Dissertations / Theses on the topic 'Fluorescence hyperspectral imaging (fHSI)'

This report deals with the possibility of creating a LED light source, to be used withhyperspectral fluorescence imaging. There are commercially available light sources thatcould be used, but they are expensive, they do not necessarily emit the right wavelength, the uniformity of the field is questionable and they are difficult to modify.First a batch of Light emitting diodes were acquired, these were subjected to a seriesof tests to classify their limitations and determine which diodes were to be included in the final light source. A spectrometer was used to determine the emitted wavelength of each diode and which scenarios could change the wavelength of the emitted light. Aphotodiode was used to acquire the viewing angle of the LEDs and their relative radiantpower. Images gathered by a hyperspectral camera were used to determine the relevancyof noise produced by the current source. When the light emitting diodes were chosen,the photodiode was used to make an image of the light field. The final light source wasmounted on the hyperspectral camera to gather fluorescent images.The final tests revealed a fully functional light source with potential to be used on aregular basis, but the current source was too cumbersome and the field was not optimal.These are issues that can be dealt with and this light source can in the future provide a cheap and easily modifiable light source alternative.

The ability to resolve multiple fluorescent emissions from different biological targets in video rate applications, such as endoscopy and intraoperative imaging, has traditionally been limited by the use of filter-based imaging systems. Hyper and multispectral imaging facilitate the detection of both spatial and spectral information in a single data acquisition, however, instrumentation for spatiospectral data acquisition is typically complex, bulky and expensive. This thesis seeks to overcome these limitations by using recently commercialised compact and robust hyper/multispectral cameras based on spectrally resolved detector arrays. Following sensor calibrations, which devoted particular attention to the angular sensitivity of the sensors, we integrated spectrally resolved detector arrays into a wide-field and an endoscopic imaging platform. This allowed multiplexed reflectance and fluorescence imaging with spectrally resolved detector array technology in vitro, in tissue mimicking phantoms, in an ex vivo oesophageal model and in vivo in a mouse model. A hyperspectral linescan sensor was first integrated in a wide-field near-infrared reflectance based imaging set-up to assess the suitability of spectrally resolved detector arrays for in vivo imaging of exogenous fluorescent contrast agents. Using this fluorescence hyperspectral imaging system, we could accurately resolve the presence and concentration of seven fluorescent dyes in solution. We also demonstrated high spectral unmixing precision, signal linearity with dye concentration, at depth in tissue mimicking phantoms, and delineation of four fluorescent dyes in vivo. After the successful demonstration of multiplexed fluorescence imaging in a wide-field set-up, we proceeded to combine near-infrared multiplexed fluorescence imaging with visible light spectral reflectance imaging in an endoscopic set-up. A multispectral endoscopic imaging system, capable of simultaneous reflectance and fluorescence imaging, was developed around two snapshot spectrally resolved detector arrays. In the process of system integration and characterisation, methods to characterise and predict the imaging performance of spectral endoscopes were developed. With the endoscope we demonstrated simultaneous imaging and spectral unmixing of chemically oxy/deoxygenated blood and three fluorescent dyes in a tissue mimicking phantom, and of two fluorescent dyes in an ex vivo oesophageal porcine model. With further developments, this technology has the potential to become applicable in medical imaging for detection of diseases such as gastrointestinal cancers.

