Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Hyperspectral imagery framework for unmixing and dimensionality estimation

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions. This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Fast unsupervised extraction of endmembers spectra from hyperspectral data

Linear unmixing decomposes an hyperspectral image into a collection of re ectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Detection and identification of dengue virus isolates from Brazil by a simplified reverse transcription - polymerase chain reaction (RT-PCR) method

We show here a simplified RT-PCR for identification of dengue virus types 1 and 2. Five dengue virus strains, isolated from Brazilian patients, and yellow fever vaccine 17DD as a negative control, were used in this study. C6/36 cells were infected and supernatants were collected after 7 days. The RT-PCR, done in a single reaction vessel, was carried out following a 1/10 dilution of virus in distilled water or in a detergent mixture containing Nonidet P40. The 50 µl assay reaction mixture included 50 pmol of specific primers amplifying a 482 base pair sequence for dengue type 1 and 210 base pair sequence for dengue type 2. In other assays, we used dengue virus consensus primers having maximum sequence similarity to the four serotypes, amplifying a 511 base pair sequence. The reaction mixture also contained 0.1 mM of the four deoxynucleoside triphosphates, 7.5 U of reverse transcriptase, 1U of thermostable Taq DNA polymerase. The mixture was incubated for 5 minutes at 37ºC for reverse transcription followed by 30 cycles of two-step PCR amplification (92ºC for 60 seconds, 53ºC for 60 seconds) with slow temperature increment. The PCR products were subjected to 1.7% agarose gel electrophoresis and visualized by UV light after staining with ethidium bromide solution. Low virus titer around 10 3, 6 TCID50/ml was detected by RT-PCR for dengue type 1. Specific DNA amplification was observed with all the Brazilian dengue strains by using dengue virus consensus primers. As compared to other RT-PCRs, this assay is less laborious, done in a shorter time, and has reduced risk of contamination

Analysis of early synthetic dyes with HPLC-DAD-MS: an important database for analysis of colorants used in cultural heritage

Presented thesis at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles

Evolution of IgG antibody response against Toxoplasma gondii tissue cyst in acute and chronic human infections

The recognition profile of the tissue cysts antigens by IgG antibodies was studied during acute and chronic human toxoplasmic infection. Thus the IgG response against Toxoplasma gondii was investigated by immunoblotting in two patients accidentally infected with the RH strain as well as in group of naturally infected patients at acute and chronic phase. There was an overall coincidence of molecular mass among antigens of tachyzoites and tissue cysts recognized by these sera, however, they appear not to be the same molecules. The response against tissue cysts starts early during acute infection, and the reactivity of antibodies is strong against a wide range of antigens. Six bands (between 82 and 151 kDa) were exclusively recognized by chronic phase sera but only the 132 kDa band was positive in more than 50% of the sera analysed. A mixture of these antigens could be used to discriminate between the two infection phases. The most important antigens recognized by the acute and the chronic phase sera were 4 clusters in the ranges 20-24 kDa, 34-39 kDa, 58-80 kDa and 105-130 kDa as well as two additional antigens of 18 and 29 kDa. Both accidentally infected patients and some of the naturally infected patients showed a weak specific response against tissue cyst antigens.

Light controlled synthesis of nucleic acids

Dissertação apresentada para obtenção do grau de Doutor em Bioquímica - especialidade Biotecnologia, pela Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

Estudo do óxido de cálcio como catalisador para a transesterificação de óleos alimentares usados

