abstract: One out of ten women has a difficult time getting or staying pregnant in the United States. Recent studies have identified aging as one of the key factors attributed to a decline in female reproductive health. Existing fertility diagnostic methods do not allow for the non-invasive monitoring of hormone levels across time. In recent years, olfactory sensing has emerged as a promising diagnostic tool for its potential for real-time, non-invasive monitoring. This technology has been proven promising in the areas of oncology, diabetes, and neurological disorders. Little work, however, has addressed the use of olfactory sensing with respect to female fertility. In this work, we perform a study on ten healthy female subjects to determine the volatile signature in biological samples across 28 days, correlating to fertility hormones. Volatile organic compounds (VOCs) present in the air above the biological sample, or headspace, were collected by solid phase microextraction (SPME), using a 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coated fiber. Samples were analyzed, using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS). A regression model was used to identify key analytes, corresponding to the fertility hormones estrogen and progesterone. Results indicate shifts in volatile signatures in biological samples across the 28 days, relevant to hormonal changes. Further work includes evaluating metabolic changes in volatile hormone expression as an early indicator of declining fertility, so women may one day be able to monitor their reproductive health in real-time as they age. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2018

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Orientador: Carla Beatriz Grespan Bottoli / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-13T13:23:28Z (GMT). No. of bitstreams: 1 Godinho_RobsonBonfim_M.pdf: 6700594 bytes, checksum: 0cfc1f35b482743b90b3d97296590a0a (MD5) Previous issue date: 2009 / Resumo: Fragrância é uma mistura de moléculas orgânicas como ésteres, cetonas, aldeídos, hidrocarbonetos, alcoóis, entre outras, tendo como fontes substâncias de origem natural ou sintética, que quando combinadas em proporções harmônicas conferem uma característica odorífera ímpar em um perfume ou em um produto perfumado. Considerando essa característica, a primeira etapa desse trabalho foi o desenvolvimento de um método de análise dos compostos voláteis provenientes de uma fragrância por cromatografia gasosa utilizando a técnica de micro-extração em fase sólida no headspace, (em inglês, Headspace ¿ Solid Phase Micro Extraction, HS-SPME). Esse método foi utilizado para monitoramento da fragrância após sua aplicação no detergente em pó em diferentes condições de estocagem. Os dados obtidos após a extração e análise cromatográfica foram tratados com ferramentas quimiométricas, como HCA, (do inglês, Hierarchical Cluster Analysis), e PCA, (do inglês, Principal Component Analysis), para auxiliar na interpretação do conjunto de dados. Os métodos quimiométricos possibilitaram a identificação de grupos em gráficos bidimensionais, os quais mostram a variação das amostras de detergente com o tempo e o ambiente de estocagem. Devido à necessidade das indústrias de fragrâncias analisarem vários aspectos físicoquímicos dos produtos que trabalham como, a matéria prima e o produto acabado, para seu controle de qualidade, a segunda etapa desse trabalho baseou-se no desenvolvimento de um modelo de calibração multivariada, PLS, (do inglês, Partial Least Squares) para previsão de propriedades físicas das fragrâncias, densidade e índice de refração, a partir de dados cromatográficos. As análises foram baseadas em uma matriz com 45 amostras de diferentes fragrâncias e 675 variáveis. O modelo obtido apresentou uma boa capacidade de previsão, apresentando para esses dados um erro relativo menor que 1,0 % para a densidade e 0,5 % para o índice de refração, evidenciando a possibilidade de predizer dados físico-químicos de fragrâncias com excelente precisão a partir de análises cromatográficas / Abstract: Fragrance is a mixture of organic molecules such as esters, ketones, aldehydes, hydrocarbons and alcohols, among others, using as sources substances of natural or synthetic origin, which, when combined in harmonious proportions, confer a unique characteristic to a perfume or a scented product. Considering this feature, the first stage of this work was the development of a method of analysis of volatile compounds from a fragrance by gas chromatography using the technique of Headspace - Solid Phase Micro Extraction , HS-SPME. This method was used to monitor the fragrance after its application in detergent powder under different conditions of storage. The data obtained after the extraction and chromatographic analysis were treated with chemometric tools, such as HCA, Hierarchical Cluster Analysis, and PCA, Principal Component Analysis, to assist in the interpretation of the data set. The chemometric methods allowed the identification of groups in two-dimensional graphs, which show the variation of samples of detergent with time and storage environment. Due to the need of the fragrance industries to examine various aspects of the physical-chemical products that work as the raw material and finished product, for their quality control, the second stage of this work was based on the development of a model of multivariate calibration, PLS, Partial Least Squares, for prediction of the physical properties of fragrances, such as density and index of refraction, from the chromatographic data. The tests were based on a matrix with 45 samples of different fragrances and 675 variables. The model obtained showed a good capability for prediction, with a relative error of less than 1.0% for density and 0.5% for the index of refraction. This shows the possibility of predicting physico-chemical data of fragrances with good precision from chromatographic analysis / Mestrado / Quimica Analitica / Mestre em Química