La fistule anastomotique (FA) est une complication grave de la chirurgie. Une perfusion locale adéquate est fondamentale pour réduire le risque de FA. Cependant, les critères cliniques ne sont pas fiables pour évaluer la perfusion intestinale. À cet égard, l'angiographie par fluorescence (AF) a été explorée. Malgré des résultats prometteurs dans les essais cliniques, l'évaluation de l'AF est subjective, d'où l'incertitude quant à son efficacité. L'AF quantitative a déjà été introduite. Cependant, elle est limitée par la nécessité d'injecter un fluorophore. L'imagerie hyperspectrale (HSI) est une technique d'imagerie optique prometteuse couplant un spectroscope à une caméra photo, permettant une analyse quantitative des tissus en temps réel et sans contraste. L'utilisation intraopératoire de l'HSI est limitée par la présence d'images statiques. Nous avons développé la hyperspectral-based enhanced reality (HYPER), pour permettre une évaluation précise de la perfusion intraopératoire. Cette thèse décrit les étapes du développement et de la validation d'HYPER<br>Anastomotic leak (AL) is a severe complication in surgery. Adequate local perfusion is fundamental to promote anastomotic healing, reducing the risk of AL. However, clinical criteria are unreliable to evaluate bowel perfusion. Consequently, a tool allowing to objectively detect intestinal viability intraoperatively is desirable. In this regard, fluorescence angiography (FA) has been explored. In spite of promising results in clinical trials, FA assessment is subjective, hence the efficacy of FA is unclear. Quantitative FA has been previously introduced. However, it is limited by the need of injecting a fluorophore. Hyperspectral imaging (HSI) is a promising optical imaging technique coupling a spectroscope with a photo camera, allowing for a contrast-free, real-time, and quantitative tissue analysis. The intraoperative usability of HSI is limited by the presence of static images. We developed hyperspectral-based enhanced reality (HYPER), to allow for precise intraoperative perfusion assessment. This thesis describes the steps of the development and validation of HYPER

Dans le domaine de l’analyse de défaillance et plus particulièrement la contamination moléculaire et particulaire, il est crucial de pouvoir détecter toute trace de contaminants durant l’intégration d’un engin orbital. Dans ce contexte, la fluorescence permet non seulement de détecter mais aussi de discriminer les contaminants. Pour ce projet, nous avons donc développé un instrument hyperspectral large-bande (UV-Vis-NIR) de 330 à 1000 nm pour pouvoir détecter un large panel de contaminants. Il s’agit d’un montage catoptrique permettant de s’affranchir des aberrations chromatiques. Il présente un champ de vue de 3,5° pour une résolution angulaire de 25 secondes d’arc. Il a été conçu pour être portable et son ensemble mécanique figé permet un alignement optique simple à mettre en œuvre et une réalisation rapide des fichiers de calibration entre deux scènes. Nous avons mesuré une résolution spectrale de 1 nm dans l’UV, 2 à 3 nm dans le visible et 5 nm dans le NIR. Cela nous a permis d’étudier la réponse en fluorescence de deux colles époxy, sources typiques de la contamination d’engin orbital et de la comparer avec une mesure obtenue avec un instrument commercial. Ces mesures nous ont permis d’évaluer les performances de notre instrument et d’identifier des perspectives d’amélioration, notamment en termes de sensibilité dans les UV<br>In the field of failure analysis and in particular molecular and particulate contamination, being able to detect any trace of contaminants during the integration of an orbital spacecraft is crucial. In this context, fluorescence allows not only to detect but also to discriminate contaminants. For this project, we have therefore developed a broadband hyperspectral instrument (UV-Vis-NIR) from 330 to 1000 nm to be able to detect a wide range of contaminants. It is a catoptric assembly that eliminates chromatic aberrations. The field of view is 3.5° for an angular resolution of 25 arc seconds. It was designed to be portable and its fixed mechanical assembly allows easy optical alignment and rapid creation of calibration files between two scenes. We measured a spectral resolution of 1 nm in the UV range, 2 to 3 nm in the visible range and 5 nm in the NIR range. This allowed us to study the fluorescence response of two epoxy glues, typical sources of orbital spacecraft contamination, and to compare it with a measurement obtained with a commercial instrument. These measurements allowed us to evaluate the performance of our instrument and identify prospects of improvement, especially in terms of sensitivity in UV range

