The work presented in this thesis has demonstrated that the use of elemental profiles for the quality control of herbal medicines can be applied to multiple stages of processing. A single method was developed for the elemental analysis of a variety of St John’s Wort (Hypericum perforatum) preparations using Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES). The optimised method consisted of using 5 ml of nitric acid and microwave digestion reaching temperatures of 185⁰C. Using NIST Polish tea (NIST INCT-TL- 1) the method was found to be accurate and the matrix effect from selected St John’s Wort (SJW) preparations was found to be ≤22%. The optimised method was then used to determine the elemental profiles for a larger number of SJW preparations (raw herbs=22, tablets=20 and capsules=12). Specifically, the method was used to determine the typical concentrations of 25 elements (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, In, Mg, Mn, Mo, Ni, Pb, Pt, Sb, Se, Sr, V, Y and Zn) for each form of SJW which ranged from not detected to 200 mg/g. To further interpret the element profiles, Principal Component Analysis (PCA) was carried out. This showed that different forms of SJW could be differentiated based on their elemental profile and the SJW ingredient used (i.e. extract or raw herb) identified. The differences in the profiles were likely due to two factors: (1) the addition of bulking agents and (2) solvent extraction. In order to further understand how the elemental profile changes when producing the extract from the raw plant, eight SJW herb samples were extracted with four solvents (100% water, 60% ethanol, 80% ethanol and 100% ethanol) and analysed for their element content. The results showed that the transfer of elements from the raw herb to an extract was solvent and metal dependent. Generally the highest concentrations of an element were extracted with 100% water, which decreased as the concentration of ethanol increased. However, the transfer efficiency for the element Cu was highest with 60% ethanol. The solvents utilised in industry (60% and 80% ethanol) were found to preconcentrate some elements; Cu (+119%), Mg (+93%), Ni (+183%) and Zn (+12%) were found to preconcentrate in 60 %v/v ethanol extracts and Cu (+5%) and Ni (+30%). PCA of the elemental profiles of the four types of extract showed that differentiation was observed between the different solvents and as the ethanol concentration increased, the extracts became more standardised. Analysis of the bioactive compounds rutin, hyperoside, quercetin, hyperforin and adhyperforin followed by subsequent Correlation Analysis (CA) displayed relationships between the elemental profiles and the molecular profiles. For example strong correlations were seen between hyperoside and Cr as well as Quercetin and Fe. This shows potential for tuning elemental extractions for metal-bioactive compounds for increased bioactivity and bioavailability; however further work in needed in this area.

Knorpel ist ein kompliziertes System aus einem kollagenen Netzwerk, gefüllt mit wasserbindenden Makromolekülen (Proteoglykanen) und darin eingebetteten Zellen. Störungen in den komplexen Wechselbeziehungen können zur Gefährdung der strukturellen Integrität des Knorpels führen. Die hochauflösende Magnetresonanztomographie (NMR-Mikroskopie) kann über die Analyse der Signalintensität interne Knorpelstrukturen darstellen (hypo- und hyperintense Zonen). Mit Hilfe ionenmikroskopischer Analysemethoden (PIXE, RBS, ERDA) wurden im Knorpel (femorale und tibiale Kondyle des Hausschweins) im Querschnitt die zweidimensionalen Verteilungen der Knorpelelemente (H, C, N, O, P, S, Cl, K und Ca) aufgenommen sowie die Konzentrationen in ausgewählten Zonen bestimmt. Ergänzend wurde mit STIM die Dichteverteilung im Knorpel untersucht. Es gelang auch mit STIM, erstmalig kollagene Fasern in ihrer, bis auf den Wasserentzug natürlichen, Umgebung im Knorpel und damit unverändert in ihrer Anordnung sichtbar zu machen (keine chemische Demaskierung nötig). Die Ergebnisse wurden mit NMR- und polarisationsmikroskopischen Untersuchungen verglichen und in ihrem Zusammenhang mit den histologischen Knorpelzonen diskutiert. In den NMR-hypointensen Zonen fanden sich eine erhöhte Chlorkonzentration und punktförmige Calciumanreicherungen. Diese Zonen waren (im gefriergetrockneten Zustand) durch eine, bis zu einem Faktor vier höhere Dichte gekennzeichnet, die im maximalen Gehalt der Matrixelemente, H, C, N, O, (höchste Kollagendichte) begründet liegt. Im tibialen Knorpel konnten in der NMR-hypointensen Zone radial verlaufende einzelne Kollagenfasern nachgewiesen werden. Im femoralen Knorpel wurden in dieser Zone keine Einzelfasern nachgewiesen. Es deutete sich eine tubuläre Anordnung der Kollagenfasern an. In der hypertrophen Zone zeigten sich hohe Konzentrationen an Phosphor (Zellorganellen), Schwefel (Proteoglykane), Kalium (alkalisches Milieu) und Calcium (Vorstufe der Kalzifizierung). Die Chlorkonzentration hatte dort ihr Minimum. In dieser Zone verlaufen die Kollagenfasern radial und münden senkrecht in den Kalkknorpel. In der Tangentialfaserschicht wurde eine erhöhte Konzentration an Calcium und Phosphor beobachtet (Einlagerung von Calciumphosphaten). In dieser Zone wurden tangential verlaufende Kollagenfasern und ihr Übergang zur stärkeren Vernetzung mit teilweise arkadenförmiger Überstruktur sichtbar gemacht. Zur genaueren Aufklärung der dreidimensionalen Anordnung der Kollagenen Strukturen wurden erste Experimente zur STIM-Tomographie durchgeführt.

