In today’s technically advanced society the collection and study of digital images has become an important aspect of various off-line applications that range from medical diagnosis to exploring the Martian surface for traces of water. Various industries have recently started moving towards vision based systems to monitor several of their manufacturing processes. Except for some simple on-line applications, these systems are primarily used to analyze the digital images off-line. This thesis is concerned with developing a more powerful on-line digital image analysis technique which links the fields of traditional digital image processing with a recently devised statistically based image analysis method called multivariate image analysis (MIA). The first part of the thesis introduces the area of traditional digital image processing techniques through a brief literature review of three of its five main classes (image enhancement, restoration, analysis, compression, & synthesis) which contain most of the commonly used operations in this area. This introduction is intended as a starting point for readers who have little background in this field, and as a means of providing sufficient details on these techniques so that they can be used in conjunction with other advanced MIA on-line monitoring operations. MIA of multispectral digital images using latent variable statistical methods (Multi-Way PCA / PLS) is the main topic covered by the second part of this thesis. After reviewing the basic theory of feature extraction using MIA for off-line analyses, a new technique is introduced that extends these ideas for image analyses in on-line applications. Instead of directly using the updated images themselves to monitor a time- varying process, this new technique uses the latent variable space of the image to monitor the increase or decline in the number of pixels belonging to various features of interest. The ability to switch between the images and their latent variable space then allows the user to determine the exact spatial locations of any features of interest. This new method is shown to be ideal for monitoring interesting features from time-varying processes equipped with multispectral sensors. It forms a basis for future on-line industrial process monitoring schemes in those industries that are moving towards automatic vision systems using multispectral digital imagery. / Thesis / Master of Engineering (ME)

Orientador: Ines Joekes / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-09T00:07:10Z (GMT). No. of bitstreams: 1 Bonalume_Clauber_M.pdf: 1295510 bytes, checksum: 20c20d40e0b0a19af56f06a1076fb4cb (MD5) Previous issue date: 2006 / Resumo: Corais marinhos do mundo inteiro estão ameaçados pelas alterações climáticas que vêm acontecendo nas duas últimas décadas. O aquecimento da superfície terrestre associado à intensificação do fenômeno El Niño têm causado prejuízos à saúde dos corais marinhos com intensidade, extensão e escala temporal sem precedentes. Quando expostos a situações de estresse ambiental, corais podem sofrer branqueamento, perdendo suas algas simbiontes, as zooxantelas, e morrer se não as recuperarem. Desta forma, métodos de avaliação da saúde dos corais tornam-se necessários. Dois métodos fotométricos foram desenvolvidos neste trabalho. No primeiro, utilizou-se a técnica de reflectância difusa para estudo do histórico de branqueamento de fatias de núcleos de esqueletos coralinos através da análise da cor do núcleo na direção do crescimento do coral. Esse método mostrou-se sensível à variação de cor ao longo da amostra e foi capaz de evidenciar o histórico conhecido de branqueamento de 2 entre 3 amostras analisadas. O método é promissor para o estudo de esqueletos, mas depende sensivelmente do tratamento prévio das amostras. No segundo, fotografaram-se amostras de Mussismilia híspida em seu ambiente natural na Laje de Santos. Foram colhidas amostras dos corais fotografados e foi determinada a densidade de zooxantelas em cada amostra. Utilizando o software MatLab e uma rotina específica de análise multivariada, estabeleceu-se uma correlação entre aspectos da imagem digital e a densidade de zooxantelas. A correlação obtida é boa, com erros menores que 35 % na estimativa da densidade de zooxantelas a partir das imagens digitais. O método é bastante promissor e tem a vantagem de ser não destrutivo. / Abstract: In the last two decades marine corals around the world have been threatened by weather global changes. Coral health is declining at unprecedented intensity, extension and temporal scale, by world surface heating associated with the strengthening of the El Niño Southern Oscillation. When exposed to environmental stresses coral can bleach, loosing their symbiotic algae, the zooxanthellae, and die if they are permanently lost. The development of methods capable to assess coral health is necessary. Two photometric methods were developed. In the first one, difuse reflectance spectrophotomety