A major goal in biological imaging is to visualize interactions of different tissues, often fluorescently labeled, during dynamic processes. Only a few of these labels fit into the available spectral range without overlap, but can be separated computationally if the full spectrum of every single pixel is known. In medical imaging, hyperspectral techniques show promise to identify different tissue types without any staining. Yet, microscopists still commonly acquire spectral information either with filters, thus integrating over a few broad bands only, or point-wise, dispersing the spectra onto a multichannel detector, which is inherently slow. Light sheet fluorescence microscopy (LSFM) and optical projection tomography (OPT) are two techniques to acquire 3D microscopic data fast, photon-efficiently and gently on the specimen. LSFM works in fluorescence mode and OPT in transmission. Both are based on a fast widefield detection scheme where a 2D detector records the spatial information but leaves no room to acquire dispersed spectra. Hyperspectral imaging had not yet been demonstrated for either technique. In this work, I developed a line-scanning hyperspectral LSFM and an excitation scanning OPT to acquire 5D data (3D spatial, 1D temporal, 1D spectral) and optimized the performance of both setups to minimize acquisition times without sacrificing image contrast, spatial or spectral information. I implemented and assessed different evaluation pipelines to classify and unmix relevant features. I demonstrate the efficiency of my workflow by acquiring up to five fluorescent markers and the autofluorescence in \\zf and fruit fly embryos on my hyperspectral LSFM. I extracted both concentration maps and spectra for each of these fluorophores from the multidimensional data. The same methods were applied to investigate the transmission data from my spectral OPT, where I found evidence that OPT image formation is governed by refraction, whereas scattering and absorption only play a minor role. Furthermore, I have implemented a robust, educational LSFM on which laymen have explored the working principles of modern microscopies. This eduSPIM has been on display in the Technische Sammlungen Dresden for one year during the UNESCO international year of light<br>Ein wichtiges Ziel biologischer Bildgebung ist die Visualisierung des Zusammenspiels von verschiedenen, meist fluoreszent markierten, Geweben bei dynamischen Prozessen. Nur wenige dieser Farbstoffe passen ohne Überlapp in das zur Verfügung stehende Spektrum. Sie können jedoch rechnerisch getrennt werden, wenn das gesamte Spektrum jedes Pixels bekannt ist. In medizinischen Anwendungen versprechen hyperspektrale Techniken, verschiedene Gewebetypen markierungsfrei zu identifizieren. Dennoch ist es in der Mikroskopie noch immer üblich, spektrale Information entweder mit Filtern über breiten Bändern zu integrieren, oder Punktspektren mithilfe von Dispersion zu trennen und auf einem Multikanaldetektor aufzunehmen, was inhärent langsam ist. Light Sheet Fluorescence Microscopy (LSFM) und Optical Projection Tomography (OPT) nehmen 3D Mikroskopiedaten schnell, photoneneffizient und sanft für die Probe auf. LSFM arbeitet mit Fluoreszenz, OPT in Transmission. Beide basieren auf schneller Weitfelddetektion, wobei die räumliche Information mit einem 2D Detektor aufgenommen wird, der keinen Raum lässt, um die getrennten Spektren zu messen. Hyperspektrale Bildgebung wurde bis jetzt für keine der zwei Techniken gezeigt. Ich habe ein hyperspektrales LSFM mit Linienabtastung und ein OPT mit Wellenlängenabtastung entwickelt, um 5D Daten (3D räumlich, 1D zeitlich, 1D spektral) aufzunehmen. Beide Aufbauten wurden hinsichtlich minimaler Aufnahmezeit optimiert, ohne dabei Kontrast, räumliche oder spektrale Auflösung zu opfern. Ich habe verschiedene Abläufe zum Klassifizieren und Trennen der Hauptkomponenten implementiert. Ich nehme bis zu fünf Fluorophore und Autofluoreszenz in Zebrafisch- und Fruchtfliegenembryos mit dem hyperspektralen LSFM auf und zeige die Effizienz des gesamten Ablaufes, indem ich Spektren und räumliche Verteilung aller Marker extrahiere. Die Transmissionsdaten des spektralen OPT werden mit denselben Methoden untersucht. Ich konnte belegen, dass die Bildformation im OPT massgeblich von Brechung bestimmt ist, und Streuung und Absorption nur einen geringen Beitrag leisten. Außerdem habe ich ein robustes, didaktisches LSFM gebaut, damit Laien die Funktionsweise moderner Mikroskopie erkunden können. Dieses eduSPIM war ein Jahr lang in den Technischen Sammlungen Dresden ausgestellt