A utilização de técnicas analíticas para classificação de produtos ou predição de propriedades químicas tem se mostrado de especial interesse tanto na indústria quanto na academia. Através da análise da concentração elementar, ou de técnicas de espectroscopia, é possível obter-se um grande número de informações sobre as amostras em análise. Contudo, o elevado número de variáveis disponíveis (comprimentos de onda, ou elementos químicos, por exemplo) pode prejudicar a acurácia dos modelos gerados, necessitando da utilização de técnicas para seleção das variáveis mais relevantes com vistas a tornar os modelos mais robustos. Esta dissertação propõe métodos para seleção de variáveis em química analítica com propósito de classificação de produtos e predição via regressão de propriedades químicas. Para tal, inicialmente propõe-se um método de seleção de intervalos não equidistantes de comprimentos de onda em espectroscopia para classificação de combustíveis, o qual baseia-se na distância entre espectros médios de duas classes distintas; os intervalos são então utilizados em técnicas de classificação.Ao ser aplicado em dois bancos de dados de espectroscopia, o método foi capaz de reduzir o número de variáveis utilizadas para somente 23,19% e 4,95% das variáveis originais, diminuindo o erro de 13,90% para 11,63% e de 4,71% para 1,21%. Em seguida é apresentado um método para seleção dos elementos mais relevantes para classificação de vinhos provenientes de quatro países da América do Sul, baseado nos parâmetros da análise discriminante linear. O método possibilitou atingir acurácia média de 99,9% retendo em média 6,82 elementos químicos, sendo que a melhor acurácia média atingida utilizando todos os 45 elementos disponíveis foi de 91,2%. Por fim, utiliza-se o algoritmo support vector regression – recursive feature elimination (SVR-RFE) para seleção dos comprimentos de onda mais importantes na regressão por vetores de suporte. Ao serem aplicado em 12 bancos de dados juntamente com outros métodos de seleção e regressão, o SVR e o SVR-RFE obtiveram os melhores resultados em 8 deles, sendo que o SVR-RFE foi significativamente superior dentre os algoritmos de seleção. A aplicação dos métodos deseleção de variáveis propostos na presente dissertação possibilitou a realização de classificações e regressões mais robustas, bem como a redução do número de variáveis retidas nos modelos. / The use of analytical techniques in product classification or chemical properties estimation has been of great interest in both industry and academy. The employment of spectroscopy techniques, or through elemental analysis, provides a great amount of information about the samples being analyzed. However, the large number of features (e.g.: wavelengths or chemical elements) included in the models may jeopardize the accuracy, urging the employment of feature selection techniques to identify the most relevant features, producing more robust models. This dissertation presents feature selection methods focused on analytical chemistry, aiming at product classification and chemical property estimation (regression). For that matter, the first proposed method aims at identifying the most relevant wavelength intervals for fuel classification based on the distance between the average spectra of the two classes being analyzed. The identified intervals are then used as input for classifiers. When applied to two spectroscopy datasets, the proposed framework reduced the number of features to just 23.19% and 4.95% of the original ones, also reducing the misclassification error to 4.71% and 1.21%. Next, a method for identifying the most important elements for wine classification is presented, which is based on the parameters from linear discriminant analysis and aims at classifying wine samples produced in four south American countries. The method achieved average accuracy of 99.9% retaining average 8.82 chemical elements; the best accuracy using all 45 available chemical elements was 91.2%. Finally, the use of the support vector regression – recursive feature elimination (SVR-RFE) algorithm is proposed to identify the most relevant wavelengths for support vector regression. The proposed framework was applied to 12 datasets with other feature selection approaches and regression algorithms. SVR and SVR-RFE achieved the best results in 8 out of 12 datasets; SVR-RFE when compared to other feature selection algorithms proved have significantly better performance. The employment of the proposed feature selection methodsin this dissertation yield more robust classifiers and regression models, also reducing the number of features needed to produce accurate results.