O instável mas tendencialmente crescente preço dos combustíveis associado a preocupações ambientais cada vez mais enraizadas nas sociedades, têm vindo a despoletar uma maior atenção à procura de combustíveis alternativos. Por outro lado, várias projecções indicam um aumento muito acentuado do consumo energético global no curto prazo, fruto do aumento da população e do nível de industrialização das sociedades. Neste contexto, o biodiesel (ésteres de ácidos gordos) obtido através da transesterificação de triglicerídeos de origem vegetal ou animal, surge como a alternativa “verde” mais viável para utilização em equipamentos de combustão. A reacção de transesterificação é catalisada, por norma com recurso a catalisadores homogéneos alcalinos (NaOH ou KOH). Este tipo de processo, o único actualmente com expressão a nível industrial, apresenta algumas desvantagens que, para além de aumentarem o custo do produto final, contribuem para reduzir a benignidade do mesmo: a impossibilidade de reutilização do catalisador, o aumento do número e complexidade das etapas de separação e a produção de efluentes resultantes das referidas etapas. Com o intuito de minimizar ou eliminar estes problemas, vários catalisadores heterogéneos têm vindo a ser estudados para esta reacção. Apesar de muitos apresentarem resultados promissores, a grande maioria não tem viabilidade para aplicação industrial seja devido ao seu próprio custo, seja devido aos pré-tratamentos necessários à sua utilização. Entre estes catalisadores, o óxido de cálcio é talvez o que apresenta resultados mais promissores. O crescente número de estudos envolvendo este catalisador em detrimento de outros, é por si mesmo prova do potencial do CaO. A realização deste trabalho pretendia atingir os seguintes objectivos principais: • Avaliar a elegibilidade do óxido de cálcio enquanto catalisador da reacção de transesterificação de óleos alimentares usados com metanol; • Avaliar qual a sua influência nas características dos produtos finais; • Avaliar as diferenças de performance entre o óxido de cálcio activado em atmosfera inerte (N2) e em ar, enquanto catalisadores da reacção de transesterificação de óleos alimentares usados com metanol; • Optimizar as condições da reacção com recurso às ferramentas matemáticas disponibilizadas pelo planeamento factorial, através da variação de quatro factores chave de influência: temperatura, tempo, relação metanol / óleo e massa de catalisador utilizado. O CaO utlizado foi obtido a partir de carbonato de cálcio calcinado numa mufla a 750 °C durante 3 h. Foi posteriormente activado a 900 °C durante 2h, em atmosferas diferentes: azoto (CaO-N2) e ar (CaO-Ar). Avaliaram-se algumas propriedades dos catalisadores assim preparados, força básica, concentração de centros activos e áreas específicas, tendo-se obtido uma força básica situada entre 12 e 14 para ambos os catalisadores, uma concentração de centros activos de 0,0698 mmol/g e 0,0629 mmol/g e áreas específicas de 10 m2/g e 11 m2/g respectivamente para o CaO-N2 e CaO-Ar. Efectuou-se a transesterificação, com catálise homogénea, da mistura de óleos usados utilizada neste trabalho com o objectivo de determinar os limites para o teor de FAME’s (abreviatura do Inglês de Fatty Acid Methyl Esters’) que se poderiam obter. Foi este o parâmetro avaliado em cada uma das amostras obtidas por catálise heterogénea. Os planos factoriais realizados tiveram como objectivo maximizar a sua quantidade recorrendo à relação ideal entre tempo de reacção, temperatura, massa de catalisador e quantidade de metanol. Verificou-se que o valor máximo de FAME’s obtidos a partir deste óleo estava situado ligeiramente acima dos 95 % (m/m). Realizaram-se três planos factoriais com cada um dos catalisadores de CaO até à obtenção das condições óptimas para a reacção. Não se verificou influência significativa da relação entre a quantidade de metanol e a massa de óleo na gama de valores estudada, pelo que se fixou o valor deste factor em 35 ml de metanol / 85g de óleo (relação molar aproximada de 8:1). Verificou-se a elegibilidade do CaO enquanto catalisador para a reacção estudada, não se tendo observado diferenças significativas entre a performance do CaO-N2 e do CaO-Ar. Identificaram-se as condições óptimas para a reacção como sendo os valores de 59 °C para a temperatura, 3h para o tempo e 1,4 % de massa de catalisador relativamente à massa de óleo. Nas referidas condições, obtiveram-se produtos com um teor de FAME’s de 95,7 % na catálise com CaO-N2 e 95,3 % na catálise com CaO-Ar. Alguns autores de estudos consultados no desenvolvimento do presente trabalho, referiam como principal problema da utilização do CaO, a lixiviação de cálcio para os produtos obtidos. Este facto foi confirmado no presente trabalho e na tentativa de o contornar, tentou-se promover a carbonatação do cálcio com a passagem de ar comprimido através dos produtos e subsequente filtração. Após a realização deste tratamento, não mais se observaram alterações nas suas propriedades (aparecimento de turvação ou precipitados), no entanto, nos produtos obtidos nas condições óptimas, a concentração de cálcio determinada foi de 527 mg/kg no produto da reacção catalisada com CaO-N2 e 475 mg/kg com CaO-A. O óxido de cálcio apresentou-se como um excelente catalisador na transesterificação da mistura de óleos alimentares usados utilizada no presente trabalho, apresentando uma performance ao nível da obtida por catálise homogénea básica. Não se observaram diferenças significativas de performance entre o CaO-N2 e o CaO-Ar, sendo possível obter nas mesmas condições reaccionais produtos com teores de FAME’s superiores a 95 % utilizando qualquer um deles como catalisador. O elevado teor de cálcio lixiviado observado nos produtos, apresenta-se como o principal obstáculo à aplicação a nível industrial do óxido de cálcio como catalisador para a transesterificação de óleos.