L'imagerie hyperspectrale consiste à acquérir une scène spatiale à plusieurs longueurs d'onde, de manière à reconstituer le spectre des pixels. Cette technique est utilisée en microscopie pour extraire des informations spectrales sur les spécimens observés. L'analyse de telles données est bien souvent difficile : lorsque l'image est observée à une résolution suffisamment fine, elle est dégradée par l'instrument (flou ou convolution) et une procédure de déconvolution doit être utilisée pour restaurer l'image originale. On parle ainsi de problème inverse, par opposition au problème direct consistant à modéliser la dégradation de l'image observée, étudié dans la première partie de la thèse. Un autre problème inverse important en imagerie consiste à extraire les signatures spectrales des composants purs de l'image ou sources et à estimer les contributions fractionnaires de chaque source à l'image. Cette procédure est qualifiée de séparation de sources, accomplie sous contrainte de positivité des sources et des mélanges. La deuxième partie propose des contributions algorithmiques en restauration d'images hyperspectrales. Le problème de déconvolution est formulé comme la minimisation d'un critère pénalisé et résolu à l'aide d'une structure de calcul rapide. Cette méthode générique est adaptée à la prise en compte de différents a priori sur la solution, tels que la positivité de l'image ou la préservation des contours. Les performances des techniques proposées sont évaluées sur des images de biocapteurs bactériens en microscopie confocale de fluorescence. La troisième partie de la thèse est axée sur la problématique de séparation de sources, abordé dans un cadre géométrique. Nous proposons une nouvelle condition suffisante d'identifiabilité des sources à partir des coefficients de mélange. Une étude innovante couplant le modèle d'observation instrumental avec le modèle de mélange de sources permet de montrer l'intérêt de la déconvolution comme étape préliminaire de la séparation. Ce couplage est validé sur des données acquises en microscopie confocale Raman.

L'imagerie hyperspectrale consiste à acquérir une scène spatiale à plusieurs longueurs d'onde, e.g. en microscopie. Cependant, lorsque l'image est observée à une résolution suffisamment fine, elle est dégradée par un flou (convolution) et une procédure de déconvolution doit être utilisée pour restaurer l'image originale. Ce problème inverse, par opposition au problème direct modélisant la dégradation de l'image observée, est étudié dans la première partie . Un autre problème inverse important en imagerie, la séparation de sources, consiste à extraire les spectres des composants purs de l'image (sources) et à estimer les contributions de chaque source à l'image. La deuxième partie propose des contributions algorithmiques en restauration d'images hyperspectrales. Le problème est formulé comme la minimisation d'un critère pénalisé et résolu à l'aide d'une structure de calcul rapide. La méthode est adaptée à la prise en compte de différents a priori sur l'image, tels que sa positivité ou la préservation des contours. Les performances des techniques proposées sont évaluées sur des images de biocapteurs bactériens en microscopie confocale de fluorescence. La troisième partie est axée sur le problème de séparation de sources, abordé dans un cadre géométrique. Nous proposons une nouvelle condition suffisante d'identifiabilité des sources à partir des coefficients de mélange. Une étude innovante couplant le modèle d'observation avec le mélange de sources permet de montrer l'intérêt de la déconvolution comme étape préliminaire de la séparation. Ce couplage est validé sur des données acquises en spectroscopie Raman<br>Hyperspectral imaging refers to the acquisition of spatial images at many spectral bands, e.g. in microscopy. Processing such data is often challenging due to the blur caused by the observation system, mathematically expressed as a convolution. The operation of deconvolution is thus necessary to restore the original image. Image restoration falls into the class of inverse problems, as opposed to the direct problem which consists in modeling the image degradation process, treated in part 1 of the thesis. Another inverse problem with many applications in hyperspectral imaging consists in extracting the pure materials making up the image, called endmembers, and their fractional contribution to the data or abundances. This problem is termed spectral unmixing and its resolution accounts for the nonnegativity of the endmembers and abundances. Part 2 presents algorithms designed to efficiently solve the hyperspectral image restoration problem, formulated as the minimization of a composite criterion. The methods are based on a common framework allowing to account for several a priori assumptions on the solution, including a nonnegativity constraint and the preservation of edges in the image. The performance of the proposed algorithms are demonstrated on fluorescence confocal images of bacterial biosensors. Part 3 deals with the spectral unmixing problem from a geometrical viewpoint. A sufficient condition on abundance coefficients for the identifiability of endmembers is proposed. We derive and study a joint observation model and mixing model and demonstrate the interest of performing deconvolution as a prior step to spectral unmixing on confocal Raman microscopy data

