modificação do critério de seleção de modelos parece não interferir significativamente no resultado final do algoritmo. Por último, foi feito um estudo da aplicação dessas versões do ACO no campo de caracterização de combustíveis, mais especificamente diesel, associando-se duas análises espectroscópicas para predição do conteúdo de enxofre. Algumas das versões desenvolvidas mostraram-se superior ao algoritmo ACO utilizado como base para este trabalho, proposto por Ranzan (2014), e todas os versões forneceram melhores resultados na quantificação de enxofre que aqueles obtidos por PCR. Dessa forma, comprova-se a potencialidade de métricas implementadas no algoritmo ACO, associadas à espectroscopia, na seleção de preditores significativos. / The development and improvement of optimization methods are points of deep interest in all areas of research. These techniques are often related to the acquisition of new or better control methods, which are directly attached to two importante tasks: choosing efficient forms of process monitoring and obtaining reliable models for the monitored variable from experimental data. Due to their several advantagens, optical sensors are being widely applied in the first task. Since several types of spectroscopy are possible through this type of sensor, models capable of dealing with spectral data are becoming increasingly attractive. The second task depends not only on which predictors are used in the model, but also on how many. Since the quality of the model depends on the number of selected variables, it is important to develop methods that identify those that explain the greater amount of data variability as possible, without compromising the reliability of the model. The Ant Colony Optimization is an important tool for variable selection, being possible to find a lot of variations of this method in literature. The purpose of this work is to develop a method of variable selection based on the Ant Colony Optimization (ACO) algorithm, statistical concepts and hypothesis testing. For this purpose, several decision criteria for trail update (C1) and model selection (C2) were implemented within the routine. In order to study these modifications, two case study was conducted: one related to bioprocess monitoring and another one envolving the characterization of food products. Both studies showed that, in general, the models with the lowest errors were obtained through the use of model component metrics, such as the length of the confidence interval associated with each parameter and the t hypothesis test. Besides, the modification of the model selection criterion doesn’t seem to affect the algorithm final result. Finally, the aplicattion of these methods in the field of fuels characterization, specifically diesel fuel, was studied, associating two spectroscopical analyses in order to predict the sulfur content. Some of the new developed methods appeared to be better than the ACO algorithm used as basis in this work, proposed by Ranzan (2014), and all methods showed better results than those from the models constructed by PCR. Thus, it is proved the high potencial of using different metrics within ACO algorithm, associated with spectroscopy, in order to select significative predictors.

O desenvolvimento e aperfeiçoamento de métodos de otimização são pontos de profundo interesse em todas as áreas de pesquisa. Tais técnicas muitas vezes envolvem a aquisição de métodos de controle novos ou melhores, o que está diretamente ligado a duas tarefas importantes: a escolha de formas eficientes de monitoramento do processo e a obtenção de modelos confiáveis para a variável de interesse a partir de dados experimentais. Graças às suas diversas vantagens, os sensores óticos vêm sendo amplamente aplicados na primeira tarefa. Uma vez que é possível a utilização de vários tipos de espectroscopia através deste tipo de sensor, modelos capazes de lidar com dados espectrais estão se tornando cada vez mais atraentes. A segunda tarefa, por sua vez, depende não só de quais preditores são utilizados na construção do modelo, mas também de quantos. Como a qualidade do modelo depende também do número de variáveis selecionadas, é importante desenvolver métodos que identifiquem aqueles que explicam o máximo possível da variabilidade dos dados. O método de otimização Colônia de Formigas (ACO) aparece como uma ferramenta bastante útil na seleção de variáveis, podendo-se encontrar muitas variações desse algoritmo na literatura. O propósito deste trabalho é desenvolver métodos de seleção de variáveis com base