Simultaneous blood perfusion and oxygenation monitoring is crucial for patients undergoing a transplant procedure. This becomes of great importance during the surgical recovery period of a transplant procedure when uncorrected loss of perfusion or reduction in oxygen saturation can result in patient death. Pulse oximeters are standard monitoring devices which are used to obtain the perfusion level and oxygen saturation using the optical absorption properties of hemoglobin. However, in cases of varying perfusion due to hemorrhage, blood clot or acute blockage, the oxygenation results obtained from traditional pulse oximeters are erroneous due to a sudden drop in signal strength. The long term goal of the project is to devise an implantable optical sensor which is able to perform better than the traditional pulse oximeters with changing perfusion and function as a local warning for sudden blood perfusion and oxygenation loss. In this work, an optical sensor based on a pulse oximeter with an additional source at 810nm wavelength has been developed for in situ monitoring of transplant organs. An algorithm has been designed to separate perfusion and oxygenation signals from the composite signal obtained from the three source pulse oximetry-based sensor. The algorithm uses 810nm reference signals and an adaptive filtering routine to separate the two signals which occur at the same frequency. The algorithm is initially applied to model data and its effectiveness is further tested using in vitro and in vivo data sets to quantify its ability to separate the signals of interest. The entire process is done in real time in conjunction with the autocorrelation-based time domain technique. This time domain technique uses digital filtering and autocorrelation to extract peak height information and generate an amplitude measurement and has shown to perform better than the traditional fast Fourier transform (FFT) for semi-periodic signals, such as those derived from heart monitoring. In particular, in this paper it is shown that the two approaches produce comparable results for periodic in vitro perfusion signals. However, when used on semi periodic, simulated, perfusion signals and in vivo data generated from an optical perfusion sensor the autocorrelation approach clearly (Standard Error, SE = 0.03) outperforms the FFT-based analysis (Standard Error, SE = 0.62).

In soils and sediments there is a strong coupling between local biogeochemical processes and the distribution of water, electron acceptors, acids and nutrients. Both sides are closely related and affect each other from small scale to larger scales. Soil structures such as aggregates, roots, layers or macropores enhance the patchiness of these distributions. At the same time it is difficult to access the spatial distribution and temporal dynamics of these parameter. Noninvasive imaging techniques with high spatial and temporal resolution overcome these limitations. And new non-invasive techniques are needed to study the dynamic interaction of plant roots with the surrounding soil, but also the complex physical and chemical processes in structured soils. In this study we developed an efficient non-destructive in-situ method to determine biogeochemical parameters relevant to plant roots growing in soil. This is a quantitative fluorescence imaging method suitable for visualizing the spatial and temporal pH changes around roots. We adapted the fluorescence imaging set-up and coupled it with neutron radiography to study simultaneously root growth, oxygen depletion by respiration activity and root water uptake. The combined set up was subsequently applied to a structured soil system to map the patchy structure of oxic and anoxic zones induced by a chemical oxygen consumption reaction for spatially varying water contents. Moreover, results from a similar fluorescence imaging technique for nitrate detection were complemented by a numerical modeling study where we used imaging data, aiming to simulate biodegradation under anaerobic, nitrate reducing conditions. / In Böden und Sedimenten sind biogeochemische Prozesse und die Verteilung von Größen wie Wasser, Elektronenakzeptoren, Säuregehalte und Nährstoffe in enger Weise miteinander gekoppelt. Diese wechselseitige Beeinflussung ist skalenübergreifend und reicht von sehr kleinen bis zu größeren Skalen. Die in realen Böden vorhandene Struktur z. Bsp. Aggregate, Pflanzenwurzeln, Schichten und Makroporen bedingen eine starke räumlich Heterogenität und zeitliche Dynamik dieser Größen. Gleichzeitig sind Verteilung und Dynamik sehr schwer zu beobachten, zumindest ohne ihre gleichzeitige Störung. Bildgebende Verfahren bieten eine sehr gute räumliche und zeitliche Auflösung und ermöglichen die Darstellung dieser Größen. Um die dynamische Wechselwirkung zwischen Pflanzenwurzeln und Boden, aber auch die komplexen physikalisch – chemischen Prozesse in Böden zu verstehen, sind neue bildgebende Verfahren notwendig. Ziel dieser Arbeit war es, eine neue nicht-invasive Methode zu entwickeln, die es ermöglicht biogeochemische Parameter in der Wurzelzone zu visualisieren. Innerhalb dieser Studie wurde ein quantitatives bildgebendes Verfahren entwickelt, dass die räumlichen und zeitlichen Dynamiken des pH Wertes in der Rhizosphäre erfasst. Diese auf Fluoreszenzemissionen basierende Methode wurde ebenso für Sauerstoffdetektion entwickelt und mit Neutronen Radiographie kombiniert um gleichzeitig Aussagen über Wurzelwachstum, Sauerstoffzehrung durch Wurzelatmung und Wurzelwasseraufnahme treffen zu können. Die kombinierte bildgebende Methode wurde dann in einem künstlichen Boden genutzt um Nischen und Übergangsbereiche von Sauerstoff bei variierenden Wassergehalten zu charakterisieren. Das große Potential von bildgebenden Verfahren zeigt sich bei Modellierungsstudien. In dieser Studie wurden Bilddaten als Eingabeparameter für die Simulierung von denitrifizierendem biologischem Schadstoffabbau genutzt.