碩士<br>國立中央大學<br>光電科學與工程學系<br>104<br>Chlorophyll fluorescence is considered as a non-invasive method to monitor the physiological state of plants and is capable to apply for higher plants researches. A home-built line scanning two-photon fluorescence hyperspectral microscopy is used to record the chlorophyll fluorescence hyperspectral images containing 3D spatial and 1D spectral information in this research. The hyperspectral images within 120 μm beneath the leaf surface can be recorded. In this research, the principle component analysis method and cryogenic fluorescence spectrum measurement are applied to analyze the spectrum bases of Photosystem I and Photosystem II. To correct the re-absorption effect in spectrum analysis, absorption spectrum of intact leaf is measured and used to build the fitting function. Using least squares method, the fluorescence spectra of mesophyll cells at various locations in three-dimensional are fitted and analyzed. Through the errors between the measured and fitted fluorescence spectra, the fitting method used in this research is verified to be applicable for analysis of chlorophyll fluorescence.

碩士<br>國立臺灣大學<br>生物產業機電工程學研究所<br>102<br>Rice is typically consumed after milling, a process of removing the husk and bran layers on rice surface. The degree of bran residue remaining on rice surface after milling directly affects the rice quality. And the bran layer of rice mainly composed of lipids. This work proposed to nondestructively detect bran residue on single rice grain using hyperspectral imaging (HSI) and fluorescence imaging (FRI). HSI and FRI are sensing techniques that combines both spatial and spectral information and may be used for chemical compound identification and quantification. In the HSI experiment, rice samples were milled and scanned using an HSI system. Afterward, the rice samples were dyed to enable the residual bran to be identified using optical microscopy and image processing algorithms. Classifiers were then developed to predict the rice bran residue by using the HSI measurements as inputs. In the FRI experiment, appropriate combinations of fluorescence excitation and emission wavelengths were identified. Fluorescence images of rice samples at these excitation and emission wavelength combinations were then acquired and were used as the inputs to the machine learning classifier. After that, the same staining procedure and model development performed in the HSI work were applied. Bran image were predicted by using the fluorescence images. The predicted images were compared with the micrograph images for classifier performance evaluation. The proposed HSI and FRI approaches could reasonably estimate the residual bran distribution on milled rice surface.

碩士<br>國立臺灣大學<br>生物產業機電工程學研究所<br>106<br>In recent years there’s lots of food safety issue around the world, according to the paper review and market survey we conclude that oil adulteration is one of the most commonly event in actual problems, whereas traditional inspection process takes time and laborious so as a respond this research aims to discover the potential of using Hyperspectral fluorescence imaging to assess the oil concentration. In this research, Ghee is the adulteration target, which will be adulterated with Rice bran, Canola, Olive oil in five concentration level (100%, 75%, 50%, 25%, 0%) respectively, first the use of Excitation and emission matrix gives the information about what exact excitation light should be used to induce fluorescence, use this excitation as a light source in Hyperspectral imaging system to get the fluorescence spectrum for each oil mixture, and build the ANN model to classify the adulterated type, for pure Ghee, adulterated Rice bran, Canola, Olive oil the accuracy approaching 7.33%, 99.50%, 100.00%, 68.25% respectively, SVR model for predicting the Ghee concentration in oil mixture reaching averaging error 88.53%, 97.4%, 82.13%, 59.5% respectively, furthermore, if also takes the different brand factor for each plant oil into account, the accuracy for ANN still retain 100%, 96%, 93.5% respectively, for SVR model we have averaging error 15.00%, 7.61%, 11.31% respectively. For ANN classification accuracy of portable detection device also reaching 97.00%, 100.00%, 100.00%, 100.00%, for SVR model also reaching average error 7.18%, 10.16%, 11.92%, respectively.