no algoritmo ACO, conceitos estatísticos e testes de hipóteses. Para isso, diversos critérios de decisão foram implementados nas etapas do algoritmo referentes à atualização de trilha de feromônios (C1) e à seleção de modelos (C2). A fim de estudar estas modificações, foram realizados dois estudos de caso: o primeiro na área de bioprocessos e o segundo na área de caracterização de alimentos. Ambos os estudos mostraram que, em geral, os modelos com menores erros são obtidos utilizando-se métricas dos componentes do modelo, tal como o tamanho do intervalo de confiança de cada parâmetro e o teste-t de hipóteses. Além disso, a modificação do critério de seleção de modelos parece não interferir significativamente no resultado final do algoritmo. Por último, foi feito um estudo da aplicação dessas versões do ACO no campo de caracterização de combustíveis, mais especificamente diesel, associando-se duas análises espectroscópicas para predição do conteúdo de enxofre. Algumas das versões desenvolvidas mostraram-se superior ao algoritmo ACO utilizado como base para este trabalho, proposto por Ranzan (2014), e todas os versões forneceram melhores resultados na quantificação de enxofre que aqueles obtidos por PCR. Dessa forma, comprova-se a potencialidade de métricas implementadas no algoritmo ACO, associadas à espectroscopia, na seleção de preditores significativos. / The development and improvement of optimization methods are points of deep interest in all areas of research. These techniques are often related to the acquisition of new or better control methods, which are directly attached to two importante tasks: choosing efficient forms of process monitoring and obtaining reliable models for the monitored variable from experimental data. Due to their several advantagens, optical sensors are being widely applied in the first task. Since several types of spectroscopy are possible through this type of sensor, models capable of dealing with spectral data are becoming increasingly attractive. The second task depends not only on which predictors are used in the model, but also on how many. Since the quality of the model depends on the number of selected variables, it is important to develop methods that identify those that explain the greater amount of data variability as possible, without compromising the reliability of the model. The Ant Colony Optimization is an important tool for variable selection, being possible to find a lot of variations of this method in literature. The purpose of this work is to develop a method of variable selection based on the Ant Colony Optimization (ACO) algorithm, statistical concepts and hypothesis testing. For this purpose, several decision criteria for trail update (C1) and model selection (C2) were implemented within the routine. In order to study these modifications, two case study was conducted: one related to bioprocess monitoring and another one envolving the characterization of food products. Both studies showed that, in general, the models with the lowest errors were obtained through the use of model component metrics, such as the length of the confidence interval associated with each parameter and the t hypothesis test. Besides, the modification of the model selection criterion doesn’t seem to affect the algorithm final result. Finally, the aplicattion of these methods in the field of fuels characterization, specifically diesel fuel, was studied, associating two spectroscopical analyses in order to predict the sulfur content. Some of the new developed methods appeared to be better than the ACO algorithm used as basis in this work, proposed by Ranzan (2014), and all methods showed better results than those from the models constructed by PCR. Thus, it is proved the high potencial of using different metrics within ACO algorithm, associated with spectroscopy, in order to select significative predictors.