A influência dos fileres no comportamento viscoso dos mastiques betuminosos

As estradas têm vindo a sofrer um aumento de importância, sendo necessário aplicar pavimentos com melhores características, assim foram desenvolvidos os mastiques betuminosos. Os mastiques são misturas de betume com filer, este material tem propriedades melhoradas em relação ao betume puro. O betume é um material viscoelástico, o que leva a que a viscosidade seja uma propriedade de vital importância estudar. A junção de fileres ao betume promove um aumento de viscosidade, levando a que esta propriedade reológica tenha ainda mais importância, principalmente porque a trabalhabilidade da mistura betuminosa fica comprometida quando a viscosidade não é a correta. Na realização desta dissertação foram realizados ensaios em mastiques betuminosos, com recurso ao viscosímetro rotativo de Brookfield. Os mastiques ensaiados são compostos com fileres de diferentes origens e com diferentes taxas de incorporação, e assim foram analisadas as diferenças obtidas nos valores da viscosidade dinâmica. Os resultados obtidos mostram que os mastiques, à temperatura de fabrico, têm um comportamento viscoso idêntico ao comportamento viscoso do betume puro. Apesar dos mastiques produzidos terem uma viscosidade maior que a do betume puro, o valor desta propriedade reológica tende a igualar ao valor do betume, este comportamento observa-se nas temperaturas mais elevadas. Quando se examinam os resultados dos mastiques produzidos com menor quantidade de filer, na generalidade, os valores da viscosidade obtidos são idênticos. Para taxas de incorporação maiores, os valores da viscosidade dinâmica dos mastiques produzidos são bastante mais altos e distintos. Levando a concluir que quanto maior a percentagem de filer no mastique, maior o valor da viscosidade e maior dispersão dos resultados obtidos, isto para os fileres e taxas de incorporação testadas.

Alternative solvents in carvone hydrogenation

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquimica

Imunossensores Eletroquímicos para o Diagnóstico Precoce e Descentralizado do Cancro da Mama