Orientadores: Ronei Jesus Poppi, Fabio Augusto / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-09T16:35:14Z (GMT). No. of bitstreams: 1 Silva_GilmareAntonia_D.pdf: 2352562 bytes, checksum: ba31981a40cd4c76d67af387ca4819bc (MD5) Previous issue date: 2007 / Resumo: A proposta deste trabalho foi explorar a utilização da cromatografia gasosa com a etapa prévia de preparação da amostra através da microextração em fase sólida em diferentes matrizes, potencializando, inovando e viabilizando essas aplicações através de metodologias de quimiometria, especialmente aquelas que envolvem técnicas de inteligência artificial. Foi realizada a otimização simultânea da determinação de cinco pesticidas em infusos de Passiflora alata Dryander (maracujá doce) através de abordagem neuro-genética, ou seja, utilização de algoritmo genético para a otimização da rede neural modelada. Foram obtidos para duas fibras de extração (PDMS/PVA e PDMS 30 µM) os valores dos parâmetros de extração, respectivamente: 67 e 77 % para diluição do chá, 65,5 e 65,0 ºC para temperatura de extração, 50 e 39 minutos para tempo de extração e 0,36 g mL de concentração de NaCl para ambas as fibras. A abordagem neuro-genética foi também utilizada para a otimização simultânea da determinação de 12 bifenilas policloradas em leite materno. O algoritmo genético forneceu as melhores condições de extração como concentração de NaCl de 0,36 g mL, temperatura de extração de 95,0 ºC, tempo de extração de 60 minutos e adição de 210 µL de metanol. Utilizando cerveja como matriz foi realizado um estudo exploratório de 20 marcas comerciais brasileiras de cerveja em lata através da rede neural de Kohonen. Foi possível visualizar a formação de seis grupos e os espectros de massas permitiram identificar os compostos voláteis responsáveis pelas similaridades e dessemelhanças entre as cervejas, através da análise da fração volátil, utilizando uma fibra PDMS/DVB. Por fim, trinta e duas marcas comerciais de cervejas em garrafa, sendo 31 nacionais e uma estrangeira, foram avaliadas sensorialmente através da Análise Descritiva Quantitativa para os atributos amargor e sabor do grão; e correlacionados os resultados sensoriais com as respostas cromatográficas das cervejas através da utilização de algoritmo genético para a seleção das variáveis (compostos identificados pelos espectros de massas) diretamente relacionadas com os parâmetros de qualidade estudados / Abstract: The purpose of this work was to explore the utilization of gas chromatography with the previous stage of sample preparation through solid phase microextraction in different matrices, innovating and becoming feasible these applications using chemometric methodologies, specially the ones that involve artificial intelligence technique. It was realized the simultaneous optimization for the determination of five pesticides in infuses of Passiflora alata Dryander through the neuron-genetic approach, in other words, through the utilization of genetic algorithm to the optimization of the neural network model. It was obtained for two extraction fibers (PDMS/PVA and PDMS 30 µm) the following parameter values, respectively: 67 and 77 % for the tea dilution, 65,5 and 65,0 ºC for the extraction temperature, 50 and 39 minutes for the extraction time and 0,36 g mL for the NaCl concentration to both fibers. The neuron-genetic approach was also utilized to the simultaneous optimization of the determination of 12 polychlorinated biphenyls in human milk. The genetic algorithm provided the optimized extraction conditions as: NaCl concentration of 0,36 g mL, extraction temperature of 95,0 ºC, extraction time of 60 minutes and addition of 210 µL of methanol. By using beer as sample, it was realized an exploratory study of 20 commercial Brazilian beer labels stored in can, through the Kohonen neural network. It was possible to visualize the formation of six sets and through the mass spectra it was identified the volatile compounds responsible for the similarities and differences among the beer samples, through the fraction volatile analysis, utilizing a PDMS/DVB fiber. Finally, 32 commercial beer labels stored in glass bottles, among these 31 were national and one was international, were evaluated in a sensorial way through the Quantitative Descriptive Analysis to the bitterness and grain taste attributes and it was correlated the sensorial results with the chromatographic data of the beer samples by the utilization of genetic algorithm to the selection of the compounds (identified by the mass spectra) directly correlated with the quality parameters studied. / Doutorado / Quimica Analitica / Doutor em Ciências