A metodologia da espectroscopia no infravermelho próximo (NIRS Near Infrared Spectroscopy), recentemente empregada em várias áreas da ciência do solo associada à quimiometria, está sendo utilizada na quantificação de atributos químicos e físicos do solo. Esta técnica rápida, não destrutiva, reprodutiva e de baixo custo fundamenta-se na medida da intensidade de absorção de radiação eletromagnética na região do infravermelho próximo. De um modo geral, a NIRS tem se mostrado uma ferramenta válida para quantificar o teor de carbono (C) em amostras de solo. A importância da determinação do teor de C no solo reside no fato de representar a fixação do CO2 atmosférico na forma de matéria orgânica do solo (MOS), um compartimento chave do ciclo global deste elemento. O manejo sustentável dos agrossistemas deve assegurar a manutenção dos teores de carbono através da restituição de matéria orgânica ao solo. No caso da cultura de cana de açúcar, a substituição do sistema de colheita de manual com prévia queima da palhada (cana queimada) para mecanizada (cana crua) em que a palhada permanece sobre o solo, favorece o acúmulo e a diferenciação do teor de C no solo. O presente trabalho teve como objetivo comparar a metodologia NIRS, associada à quimiometria, com o método de referência tradicional da combustão a seco na quantificação do teor de C em amostras de solo provenientes do agroossistema cana-de-açúcar. Foram analisadas amostras de solo provenientes de três situações: cana queimada, cana crua e uma área nativa, totalizando 450 amostras, apresentando respectivamente teores médios de C de 18,30, 22,36 e 24,72 g kg-1. Modelos de calibração foram ajustados utilizando PLS nos dados espectrais, submetidos a 1ª derivada suavizados com Savitz Golay alisados com janela 15. As amostras foram aleatorizadas e divididas em 3 partes iguais, sendo 2/3 das 450 amostras para a calibração e validação cruzada e 1/3 para validação externa. Os parâmetros quimiométricos para a avaliação da qualidade do modelo escolhido foram: RMSEC, RMSECV, RMSEP, R2, BIAS, CV% e RPD. Os resultados de RMSEC> 1,93, RMSEP > 1,90, satisfatórios R2 0,94. Na área nativa obteve-se RMSEP 1,53 e R2 0,95, e na área de cana queimada RMSEP 1,90 e R2 0,50. Ao comparar as técnicas, de referência e NIRS, demonstrou que a técnica de referência tem SD 0,12 e CV% 0,65 e a técnica NIRS SD 0,18 e CV% 0,18, provando a eficiência do emprego da NIRS para a determinação de C no solo / The methodology of near infrared spectroscopy, recently used in several areas of soil science associated with chemometrics, has been used in the quantification of chemical and physical soil attributes. This rapid, nondestructive, reproductive and low cost technique is based on the measurement of the intensity of the electromagnetic radiation absorption in the near infrared region. In general, the NIRS has been proved as an effective tool to quantify the carbon (C) content in soil samples. The importance this determination is that it represents the fixation of atmospheric CO2 as soil organic matter (SOM), an important compartment of the global C cycle. The sustainable management of agro-systems must assure the maintenance of the carbon levels through the restoration of soil organic matter. In case of sugar cane crop, the replacement of the manual cutting system, preceded by residue burning (preharvest burning), by mechanized harvest (raw sugar cane) where the straw remains on the field, favors the accumulation and differentiation of the C content in the soil. This study aimed to compare the NIRS methodology, associated with chemometrics, to the traditional reference method of dry combustion quantifying the C content in soil samples from the sugar cane agro-system. Soil samples from three situations were analyzed: burnt sugar cane, raw sugar cane and a native area, totalizing 450 samples, with levels of respectively 18.30, 22.36 and 24.72 g C kg-1. Calibration models were adjusted using PLS in the spectral data, submitted to the first derivative and smoothing with Savitz Golay with window 15. Samples were randomized and divided into 3 parts: 2/3 of the 450 samples were designated to calibration and cross validation, and the odder 1/3 to external validation. The chemometrics parameters RMSEC, RMSECV, RMSEP, R2, BIAS, CV% and RPD were chosen to estimate the quality of the model. The results of RMSEC> 1.93, RMSEP> 1.90, and R2 0.94 were satisfactory. In the native area a range of RMSEP 1.53 and R2 0.95 was obtained, while in the area of burnt sugar cane it was R2 0.50 and RMSEP 1.90. Comparing the reference technique and NIRS, the first showed a SD 0.12 and CV 0.65%, while a SD 0.18 CV 0.81% was obtained for the second, proving the efficiency of the use of NIRS to determine C content in soil