O cancro é uma das principais causas de morte em todo o mundo. Entre as mulheres, o cancro da mama é o mais frequente. A deteção precoce do cancro é de extrema importância na medida em que pode aumentar as possibilidades de cura dos pacientes e contribuir para a diminuição da taxa de mortalidade desta doença. Um método que tem contribuído para a deteção precoce do cancro é a análise de biomarcadores. Biomarcadores associados ao cancro da mama, como o Recetor 2 do Fator de Crescimento Epidérmico Humano (HER2) e o Antigénio Carbohidratado 15-3 (CA 15-3), podem ser detetados através de dispositivos como os biossensores. Neste trabalho foram desenvolvidos dois imunossensores eletroquímicos para a análise de HER2 e CA 15-3. Para ambos os sensores foram utilizados, como transdutores, elétrodos serigrafados de carbono. A superfície destes transdutores foi nanoestruturada com nanopartículas de ouro. Foram realizados imunoensaios não-competitivos (do tipo sandwich) em ambos os imunossensores, cuja estratégia consistiu na (i) imobilização do respetivo anticorpo de captura na superfície nanoestruturada dos elétrodos, (ii) bloqueio da superfície com caseína, (iii) incubação com uma mistura do analito (HER2 ou CA 15-3) e o respetivo anticorpo de deteção biotinilado, (iv) adição de estreptavidina conjugada com fosfatase alcalina (S-AP; a AP foi utilizada como marcador enzimático), (v) adição de uma mistura do substrato enzimático (3-indoxil fosfato) e nitrato de prata, e (vi) deteção do sinal analítico através da redissolução anódica, por voltametria de varrimento linear, da prata depositada enzimaticamente. Com as condições experimentais otimizadas, foi estabelecida a curva de calibração para a análise de HER2 em soro, entre 15 e 100 ng/mL, obtendo-se um limite de deteção de 4,4 ng/mL. Para o CA 15-3 a curva de calibração (em solução aquosa) foi estabelecida entre 15 e 250 U/mL, obtendo-se um limite de deteção de 37,5 U/mL. Tendo em conta o valor limite (cutoff value) estabelecido para o HER2 (15 ng/mL) pode-se comprovar a possível utilidade do imunossensor desenvolvido para o diagnóstico precoce e descentralizado do cancro da mama. No caso do CA 15-3 serão necessários estudos adicionais para se poder avaliar a utilidade do imunossensor para o diagnóstico do cancro da mama.

Eficiência de antioxidantes na estabilização do Biodiesel

O Biodiesel é uma fonte de energia renovável que actualmente se encontra em expansão. O Biodiesel é constituído por uma mistura de ésteres alquílicos de ácidos gordos. A existência de ácidos gordos insaturados torna o Biodiesel quimicamente menos estável, podendo ocorrer oxidação, degradação e polimerização do combustível, se este for inadequadamente armazenado ou transportado. O objectivo deste trabalho consistiu em avaliar a eficiência da utilização de antioxidantes fenólicos (ácido protocatecuico, ácido gálico, ácido 3,4 di-hidroxifenilacético, ácido cafeico, ácido hidrocafeico, ácido 3,4,5-tri-hidroxicinâmico, ácido m-coumárico e ácido p-coumárico), na estabilização do Biodiesel. O estudo envolveu a análise da influência do uso de cada um dos antioxidantes na inibição da peroxidação lipídica do ácido linoleico um dos principais ácidos gordos insaturados presentes na matéria-prima utilizada na produção de Biodiesel. A avaliação do efeito de inibição dos antioxidantes na peroxidação do ácido linoleico foi efetuada usando o método do tiocianato de ferro (III). Os resultados obtidos demonstraram, que todos os ácidos fenólicos estudados, apresentam uma elevada capacidade para inibir a peroxidação lipídica do ácido linoleico. As percentagens de inibição da peroxidação do ácido linoleico variaram entre os 72%, observada para o ácido p-coumárico, e os 82 %, verificada para o ácido protocatecuico. A eficiência de inibição da peroxidação por parte dos antioxidantes fenólicos em estudo foi comparada com a obtida utilizando um antioxidante de referência, o trolox. A eficiência de inibição obtida para todos os antioxidantes fenólicos estudados foi muito superior à observada para o trolox. Os resultados obtidos nesta dissertação permitem concluir que a utilização de ácidos fenólicos constitui uma boa alternativa para a estabilização de matrizes lipídicas, particularmente em combustíveis como o Biodiesel.