was used to analize the bleaching history of slices of coral skeletons by color analysis along the growing direction of the colony. The method showed apropriate sensibility for measuring color changes along the samples and was able to disclose the bleaching history in two of the three samples studied. However, results depend strongly on the previous treatment of the skeleton samples. In the second method, samples of Mussismilia híspida coral colonies were photographed in their natural habitat in the Laje de Santos marine conservation park. Small samples of the photographed coral were taken and the density of zooxanthellae in each sample was measured. Using the MatLab sofware and a specific algorithm for multivariated image analisys (MIA), a correlation between features of the images and the density of zooxanthellae was established. The correlation obtained is fairly good, with less than 35 % error in the estimation of zooxanthellae density from the digital images. The method is quite promissing and has the advantage of being nondestructive. / Mestrado / Físico-Química / Mestre em Química

The ladle metallurgical furnace (LMF) is a very flexible and common unit operation found in most steelmaking melt shops, and enables the adjustment and fine-tuning of molten steel's composition and temperature prior to casting. Despite the importance of ladle metallurgy to the overall steel making process very little has been achieved in the way of advanced ladle control. Limited sensors are available to monitor heat progress during refining and current control methods involve manual procedures. This thesis represents part of an ongoing study on the modelling of a full-scale LMF in real-time with the forward goal of improved control and optimization. The first part of this thesis details a vision-based sensor for analyzing ladle eye dynamics online using a multivariate image analysis (MIA) technique based on principal component analysis (PCA). Predictive capabilities of the developed model are demonstrated using previously published cold model data over a wide range of operating variables. Further, preliminary work has confirmed the ability of the sensor for potential use in an industrial setting. The second part of this study concerns the development of metallurgical models for assessing the state of a ladle metallurgical furnace. Specifically, a multi-component kinetic model in combination with developed slag and steel thermodynamic solution models were used to quantitatively describe the kinetics of slag-metal reactions within 41 industrially sampled heats at ArcelorMittal Dofasco's LMF#2. Metal phase mass transfer coefficients for all elements in steel were assumed to follow the empirical relation derived from measured sulphur contents, while slag phase mass transfer coefficients were calculated by fitting the ratio of k^Mm/ k^MxOysl to the experimental results. On the basis of the fitted results, slag phase mass transfer coefficient correlations were evaluated using linear regression. Computed results from the model using these slag phase mass transfer coefficient correlations were found to be consistent with the experimental data. In regard to the developed thermodynamic solution models, original contributions to the modified interaction parameter formalism and cell model are presented. As process model predictions are invariably uncertain, the final part of this work involves the use of a stochastic model (extended Kalman filter) to account for process disturbances, model-mismatch and other sources of uncertainty that may result in significant error propagation causing poor process control and plant economics. Several case studies were performed to illustrate the effectiveness of the extended Kalman filter and its application to optimal sensor selection was introduced. / Thesis / Doctor of Philosophy (PhD)

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The present Ph.D. thesis is devoted to study, develop and apply approaches commonly used in chemometrics to the emerging field of systems biology. Existing procedures and new methods are applied to solve research and industrial questions in different multidisciplinary teams. The methodologies developed in this document will enrich the plethora of procedures employed within omic sciences to understand biological organisms and will improve processes in biotechnological industries integrating biological knowledge at different levels and exploiting the software packages derived from the thesis. This dissertation is structured in four parts. The first block describes the framework in which the contributions presented here are based. The objectives of the two research projects related to this thesis are highlighted and the specific topics addressed in this document via conference presentations and research articles are introduced. A comprehensive description of omic sciences and their relationships within the systems biology paradigm is given in this