Orientador: Fabio Augusto / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-19T18:08:24Z (GMT). No. of bitstreams: 1 Marques_SandraReginaRivellino_D.pdf: 2317302 bytes, checksum: fe68941a7328088e8aaba96b46928a24 (MD5) Previous issue date: 2011 / Resumo: A técnica de microextração em fase sólida através do headspace (HS-SPME) combinada com a cromatografia gasosa bidimensional abrangente e detecção por ionização em chama (GCxGCxFID) foi empregada para detectar artefatos formados durante o preparo da amostra de méis, que poderiam ser prejudiciais ao processo de identificação de sua origem floral. O método foi otimizado utilizando-se planejamento multivariado. Para isso, uma mistura de diferentes tipos de méis brasileiros foi usada como modelo. Os artefatos de extração identificados foram classificados como resultantes da manipulação através do HS. A influência da temperatura e do tempo de exposição ao tratamento térmico também foi avaliada. A identificação da fração volátil da mistura de mel foi realizada por GCxGCxFID e cromatografia gasosa acoplada à espectrometria de massas com analisador quadrupolar (GC-QMS) comparando-se o índice de retenção linear com programação de temperatura obtido na primeira dimensão (D-LTPRI), calculado a partir dos cromatogramas obtidos por GCxGCxFID, e os índices obtidos por GC-QMS para as mesmas amostras. Esta identificação foi confirmada por cromatografia gasosa bidimensional abrangente combinada a um espectrômetro de massas por tempo de vôo (GCxGCxTOFMS). Portanto, a identificação e detecção de artefatos de extração previamente desconhecidos é atribuída às vantagens da GCxGC. A GCxGCxFID combinada com ferramentas quimiométricas foi empregada para classificar algumas amostras de méis de diferentes origens do Piauí-Brasil. A GCxGCxQMS foi empregada para identificação dos voláteis de algumas destas amostras. A combinação de HS-SPME - (GCxGC ou GC) com o poder de identificação do detector MS, juntamente com índices de retenção e de métodos quimiométricos forneceram informações valiosas sobre a classificação química dos méis / Abstract: Solid phase microextraction through headspace (HS-SPME) was otimizated by a multivariate design. This technique combined with comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) was employed to detect potential artifacts formed during preparation of honey samples, that could possibly be relevant to the identification of its floral origin. A mixture of different types of brazilian honeys was used as the model sample. The extraction artifacts identified were classified as resulting from HS manipulation. The influence of temperature and time exposure of the thermal treatment was also evaluated. The identification of the volatile fraction of the honey blend was performed through combination of GCxGC-FID and GC coupled to quadrupole mass spectrometry (GC-QMS) by comparing the one dimensional linear temperature programmed retention index (D-LTPRI) calculated from GCxGCxFID chromatograms to that of chromatograms of the same samples obtained on GC-QMS. This identification was confirmed by GCxGC combined with a time-of-flight mass analyzer (GCxGC-TOFMS). Therefore, the identification and detection of previously unknown extraction artifacts is attributed to advantages of GCxGC. GCxGCxFID in combination with chemometric tools was employed to classify some honey samples from different origins from Piauí-Brazil. GCxGCxQMS was employed for the identification of the volatiles from some one of these samples. The combination of HS-SPME - (GCxGC or GC) and the qualitative information of MS, retention index and chemometric methods may be able to provide valuable information on the chemical classification of honeys / Doutorado / Quimica Analitica / Doutor em Ciências

There is limited scientific knowledge on the composition of human odor from different biological specimens and the effect that physiological and psychological health conditions could have on them. There is currently no direct comparison of the volatile organic compounds (VOCs) emanating from different biological specimens collected from healthy individuals as well as individuals with certain diagnosed medical conditions. Therefore the question of matching VOCs present in human odor across various biological samples and across health statuses remains unanswered. The main purpose of this study was to use analytical instrumental methods to compare the VOCs from different biological specimens from the same individual and to compare the populations evaluated in this project. The goals of this study were to utilize headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC/MS) to evaluate its potential for profiling VOCs from specimens collected using standard forensic and medical methods over three different populations: healthy group with no diagnosed medical or psychological condition, one group with diagnosed type 2 diabetes, and one group with diagnosed major depressive disorder. The pre-treatment methods of collection materials developed for the study allowed for the removal of targeted VOCs from the sampling kits prior to sampling, extraction and analysis. Optimized SPME-GC/MS conditions has been demonstrated to be capable of sampling, identifying and differentiating the VOCs present in the five biological specimens collected from different subjects and yielded excellent detection limits for the VOCs from buccal swab, breath, blood, and urine with average limits of detection of 8.3 ng. Visual, Spearman rank correlation, and PCA comparisons of the most abundant and frequent VOCs from each specimen demonstrated that each specimen has characteristic VOCs that allow them to be differentiated for both healthy and diseased individuals. Preliminary comparisons of VOC profiles of healthy individuals, patients with type 2 diabetes, and patients with major depressive disorder revealed compounds that could be used as potential biomarkers to differentiate between healthy and diseased individuals. Finally, a human biological specimen compound database has been created compiling the volatile compounds present in the emanations of human hand odor, oral fluids, breath, blood, and urine.