Microscopic imaging in the biomedical sciences allows for detailed study of the structure and function of normal and abnormal (i.e., diseased) states of cells and tissues. The expression patterns of proteins and/or physiological parameters within these specimens can be related to disease progression and prognosis, and are often heterogeneously spread throughout the entire specimen. With conventional microscopy, a large number of individual image ‘tiles’ must be captured and subsequently combined into a mosaic of the entire specimen. This has the potential to introduce artefacts at the image seams, as well as introducing non-uniform illumination of the entire specimen. A further limitation often encountered in biomedical fluorescence microscopy is the high background due to the autofluorescence (AF) of endogenous compounds within cells and tissues. Often, AF can prevent the detection and/or accurate quantification in fluorescently- labelled tissues and, in general, can reduce the reliability of results obtained from such specimens. AF spectra are relatively broad and so can be present across a large number of image spectral channels. The intensity of AF also increases as the excitation wavelength is decreased, causing increasing amounts of autofluorescence when exciting in the blue and near-UV range of the spectrum (400 - 500 nm). This thesis reports the development of hyperspectral, fluorescence and brightfield imaging of entire, paraffin-embedded, formalin-fixed (PEFF) tissue slides using a prototype confocal scanner with a large field of view (FOV). This technology addresses the challenges of imaging large tissue sections through the use of a telecentric f-theta laser scan lens thus allowing an entire microscope slide (22x70 mm) to be imaged in a single scan at resolution equivalent to a 10x microscope objective. The development and optimization of brightfield and single-channel fluorescence imaging modes are discussed in the first half of this thesis, while the second half and appendices concentrate on the spectral properties of the system and removal of AF from PEFF tissue sections. The hyperspectral imaging mode designed for this system allows the fluorescence emission spectrum of each image pixel to be sampled at 6.7 nm/channel over a spectral range of 500-700 nm. This results in the ability to separate distinct fluorescence signatures from each other, and enables quantification even in situations where the AF completely masks the signal from the applied labels.

Tissue optical properties, absorption, scattering and fluorescence, reveal important information about health, and holds the potential for non-invasive diagnosis and therefore earlier treatment for many diseases. On the other hand, tissue structure determines its function. Studying tissue structural properties helps us better understand structure-function relationship. Optical imaging is an ideal tool to study these tissue properties. However, conventional optical imaging techniques have limitations, such as not being able to quantitatively evaluate tissue absorption and scattering properties and only providing volumetrically averaged quantities with no depth control capability. To better study tissue properties, we integrated spatial frequency domain imaging (SFDI) with conventional reflectance imaging modalities. SFDI is a non-invasive, non-contact wide-field imaging technique which utilizes structured illumination to probe tissues. SFDI imaging is able to accurately quantify tissue optical properties. By adjusting spatial frequency, the imaging depth can be tuned which allows for depth controlled imaging. Especially at high spatial frequency, SFDI reflectance image is more sensitive to tissue scattering property than absorption property. The imaging capability of SFDI allows for studying tissue properties from a whole new perspective. In our study, we developed both benchtop and handheld SFDI imaging systems to accommodate different applications. By evaluating tissue optical properties, we corrected attenuation in fluorescence imaging using an analytical model; and we quantified optical and physical properties of skin diseases. By imaging at high spatial frequency, we demonstrated that absorption in fluorescence imaging can also be reduced because of a reduced imaging depth. This correction can be performed in real-time at 19 frames/second. Furthermore, fibrous structures orientation from the superficial layer can be accurately quantified in a multi-layered sample by limiting imaging depth. Finally, we color rendered SFDI reflectance image at high spatial frequency to reveal structural changes in skin lesions.