New methodologies for the collection and analysis of biological samples from psychological, physical and emotional stress are described. Currently, there is little research relating to the elemental, VOC and small molecule changes in biological samples as a consequence of stress on the human body, with much of the current research indicating physical symptoms. This research sought to measure chemical changes in three different categories of stress. The first uses an existing PASAT intervention to induce psychological stress and a further two new methodologies using exercise to induce physical stress and a trapped human in a simulation of a collapsed building to induce emotion stress. Psychological, physical and emotional stress elemental profiles are compared against their respective chemical baseline profiles. Skin samples are collected from the foreheads of participants who endured emotional stress while drool saliva, urine, plasma and forehead skin samples were obtained from physically stressed participants. Furthermore, drool saliva is also obtained from the individual who experienced emotional stress.

El número de aplicaciones de la energía laser en el campo científico crece día a día. Estas no solo se han extendido en los campos de química, física y ciencia de materiales, sino también en biología y medicina. Este artículo es una breve introducción a los principios fundamentales del funcionamiento del láser, así como a su aplicación en el campo de la química. / The number of applications of lasers in science is constantly growing, with applications stretching from chemistry, physics and materials science to biology and medicine. This article provides a short overview of the fundamentals of lasers and an introduction to the application of lasers and laser ablation in chemistry.

A utilização de técnicas analíticas para classificação de produtos ou predição de propriedades químicas tem se mostrado de especial interesse tanto na indústria quanto na academia. Através da análise da concentração elementar, ou de técnicas de espectroscopia, é possível obter-se um grande número de informações sobre as amostras em análise. Contudo, o elevado número de variáveis disponíveis (comprimentos de onda, ou elementos químicos, por exemplo) pode prejudicar a acurácia dos modelos gerados, necessitando da utilização de técnicas para seleção das variáveis mais relevantes com vistas a tornar os modelos mais robustos. Esta dissertação propõe métodos para seleção de variáveis em química analítica com propósito de classificação de produtos e predição via regressão de propriedades químicas. Para tal, inicialmente propõe-se um método de seleção de intervalos não equidistantes de comprimentos de onda em espectroscopia para classificação de combustíveis, o qual baseia-se na distância entre espectros médios de duas classes distintas; os intervalos são então utilizados em técnicas de classificação.Ao ser aplicado em dois bancos de dados de espectroscopia, o método foi capaz de reduzir o número de variáveis utilizadas para somente 23,19% e 4,95% das variáveis originais, diminuindo o erro de 13,90% para 11,63% e de 4,71% para 1,21%. Em seguida é apresentado um método para seleção dos elementos mais relevantes para classificação de vinhos provenientes de quatro países da América do Sul, baseado nos parâmetros da análise discriminante linear. O método possibilitou atingir acurácia média de 99,9% retendo em média 6,82 elementos químicos, sendo que a melhor acurácia média atingida utilizando todos os 45 elementos disponíveis foi de 91,2%. Por fim, utiliza-se o algoritmo support vector regression – recursive feature elimination (SVR-RFE) para seleção dos comprimentos de onda mais importantes na regressão por vetores de suporte. Ao serem aplicado em 12 bancos de dados juntamente com outros métodos de seleção e regressão, o SVR e o SVR-RFE obtiveram os melhores resultados em 8 deles, sendo que o SVR-RFE foi significativamente superior dentre os algoritmos de seleção. A aplicação dos métodos deseleção de variáveis propostos na presente dissertação possibilitou a realização de classificações e regressões mais robustas, bem como a redução do número de variáveis retidas nos modelos. / The use of analytical techniques in product classification or chemical properties estimation has been of great interest in both industry and academy. The employment of spectroscopy techniques, or through elemental analysis, provides a great amount of information about the samples being analyzed. However, the large number of features (e.g.: wavelengths or chemical elements) included in the models may jeopardize the accuracy, urging the employment of feature selection techniques to identify the most relevant features, producing more robust models. This dissertation presents feature selection methods focused on analytical chemistry, aiming at product classification and chemical property estimation (regression). For that matter, the first proposed method aims at identifying the most relevant wavelength intervals for fuel classification based on the distance between the average spectra of the two classes being analyzed. The identified intervals are then used as input for classifiers. When applied to two spectroscopy datasets, the proposed framework reduced the number of features to just 23.19% and 4.95% of the original ones, also reducing the misclassification error to 4.71% and 1.21%. Next, a method for identifying the most important elements for wine classification is presented, which is based on the parameters from linear discriminant analysis and aims at classifying wine samples produced in four south American countries. The method achieved average accuracy of 99.9% retaining average 8.82 chemical elements; the best accuracy using all 45 available chemical elements was 91.2%. Finally, the use of the support vector regression – recursive feature elimination (SVR-RFE) algorithm is proposed to identify the most relevant wavelengths for support vector regression. The proposed framework was applied to 12 datasets with other feature selection approaches and regression algorithms. SVR and SVR-RFE achieved the best results in 8 out of 12 datasets; SVR-RFE when compared to other feature selection algorithms proved have significantly better performance. The employment of the proposed feature selection methodsin this dissertation yield more robust classifiers and regression models, also reducing the number of features needed to produce accurate results.