Orientador: Jarbas Jose Rodrigues Rohwedder / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-12T12:27:30Z (GMT). No. of bitstreams: 1 Franco_CamilaManara_M.pdf: 1935277 bytes, checksum: 425b8e7a22fc4b566bca70f2763ad362 (MD5) Previous issue date: 2008 / Resumo: Através da Espectroscopia de Infravermelho Próximo (NIR), auxiliada por quimiometria, foram desenvolvidos modelos de calibração para determinar a concentração de hidrocarbonetos majoritários em misturas gasosas cujas concentrações são semelhantes a aquelas observadas em gás natural. Os espectros foram obtidos em dois diferentes espectrofotômetros NIR construídos no próprio laboratório, os quais empregavam células de caminho óptico fixo e variável. Diferentes conjuntos de amostras foram preparados de forma a reproduzir a variabilidade de concentração de metano, etano, propano e butano encontrada nas diversas fontes de gás natural. A análise de amostras certificadas, através dos modelos de calibração, apresentou valores para a raiz do erro médio quadrático de previsão (RMSEP) iguais a 1,07, 0,21, 0,22 e 0,14 % (v/v) na determinação de metano, etano, propano e butano, respectivamente. A previsão do gás metano apresentou melhor repetibilidade quanto realizada pela espectroscopia NIR do que com a técnica padrão, cromatografia gasosa. Visando a possibilidade da construção de um espectrofotômetro NIR dedicado à análise de gás natural foi realizado um estudo de seleção de variáveis, cujo resultado indicou que, utilizando até 13% do número inicial de variáveis (280) é possível realizar a previsão dos hidrocarbonetos gasosos sem perda da qualidade analítica quando comparado à análise que utiliza a faixa espectral completa. Por meio dos comprimentos de onda selecionados, pode-se prever a concentração de metano, etano, propano e butano com valores de RMSEP iguais a 1,32, 0,41, 0,22 e 0,14 % (v/v), respectivamente. / Abstract: Near Infrared (NIR) Spectroscopy and Chemometrics were used to construct calibration models to determine the concentration of major hydrocarbons in gas mixtures in concentrations similar to those observed in natural gas. The spectra were obtained by two different NIR spectrophotometers made in the laboratory, one employing a cell of fixed and other with variable optical path. Different sample sets were prepared in order to mimic the variability of methane, ethane, propane and butane concentration found in natural gas obtained from various sources. The analysis of certified samples made by using the calibration models showed Root-Mean-Square Errors of Prediction (RMSEP) equal to 1.07, 0.21, 0.22 and 0.14% (v/v) for methane, ethane, propane and butane determination, respectively. The prediction of methane gas content showed better repeatability compared to the standard technique based on gas chromatography. To investigate the possibility of constructing an NIR spectrometer dedicated to the analysis of natural gas, the selection of variables was evaluated. The results indicated that, by using up to 13% of the initial variables, the prediction of hydrocarbon gases is achieved with the same quality when compared to the results obtained using the full spectral range. Employing the selected wavelengths, it is possible to predict the concentration of methane, ethane, propane and butane with values of RMSEP equal to 1.32, 0.41, 0.22 and 0.14% (v / v), respectively. / Mestrado / Quimica Analitica / Mestre em Química

This thesis deals with the continual measurement of the hydrated lime moisture content on the principle of spectral measurements. The theoretical part describes the basic principles of the infrared spectroscopy, Fourier transformation and near infrared spectroscopy, which are used in the process of the spectroscopy measurement of fine particulates moisture content. The practical part of the work describes experiments and the evaluation of moisture hydrated lime measuring results, using gravimetric method and NIR spectroscopy in both the laboratory conditions and the conditions of operation production of hydrated lime as well. The part of the work monitors the spectroscopy measurement moisture values as the dependence on the optical quality of hydrate limes and the process conditions of the measurements.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19019号 / 農博第2097号 / 新制||農||1029(附属図書館) / 学位論文||H27||N4901(農学部図書室) / 31970 / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 近藤 直, 教授 清水 浩, 准教授 小川 雄一 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

No description available.

Yes / The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen – nicotinamide (IBU-NIC) and 1:1 carbamazepine – nicotinamide (CBZ-NIC) has been evaluated. A Partial Least Squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals.