<p>Solid Phase Microextraction (SPME) is a popular sampling technique that can be paired with Gas Chromatography/Mass Spectrometry (GC-MS). SPME-GC-MS is used in forensic chemistry due to its simplification of the sample preparation process. Headspace-Solid Phase Microextraction (HS-SPME) is a technique where the sample is heated to generate volatiles in the headspace of the vial. A SPME fiber is then inserted into the vial and the compounds in the headspace will bind to the fiber. Total Vaporization- Solid Phase Microextraction (TV-SPME) is a technique that is derived from the HS-SPME technique. </p><p>In Chapter 1, the critical comparison of HS-SPME and TV-SPME is discussed. Samples including marijuana, essential oils, and CBD oil were utilized to compare the two techniques. The compounds of interest in marijuana are the three main cannabinoids: cannabinol (CBN), cannabidiol (CBD), and tetrahydrocannabinol (THC). The sample preparation and GC-MS parameters were kept the same for all samples to determine which SPME technique works best for these sample types and yielded the greatest sensitivity. It was found that HS-SPME shows greater sensitivity with CBN and equivalent sensitivity with essential oils, THC and CBD. </p><p>In Chapter 2, the detection of synthetic cannabinoids utilizing liquid-liquid injection as well as HS-SPME and TV-SPME is discussed. The detection of these compounds is important because this type of drug has become more prevalent in the United States because they can be chemically altered slightly so they still have the effects of a drug but can evade drug legislation. The detection of synthetic cannabinoids using liquid injection was found to be successful but detection using HS-SPME and TV-SPME was found to be unsuccessful. </p>In Chapter 3, the analyses of real and artificial saliva utilizing HS-SPME and TV-SPME is discussed. Determining the compounds present in real saliva and artificial saliva will be of importance for future research into determining if the presence of drugs in saliva can be analyzed with these techniques. The analyses of real and artificial saliva were found to be successful using HS-SPME, without derivatization, and TV-SPME, with and without derivatization. Many of the compounds present in the real saliva were detected and were confirmed to be compounds regularly found in saliva by other scientific literature.

Indiana University-Purdue University Indianapolis (IUPUI) / Solid Phase Microextraction (SPME) is a popular sampling technique that can be paired with Gas Chromatography/Mass Spectrometry (GC-MS). SPME-GC-MS is used in forensic chemistry due to its simplification of the sample preparation process. Headspace-Solid Phase Microextraction (HS-SPME) is a technique where the sample is heated to generate volatiles in the headspace of the vial. A SPME fiber is then inserted into the vial and the compounds in the headspace will bind to the fiber. Total Vaporization- Solid Phase Microextraction (TV-SPME) is a technique that is derived from the HS-SPME technique. In Chapter 1, the critical comparison of HS-SPME and TV-SPME is discussed. Samples including marijuana, essential oils, and CBD oil were utilized to compare the two techniques. The compounds of interest in marijuana are the three main cannabinoids: cannabinol (CBN), cannabidiol (CBD), and tetrahydrocannabinol (THC). The sample preparation and GC-MS parameters were kept the same for all samples to determine which SPME technique works best for these sample types and yielded the greatest sensitivity. It was found that HS-SPME shows greater sensitivity with CBN and equivalent sensitivity with essential oils, THC and CBD. In Chapter 2, the detection of synthetic cannabinoids utilizing liquid-liquid injection as well as HS-SPME and TV-SPME is discussed. The detection of these compounds is important because this type of drug has become more prevalent in the United States because they can be chemically altered slightly so they still have the effects of a drug but can evade drug legislation. The detection of synthetic cannabinoids using liquid injection was found to be successful but detection using HS-SPME and TV-SPME was found to be unsuccessful. In Chapter 3, the analyses of real and artificial saliva utilizing HS-SPME and TV-SPME is discussed. Determining the compounds present in real saliva and artificial saliva will be of importance for future research into determining if the presence of drugs in saliva can be analyzed with these techniques. The analyses of real and artificial saliva were found to be successful using HS-SPME, without derivatization, and TV-SPME, with and without derivatization. Many of the compounds present in the real saliva were detected and were confirmed to be compounds regularly found in saliva by other scientific literature.

NR 20140805