A utilização de técnicas analíticas para classificação de produtos ou predição de propriedades químicas tem se mostrado de especial interesse tanto na indústria quanto na academia. Através da análise da concentração elementar, ou de técnicas de espectroscopia, é possível obter-se um grande número de informações sobre as amostras em análise. Contudo, o elevado número de variáveis disponíveis (comprimentos de onda, ou elementos químicos, por exemplo) pode prejudicar a acurácia dos modelos gerados, necessitando da utilização de técnicas para seleção das variáveis mais relevantes com vistas a tornar os modelos mais robustos. Esta dissertação propõe métodos para seleção de variáveis em química analítica com propósito de classificação de produtos e predição via regressão de propriedades químicas. Para tal, inicialmente propõe-se um método de seleção de intervalos não equidistantes de comprimentos de onda em espectroscopia para classificação de combustíveis, o qual baseia-se na distância entre espectros médios de duas classes distintas; os intervalos são então utilizados em técnicas de classificação.Ao ser aplicado em dois bancos de dados de espectroscopia, o método foi capaz de reduzir o número de variáveis utilizadas para somente 23,19% e 4,95% das variáveis originais, diminuindo o erro de 13,90% para 11,63% e de 4,71% para 1,21%. Em seguida é apresentado um método para seleção dos elementos mais relevantes para classificação de vinhos provenientes de quatro países da América do Sul, baseado nos parâmetros da análise discriminante linear. O método possibilitou atingir acurácia média de 99,9% retendo em média 6,82 elementos químicos, sendo que a melhor acurácia média atingida utilizando todos os 45 elementos disponíveis foi de 91,2%. Por fim, utiliza-se o algoritmo support vector regression – recursive feature elimination (SVR-RFE) para seleção dos comprimentos de onda mais importantes na regressão por vetores de suporte. Ao serem aplicado em 12 bancos de dados juntamente com outros métodos de seleção e regressão, o SVR e o SVR-RFE obtiveram os melhores resultados em 8 deles, sendo que o SVR-RFE foi significativamente superior dentre os algoritmos de seleção. A aplicação dos métodos deseleção de variáveis propostos na presente dissertação possibilitou a realização de classificações e regressões mais robustas, bem como a redução do número de variáveis retidas nos modelos. / The use of analytical techniques in product classification or chemical properties estimation has been of great interest in both industry and academy. The employment of spectroscopy techniques, or through elemental analysis, provides a great amount of information about the samples being analyzed. However, the large number of features (e.g.: wavelengths or chemical elements) included in the models may jeopardize the accuracy, urging the employment of feature selection techniques to identify the most relevant features, producing more robust models. This dissertation presents feature selection methods focused on analytical chemistry, aiming at product classification and chemical property estimation (regression). For that matter, the first proposed method aims at identifying the most relevant wavelength intervals for fuel classification based on the distance between the average spectra of the two classes being analyzed. The identified intervals are then used as input for classifiers. When applied to two spectroscopy datasets, the proposed framework reduced the number of features to just 23.19% and 4.95% of the original ones, also reducing the misclassification error to 4.71% and 1.21%. Next, a method for identifying the most important elements for wine classification is presented, which is based on the parameters from linear discriminant analysis and aims at classifying wine samples produced in four south American countries. The method achieved average accuracy of 99.9% retaining average 8.82 chemical elements; the best accuracy using all 45 available chemical elements was 91.2%. Finally, the use of the support vector regression – recursive feature elimination (SVR-RFE) algorithm is proposed to identify the most relevant wavelengths for support vector regression. The proposed framework was applied to 12 datasets with other feature selection approaches and regression algorithms. SVR and SVR-RFE achieved the best results in 8 out of 12 datasets; SVR-RFE when compared to other feature selection algorithms proved have significantly better performance. The employment of the proposed feature selection methodsin this dissertation yield more robust classifiers and regression models, also reducing the number of features needed to produce accurate results.