part, jointly with a review of the most applied multivariate methods in chemometrics, on which the novel approaches proposed here are founded. The second part addresses many problems of data understanding within metabolomics, fluxomics, proteomics and genomics. Different alternatives are proposed in this block to understand flux data in steady state conditions. Some are based on applications of multivariate methods previously applied in other chemometrics areas. Others are novel approaches based on a bilinear decomposition using elemental metabolic pathways, from which a GNU licensed toolbox is made freely available for the scientific community. As well, a framework for metabolic data understanding is proposed for non-steady state data, using the same bilinear decomposition proposed for steady state data, but modelling the dynamics of the experiments using novel two and three-way data analysis procedures. Also, the relationships between different omic levels are assessed in this part integrating different sources of information of plant viruses in data fusion models. Finally, an example of interaction between organisms, oranges and fungi, is studied via multivariate image analysis techniques, with future application in food industries. The third block of this thesis is a thoroughly study of different missing data problems related to chemometrics, systems biology and industrial bioprocesses. In the theoretical chapters of this part, new algorithms to obtain multivariate exploratory and regression models in the presence of missing data are proposed, which serve also as preprocessing steps of any other methodology used by practitioners. Regarding applications, this block explores the reconstruction of networks in omic sciences when missing and faulty measurements appear in databases, and how calibration models between near infrared instruments can be transferred, avoiding costs and time-consuming full recalibrations in bioindustries and research laboratories. Finally, another software package, including a graphical user interface, is made freely available for missing data imputation purposes. The last part discusses the relevance of this dissertation for research and biotechnology, including proposals deserving future research. / Esta tesis doctoral se centra en el estudio, desarrollo y aplicación de técnicas quimiométricas en el emergente campo de la biología de sistemas. Procedimientos comúnmente utilizados y métodos nuevos se aplican para resolver preguntas de investigación en distintos equipos multidisciplinares, tanto del ámbito académico como del industrial. Las metodologías desarrolladas en este documento enriquecen la plétora de técnicas utilizadas en las ciencias ómicas para entender el funcionamiento de organismos biológicos y mejoran los procesos en la industria biotecnológica, integrando conocimiento biológico a diferentes niveles y explotando los paquetes de software derivados de esta tesis. Esta disertación se estructura en cuatro partes. El primer bloque describe el marco en el cual se articulan las contribuciones aquí presentadas. En él se esbozan los objetivos de los dos proyectos de investigación relacionados con esta tesis. Asimismo, se introducen los temas específicos desarrollados en este documento mediante presentaciones en conferencias y artículos de investigación. En esta parte figura una descripción exhaustiva de las ciencias ómicas y sus interrelaciones en el paradigma de la biología de sistemas, junto con una revisión de los métodos multivariantes más aplicados en quimiometría, que suponen las pilares sobre los que se asientan los nuevos procedimientos aquí propuestos. La segunda parte se centra en resolver problemas dentro de metabolómica, fluxómica, proteómica y genómica a partir del análisis de datos. Para ello se proponen varias alternativas para comprender a grandes rasgos los datos de flujos metabólicos en estado estacionario. Algunas de ellas están basadas en la aplicación de métodos multivariantes propuestos con anterioridad, mientras que otras son técnicas nuevas basadas en descomposiciones bilineales utilizando rutas metabólicas elementales. A partir de éstas se ha desarrollado software de libre acceso para la comunidad científica. A su vez, en esta tesis se propone un marco para analizar datos metabólicos en estado no estacionario. Para ello se adapta el enfoque tradicional para sistemas en estado estacionario, modelando las dinámicas de los experimentos empleando análisis de datos de dos y tres vías. En esta parte de la tesis también se establecen relaciones entre los distintos niveles ómicos, integrando diferentes fuentes de información en modelos de fusión de datos. Finalmente, se estudia la interacción entre organismos, como naranjas y hongos, mediante el análisis multivariante de imágenes, con futuras aplicaciones a la industria alimentaria. El tercer bloque de esta tesis representa un estudio a fondo de diferentes problemas relacionados con datos faltantes