Thesis (PhD(Agric) (Horticulture))--University of Stellenbosch, 2011. / Includes bibliography. / ENGLISH ABSTRACT: Plums with good taste, aroma and eating quality lead to repeat purchases and sustained demand. Taste includes non-volatile compounds, e.g. sugars and acids, and has been well researched to meet the consumers’ preferences. Plum aroma, however, has not enjoyed the same attention. Limited literature is available on the aroma of Japanese plums and none could be found on the effects of relatively long cold storage on the profiles. The main aim of this study is to investigate the changes in aroma compounds of Japanese plums throughout maturation and ripening and the effects of commercial cold storage regimes. Near infra-red (NIR) spectroscopy was also evaluated as a non-destructive method to determine plum quality parameters aimed at minimising sample variability. In Paper 1, NIR spectroscopy was used to develop prediction models for total soluble solid (TSS), total acidity (TA), sugar-to-acid ratio, firmness and weight in three cultivars (Pioneer, Laetitia and Angeleno) and a multi-cultivar model. Samples were collected for seven consecutive weeks and repeated over two seasons. TSS results showed excellent predictability (R2 = 0.817-0.955; RMSEP= 0.453-0.610 % Brix) but the TA models did not perform well. The sugar-to-acid ratio models had results comparable to that of TSS. Both the firmness and weight models had acceptable results. The models of ‘Pioneer’ and ‘Laetitia’ had a better predictability capacity than the ‘Angeleno’ model. Although the multi-cultivar models outperformed the single cultivar models on R2 values it had higher prediction errors. The robustness of all the TSS, TA and firmness models is high in terms of seasonality, range and cultivar. Papers 2 and 3, the main focus of the study, are concerned with the aroma profile dynamics of Japanese plums. HS-SPME was used in both papers to extract the aroma compounds followed by GC-TOFMS for separation and identification. In Paper 2, the aroma volatile compounds of three cultivars (Pioneer, Laetitia and Angeleno) were determined for a seven week period including samples from three maturity stages (immature, harvest and tree-ripe). A total of 35 compounds were identified of which ten were generic. Each cultivar had five unique compounds resulting in different aroma profiles for each of the maturity stages and distinct separation patterns using discriminant analysis. The study was extended in Paper 3 where the aroma volatile compounds of six cultivars (Pioneer, Sapphire, Laetitia, Songold, Larry Anne and Angeleno) and one plumcot (Flavor King) were determined at three functional stages (commercial harvest, tree-ripe fruit and cold stored fruit). A total of 62 compounds were identified and classified into three groups (‘unique’ (31), ‘generic’ (11) and ‘frequent’ (20)) based on their frequency of occurrence. The aroma profiles of ‘Larry Anne’ and ‘Flavor King’ are the most affected by cold storage conditions and ‘Pioneer’ appears to be the least affected. All the cultivars have significantly different aroma profiles at all three of the functional stages with ‘Sapphire’, ‘Larry Anne’ and ‘Flavor King’ showing the largest differences. ‘Flavor King’, a plumcot, presented a ripe aroma profile that was much diverged from that of the true plums. / AFRIKAANSE OPSOMMING: Pruime met ‘n goeie smaak, aroma en eetkwaliteit lei tot herhaalde verkope en volhoubare aanvraag. Smaak sluit die nie-vlugtige stowwe (suikers en sure) in en is goed nagevors om die verbruikersvoorkeure te bevredig. Pruim aroma het egter nie dieselfde aandag geniet nie. Daar is beperkte literatuur beskikbaar wat handel oor die aroma van Japanese pruime en geen kon gevind word oor die effekte van lang koelopberging op die aromaprofiele nie. Die hoof doel van hierdie studie is om die veranderinge in die aromatiese komponente van Japanese pruime te ondersoek tydens die volwassewording- en rypwordingsprosesse asook die effekte van kommersiele koelopberging. Naby infrarooi (NIR) spektroskopie is ook geevalueer as ‘n nie-destruktiewe manier om pruim kwaliteitsparameters te bepaal met die doel om monstervariasie te beperk. In Artikel 1 is NIR spektroskopie gebruik om voorspellingsmodelle vir totale oplosbare suikers (TOS), totale suur (TS), suiker-tot-suur verhouding, fermheid en gewig te bepaal in drie kultivars (Pioneer, Laetitia en Angeleno) asook ‘n multi-kultivar model. Monsters is vir sewe opeenvolgende weke versamel en herhaal oor twee seisoene. TOS resultate toon uitstekende voorspelbaarheid (R2 = 0.817-0.955; RMSEP= 0.453-0.610 % Brix) maar TS modelle het egter nie so goed gevaar nie. Die suiker-tot-suur verhoudingsmodelle se resultate was vergelykbaar met die van TOS. Beide die fermheid- en gewigsmodelle het aanvaarbare resultate opgelewer. Die modelle vir ‘Pioneer’ en ‘Laetitia’ het ‘n beter voorspelbaarheidskapasiteit getoon as die van ‘Angeleno’. Alhoewel die multi-kultivar model beter presteer het as die enkel kultivar modelle op die R2-waardes was daar meer voorspellingsfoute. Hoe robuustheid is gevind i.t.v. seisoene, datagrense en kultivar vir al die TOS, TA en fermheidsmodelle. Artikels 2 en 3, die fokuspunt van die studie, handel oor die dinamika van die aromaprofiel van Japanese pruime. HS-SPME is in beide artikels gebruik on die aromatiese verbindings te ekstraeer gevolg deur GCTOFMS vir skeiding en identifikasie. In Artikel 2 is die aromatiese stowwe van drie kultivars (Pioneer, Laetitia en Angeleno) bepaal vir sewe opeenvolgende weke en sluit monsters van drie volwassenheidsstadiums in (onvolwasse, oes en boom-rypgemaakte pruime). ‘n Totaal van 35 verbindings is geidentifiseer waarvan tien as generies beskou kan word. Elke kultivar het vyf unieke komponente gehad en het gelei tot verskillende aromaprofiele vir elk van die volwassenheidsstadiums en diverse skeidingspatrone tydens die gebruik van diskriminant analise. Die studie is uitgebrei in Artikel 3 waartydens die aromatiese vlugtige stowwe van ses kultivars (Pioneer, Sapphire, Laetitia, Songold, Larry Anne en Angeleno) en een plumcot (Flavor King) bepaal is tydens drie funksionele stadiums (oes, boom-rypgemaak en koelopgebergde pruime). ‘n Totaal van 62 verbindings is geidentifiseer en in drie groepe geklassifiseer (‘uniek’ (31), ‘generies’(11) en ‘gereeld’ (20)) gebaseer op voorkomsfrekwensie. Die aromaprofiele van ‘Larry Anne’ en ‘Flavor King’ is die meeste deur die koelopberging geaffekteer en ‘Pioneer’ die minste. Al die kultivars het kenmerkend verskil t.o.v. hul aromaprofiele in al drie die funksionele groepe en ‘Sapphire’, ‘Larry Anne’ en ‘Flavor King’ het die grootste verskille getoon. ‘Flavor King’, die plumcot, het ook ‘n ryp aromaprofiel gehad wat baie van die van die egte pruime verskil het.

Orientadores: Márcia Miguel Castro Ferreira, Jarbas José Rodrigues Rohwedder / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Qímica / Made available in DSpace on 2018-09-11T21:19:01Z (GMT). No. of bitstreams: 1 Dias_YuriGuimaraes_M.pdf: 1972063 bytes, checksum: 17925102065d8dd73072baa01e19447c (MD5) Previous issue date: 2011 / Resumo: Este estudo avalia a capacidade do uso da espectroscopia NIR para a determinação do teor de 6 hidrocarbonetos e dióxido de carbono em 40 misturas gasosas com composição semelhante à do gás natural. Os espectros NIR foram obtidos a partir de um equipamento experimental, construído no próprio laboratório, baseado em um monocromador do tipo AOTF e simultaneamente em um espectrofotômetro comercial NIR-FT. Modelos de calibração multivariada PLS-1 foram construídos, para os 6 gases, com os dois conjuntos de dados. A validação do método foi realizada calculando-se as figuras de mérito multivariadas, envolvendo em alguns casos o conceito do sinal analítico líquido (NAS). Os valores de RMSECV variam de 0,1858% para o 1,3- butadieno, cuja faixa de porcentagem volumétrica é de: 0,8% a 4,9%; a 0,9732% para o metano cuja faixa de porcentagem volumétrica é de: 61,7% a 89,9%. Estes valores confirmam a possibilidade de determinação do teor das espécies constituintes do gás natural e uma comparação das variâncias, por meio do teste F, dos modelos obtidos com os espectrofotômetros NIR-AOTF e NIR-FT indicam a adequação do equipamento experimental utilizado / Abstract: The present study evaluates the potential of NIR spectroscopy in determining the content of 6 hidrocarbons and carbon dioxide in 40 gas mixtures very similar in composition to the brazilian natural fuel gas. The NIR spectra were obtained from an equipament built in our laboratory and based on a AOTF wavelength selector device. All the spectra were simultaneously recorded in a comercial NIR-FT spectrophotometer (Bomem MB160D). Multivariate calibration models (PLS-1) were built for the six gases with both data sets. Method validation was performed calculating the figures of merit based on the Net Analyte Signal (NAS). RMSECV values ranged from 0.1858%, for 1,3-butadiene wich has a volumetric ratio of 0.8% to 4.9%; to 0.9732 for methane which has a volumetric ratio of 65.8% to 89.9%. These values confim the possibility of measuring the content of all the constituents evaluated in this study. A model variance comparision regarding the calibrations obtained with the NIR-AOTF and NIR-FT spectrophotometers showed the adequacy of the experimental equipament employed / Mestrado / Quimica Analitica / Mestre em Química