Elemental analysis can become an important piece of evidence to assist the solution of a case. The work presented in this dissertation aims to evaluate the evidential value of the elemental composition of three particular matrices: ink, paper and glass. In the first part of this study, the analytical performance of LIBS and LA-ICP-MS methods was evaluated for paper, writing inks and printing inks. A total of 350 ink specimens were examined including black and blue gel inks, ballpoint inks, inkjets and toners originating from several manufacturing sources and/or batches. The paper collection set consisted of over 200 paper specimens originating from 20 different paper sources produced by 10 different plants. Micro-homogeneity studies show smaller variation of elemental compositions within a single source (i.e., sheet, pen or cartridge) than the observed variation between different sources (i.e., brands, types, batches). Significant and detectable differences in the elemental profile of the inks and paper were observed between samples originating from different sources (discrimination of 87 – 100% of samples, depending on the sample set under investigation and the method applied). These results support the use of elemental analysis, using LA-ICP-MS and LIBS, for the examination of documents and provide additional discrimination to the currently used techniques in document examination. In the second part of this study, a direct comparison between four analytical methods (µ-XRF, solution-ICP-MS, LA-ICP-MS and LIBS) was conducted for glass analyses using interlaboratory studies. The data provided by 21 participants were used to assess the performance of the analytical methods in associating glass samples from the same source and differentiating different sources, as well as the use of different match criteria (confidence interval (±6s, ±5s, ±4s, ±3s, ±2s), modified confidence interval, t-test (sequential univariate, p=0.05 and p=0.01), t-test with Bonferroni correction (for multivariate comparisons), range overlap, and Hotelling’s T2 tests. Error rates (Type 1 and Type 2) are reported for the use of each of these match criteria and depend on the heterogeneity of the glass sources, the repeatability between analytical measurements, and the number of elements that were measured. The study provided recommendations for analytical performance-based parameters for µ-XRF and LA-ICP-MS as well as the best performing match criteria for both analytical techniques, which can be applied now by forensic glass examiners.

This work is a research on the topic of reactive nanoparticles and their agitation into the solder paste, which it also describes. It describes in detail the properties of each solder alloys. It explains the creation of intermetallic layers in the soldering process and examines their structure. It also focuses on the evaluation and methodology of testing the properties of solder pastes. In the practical part, individual tests are performed with PF606 and PF610 solder paste.

Presented master thesis dealt with the analysis of garlic and onion samples and tried to find correlations between chemical composition and country of origin. The parameters examined were dry content, crude protein content, concentration of phenolic compunds, carbohydrate content (fructose, glucose), alliin content and concentration of selected elements (P, Mg, Ca, Na, K, Fe, Zn). Average content of dry matter of fresh samples was 35,84 ±2,12 g/100 g and of dehydrated samples was 90,61 ±2,90 g/100 g. Concentration of phenolic compounds was measured spectrophotometrically with Folin-Ciocault reagent. Average phenolic compounds content of samples was 0,1840 ±0,1286 GAE g/100 g. Crude protein content was measured by Kjeldahl method, and calculated from total nitrogen content. Carbohydrates were measured by HPLC-ELSD after hydrolysis of fructans. Average content of fructose of garlic samples was 57,014 ±0,863 g/100 g, of onion samples was 33,718 ±1,168 g/100 g. Average content of glucose of onion samples was 22,633 ±0,405 g/100 g. Alliin content was measured by HPLC-DAD method. Average alliin content of fresh samples was 4,644 ±0,446 g/100 g and of dehydrated samples was 1,962 ±0,180 g/100 g. Elemental analysis was conducted by ICP-OES method. Average concentration of selected elements was: P 2,15 ±0,11 mg/g, Mg 0,638 ±0,03 mg/g, Ca 1,246 ±0,05 mg/g, Na 0,550 ±0,08 mg/g, K 7,49 ±0,41 mg/g, Fe 79,3 ±6,16 mg/kg, Zn 11,4 ±3,33 mg/kg. Obtained data were statistically processed on significance level 0,05. Based on the principal components analysis, it was found, that the best parameters to differentiate samples from Czechia, Poland and Ukraine from other countries were content of nitrogen, phosphorus, magnesium, sodium and calcium. It was also discovered, that genus differences between onion and garlic have greater significance than geographical differences.