en quimiometría, biología de sistemas y en la industria de bioprocesos. En los capítulos más teóricos de esta parte, se proponen nuevos algoritmos para ajustar modelos multivariantes, tanto exploratorios como de regresión, en presencia de datos faltantes. Estos algoritmos sirven además como estrategias de preprocesado de los datos antes del uso de cualquier otro método. Respecto a las aplicaciones, en este bloque se explora la reconstrucción de redes en ciencias ómicas cuando aparecen valores faltantes o atípicos en las bases de datos. Una segunda aplicación de esta parte es la transferencia de modelos de calibración entre instrumentos de infrarrojo cercano, evitando así costosas re-calibraciones en bioindustrias y laboratorios de investigación. Finalmente, se propone un paquete software que incluye una interfaz amigable, disponible de forma gratuita para imputación de datos faltantes. En la última parte, se discuten los aspectos más relevantes de esta tesis para la investigación y la biotecnología, incluyendo líneas futuras de trabajo. / Aquesta tesi doctoral es centra en l'estudi, desenvolupament, i aplicació de tècniques quimiomètriques en l'emergent camp de la biologia de sistemes. Procediments comúnment utilizats i mètodes nous s'apliquen per a resoldre preguntes d'investigació en diferents equips multidisciplinars, tant en l'àmbit acadèmic com en l'industrial. Les metodologies desenvolupades en aquest document enriquixen la plétora de tècniques utilitzades en les ciències òmiques per a entendre el funcionament d'organismes biològics i milloren els processos en la indústria biotecnològica, integrant coneixement biològic a distints nivells i explotant els paquets de software derivats d'aquesta tesi. Aquesta dissertació s'estructura en quatre parts. El primer bloc descriu el marc en el qual s'articulen les contribucions ací presentades. En ell s'esbossen els objectius dels dos projectes d'investigació relacionats amb aquesta tesi. Així mateix, s'introduixen els temes específics desenvolupats en aquest document mitjançant presentacions en conferències i articles d'investigació. En aquesta part figura una descripació exhaustiva de les ciències òmiques i les seues interrelacions en el paradigma de la biologia de sistemes, junt amb una revisió dels mètodes multivariants més aplicats en quimiometria, que supossen els pilars sobre els quals s'assenten els nous procediments ací proposats. La segona part es centra en resoldre problemes dins de la metabolòmica, fluxòmica, proteòmica i genòmica a partir de l'anàlisi de dades. Per a això es proposen diverses alternatives per a compendre a grans trets les dades de fluxos metabòlics en estat estacionari. Algunes d'elles estàn basades en l'aplicació de mètodes multivariants propostos amb anterioritat, mentre que altres són tècniques noves basades en descomposicions bilineals utilizant rutes metabòliques elementals. A partir d'aquestes s'ha desenvolupat software de lliure accés per a la comunitat científica. Al seu torn, en aquesta tesi es proposa un marc per a analitzar dades metabòliques en estat no estacionari. Per a això s'adapta l'enfocament tradicional per a sistemes en estat estacionari, modelant les dinàmiques dels experiments utilizant anàlisi de dades de dues i tres vies. En aquesta part de la tesi també s'establixen relacions entre els distints nivells òmics, integrant diferents fonts d'informació en models de fusió de dades. Finalment, s'estudia la interacció entre organismes, com taronges i fongs, mitjançant l'anàlisi multivariant d'imatges, amb futures aplicacions a la indústria alimentària. El tercer bloc d'aquesta tesi representa un estudi a fons de diferents problemes relacionats amb dades faltants en quimiometria, biologia de sistemes i en la indústria de bioprocessos. En els capítols més teòrics d'aquesta part, es proposen nous algoritmes per a ajustar models multivariants, tant exploratoris com de regressió, en presencia de dades faltants. Aquests algoritmes servixen ademés com a estratègies de preprocessat de dades abans de l'ús de qualsevol altre mètode. Respecte a les aplicacions, en aquest bloc s'explora la reconstrucció de xarxes en ciències òmiques quan apareixen valors faltants o atípics en les bases de dades. Una segona aplicació d'aquesta part es la transferència de models de calibració entre instruments d'infrarroig proper, evitant així costoses re-calibracions en bioindústries i laboratoris d'investigació. Finalment, es proposa un paquet software que inclou una interfície amigable, disponible de forma gratuïta per a imputació de dades faltants. En l'última part, es discutixen els aspectes més rellevants d'aquesta tesi per a la investigació i la biotecnologia, incloent línies futures de treball. / Folch Fortuny, A. (2016). Chemometric Approaches for Systems Biology [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/77148 / TESIS / Premios Extraordinarios de tesis doctorales

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