Les nanomatériaux sont une classe de contaminants qui est de plus en plus présent dans l’environnement. Leur impact sur l’environnement dépendra de leur persistance, mobilité, toxicité et bioaccumulation. Chacun de ces paramètres dépendra de leur comportement physicochimique dans les eaux naturelles (i.e. dissolution et agglomération). L’objectif de cette étude est de comprendre l’agglomération et l’hétéroagglomération des nanoparticules d’argent dans l’environnement. Deux différentes sortes de nanoparticules d’argent (nAg; avec enrobage de citrate et avec enrobage d’acide polyacrylique) de 5 nm de diamètre ont été marquées de manière covalente à l’aide d’un marqueur fluorescent et ont été mélangées avec des colloïdes d’oxyde de silice (SiO2) ou d’argile (montmorillonite). L’homo- et hétéroagglomération des nAg ont été étudiés dans des conditions représentatives d’eaux douces naturelles (pH 7,0; force ionique 10 7 à 10-1 M de Ca2+). Les tailles ont été mesurées par spectroscopie de corrélation par fluorescence (FCS) et les résultats ont été confirmés à l’aide de la microscopie en champ sombre avec imagerie hyperspectrale (HSI). Les résultats ont démontrés que les nanoparticules d’argent à enrobage d’acide polyacrylique sont extrêmement stables sous toutes les conditions imposées, incluant la présence d’autres colloïdes et à des forces ioniques très élevées tandis que les nanoparticules d’argent avec enrobage de citrate ont formées des hétéroagrégats en présence des deux particules colloïdales.<br>Nanomaterials are a class of contaminants that are increasingly found in the natural environment. Their environmental risk will depend on their persistence, mobility, toxicity and bioaccumulation. Each of these parameters will depend strongly upon their physicochemical fate (dissolution, agglomeration) in natural waters. The goal of this paper is to understand the agglomeration and heteroagglomeration of silver nanoparticles in the environment. Two different silver nanoparticles (nAg; citrate coated and polyacrylic acid coated) with a diameter of 5 nm were covalently labelled with a fluorescent dye and then mixed with colloidal silicon oxides (SiO2) and clays (montmorillonite). The homo- and heteroagglomeration of the silver nanoparticles were then studied in waters that were representative of natural freshwaters (pH 7.0; ionic strength 10-7 to 10-1 M of Ca2+). Sizes were followed by fluorescence correlation spectroscopy (FCS) and results were validated using enhanced darkfield microscopy with hyperspectral imaging (HSI). Results have demonstrated that the polyacrylic acid coated nAg was extremely stable under all conditions, including in the presence of other colloids and at high ionic strength, whereas the citrate coated nAg formed heteroagregates in the presence of both natural colloidal particles.

Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision<br>making in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.<br>

Optics are a powerful probe of chemical structure that can often be linked to theoretical predictions, providing robustness as a measurement tool. Not only do optical interactions like second harmonic generation (SHG), single and two-photon excited fluorescence (TPEF), and infrared absorption provide chemical specificity at the molecular and macromolecular scale, but the ability to image enables mapping heterogeneous behavior across complex systems such as biological tissue. This thesis will discuss nonlinear and linear optics, leveraging theoretical predictions to provide frameworks for interpreting analytical measurement. In turn, the causal mechanistic understanding provided by these frameworks will enable structurally specific quantitative tools with a special emphasis on application in biological imaging. The thesis will begin with an introduction to 2nd order nonlinear optics and the polarization analysis thereof, covering both the Jones framework for polarization analysis and the design of experiment. Novel experimental architectures aimed at reducing 1/f noise in polarization analysis will be discussed, leveraging both rapid modulation in time through electro-optic modulators (Chapter 2), as well as fixed-optic spatial modulation approaches (Chapter 3). In addition, challenges in polarization-dependent imaging within turbid systems will be addressed with the discussion of a theoretical framework to model SHG occurring from unpolarized light (Chapter 4). The application of this framework to thick tissue imaging for analysis of collagen local structure can provide a method for characterizing changes in tissue morphology associated with some common cancers (Chapter 5). In addition to discussion of nonlinear optical phenomena, a novel mechanism for electric dipole allowed fluorescence-detected circular dichroism will be introduced (Chapter 6). Tackling challenges associated with label-free chemically specific imaging, the construction of a novel infrared hyperspectral microscope for chemical classification in complex mixtures will be presented (Chapter 7). The thesis will conclude with a discussion of the inherent disadvantages in taking the traditional paradigm of modeling and measuring chemistry separately and provide the multi-agent consensus equilibrium (MACE) framework as an alternative to the classic meet-in-the-middle approach (Chapter 8). Spanning topics from pure theoretical descriptions of light-matter interaction to full experimental work, this thesis aims to unify these two fronts. <br>