Surface plasmon waves are TM polarized charge density waves that propagate at the interface of two media with real dielectric constants of opposite sign (i.e. liquid dielectric and certain metals). Surface plasmon resonance (SPR) sensors use these waves to detect refractive index changes adjacent to the metal layer. Refractive index changes arise from the binding of an analyte (e.g. a target molecule, protein, or bacterium) to the functionalized metal layer or from interfering effects such as changes in solution index. Standard, single channel SPR sensors cannot differentiate these two effects as their design allows only one mode to be coupled. This novel self-referencing technique employs two surface plasmon modes to simultaneously measure surface binding and solution refractive index. Dual surface plasmon modes are achieved by matching the refractive indices on either side of the metal film. The two modes generated - symmetric, long-range surface plasmon (LRSP) and anti-symmetric, short-range surface plasmon (SRSP) - have different field profiles and hence assist in differentiating solution refractive index changes from surface layer formation. Amorphous Teflon, with a refractive index close to water, is chosen as the buffer layer and gold is chosen as the metal layer. Magnesium fluoride, with a higher index than Teflon, is used as the buffer layer when using ethanol as the base solution. The sensor operation was optimized through simulations to yield higher sensitivity, lower reflectivity and resonances within the spectrometers range. Optimization results showed good performance over a wide range for Teflon, MgF2 and gold thicknesses which helped in the fabrication of the sensor. Demonstration of self-referencing operation was done through two different sets of experiments: (1) formation of an alkanethiol self-assembled monolayer on gold in the presence of ethanol and methanol solutions having different refractive indices and (2) streptavidin-biotin binding with solutions of different NaCl concentration and thus different refractive indices. In both these experiments, the resonance wavelengths were accurately predicted, reflectivity varied by 10-15% and sensitivity by 25% from that of the simulated values.

Surface plasmons are bound TM polarized electromagnetic waves that propagate along the interface of two materials with real dielectric constants of opposite signs. Surface plasmon resonance (SPR) sensors make use of the surface plasmon waves to detect refractive index changes occurring near this interface. For sensing purposes, this interface typically consists of a metal layer, usually gold or silver, and a liquid dielectric. SPR sensors usually measure the shift in resonance wavelength or resonance angle due to index changes adjacent to the metal layer. However this restricts the limit of detection (LOD), as the regions of low slope (intensity vs. wavelength or angle) in the SPR curve contain little information about the resonance. This work presents the technique of tunable laser interrogation of SPR sensors. A semiconductor laser with a typical lasing wavelength of 650nm was used. A 45nm gold layer sputtered on a BK7 glass substrate served as the sensor. The laser wavelength is tuned to always operate in the region of highest slope by using a custom-designed LabVIEW program. It is shown that the sensitivity is maximized and LOD is minimized by operating around the region of high slope on the SPR curve.

Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-12-15Bitstream added on 2014-06-13T20:09:47Z : No. of bitstreams: 1 domingues_el_me_bauru.pdf: 10744312 bytes, checksum: 7725f3300b6f6233a8bbfdc36c096755 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A busca por novas soluções que estimulem o crescimento da produção para atender o aumento do consumo mundial de produtos agrícolas, sem aumentar o impacto ambiental, desperta o interesse pelo desenvolvimento de novas técnicas de controle e aplicação de insumos nas plantações, visando uma melhor precisão no gerenciamento das quantidades de nutrientes necessárias nas áreas agrícolas. No Brasil a cultura da cana-de-açúcar esta se destacando no segmento agrícola e o aumento do interesse mundial no mercado de açúcar e álcool têm interessado o aumento de investimentos no setor de todo o país. A cultura de cana-de-açúcar tem grande necessidade de aplicação na aplicação de fertilizantes nitrogenados e esta necessidades estimula as pesquisas por novas soluções que permitam o monitoramento da quantidade de nitrogênio absorvida nas plantas, respeitando sua variabilidade espacial, para melhorar a eficiência no uso dos fertilizantes nitrogenados, aumentando a produtividade, reduzindo custos e os impactos ambientais. O uso dos sensores ópticos como ferramenta para medir a quantidade de nitrogênio absorvida pelas plantas através da refletância vem despertando o interesse dos pesquisadores e a utilização desta técnica em plantas de cana-de-açúcar no estagio inicial do crescimento precisa ser aprimorada. Esta dissertação pesquisou novas técnicas de processamento em tempo real utilizando sistemas inteligentes aplicados à ambientes agrícolas baseados em redes neurais artificiais, para melhorar a precisão das correlações das medições de refletâncias de um sensor óptico utilizado para estimular a quantidade de nitrogênio nas plantas de cana-de-açúcar no estágio inicial de crescimento. Os resultados obtidos pelas redes neurais artificiais apresentaram precisão melhor na correlação da... / The search for new solutions that stimulate the agricultural production growth in order to meet the worl food consumption increase, without further environmental impact, motivates the development of new crops inputs use and control techniques, aiming to improve the accuracy on amounts management of needed nutrients in agricultural areas. In Brazil, the sugar cane cultivation has been highlighted in the agricultural sector and increased global interest in the sugar and alcohol has spurred increased investment in the sector throughout the country. The cultivation of sugar cane is in great need to control the use of nitrogen fertilizers and this need stimulates researchers for new solutions allowing the monitoring of the among of absorbed nitrogen in plants, consistent with their spatial variability, to improve the efficiency of nitrogen fertilizers use, increasing productivity, reducing cost and environmental impacts. The use of optical sensors as a tool for measuring the amount of nitrogen absorbed by plants through reflectance has stimulated the interest of researchers and the use of said technique on sugar cane plant during the early stages of growing is still to be enhanced. This dissertation researched new real-time processing techniques using intelligent systems applied to agricultural based on artificial neural network in order to improve the accuracy of the correlations of the measurements of reflectance of an optical sensor in order to estimate the amount of nitrogen in sugar cane plants in the early stages of growing. The results presented by the artificial neural networks show the possibility of using embedded systems with better accuracy in the results of real time nitrogen estimation

A área de sensores é uma das mais importantes do mundo tecnológico e científico moderno. O monitoramento contínuo de processos através de variáveis de diversas naturezas está presente em áreas como indústria, agricultura, biologia, meio ambiente, e centros de pesquisa. Os sensores químicos de pH fazem parte deste conjunto por analisar um dos parâmetros mais importantes em muitas áreas. Neste trabalho, o uso de filmes finos de polianilina (PANI) eletrodepositada em sensores de pH foi estudado. Duas configurações do sensor EGFET (Extended Gate Field-Effect Transistor) foram estudadas: o sensor Single-EGFET (S-EGFET) e o sensor Instrumental Amplifier-EGFET (IA-EGFET). Os filmes foram analisados nos dois sistemas e a sensibilidade e linearidade de cada sensor, comparada. Valores iniciais de sensibilidade no sensor IA-EGFET foram reduzidas devido a protonação interna do polímero quando medidos no sensor S-EGFET. Observamos uma relação entre quantidade de material polimérico depositado e o grau de alteração dos parâmetros. Os filmes de PANI foram estudados em sensores IA-EGFET como passo inicial para aplica-los em sensores diferencias, Diferencial-IA-EGFET (D-IA-EGFET). Desenvolveu-se o sensor diferencial por esse apresentar a vantagem de ser insensível a ruído (temperatura, tempo, sistema eletrônico, concentração, etc.) sobre o sensor simples. Para este sensor temos um filme principal com alta sensibilidade ao íon de interesse, um filme de contraste com baixa sensibilidade aos íons de interesse e igual sensibilidade às fontes indesejáveis. Esses pares de filmes foram compostos por PANI, protonada e não protonada, óxido de estanho dopado com flúor e dióxido de titânio. Medidas diferenciais em função de temperatura, concentração da solução de estudo e tempo mostraram que um mecanismo de sensibilidade a íons e propriedades elétricas similares dos filmes gera um sensor diferencial bom e estável. A PANI é um material poli-eletrocrômico, isto é, seu estado de oxidação altera sua coloração. Utilizando filmes finos de PANI, que sofrem reações de protonação e desprotonação em contato com soluções ácidas e básicas, obtivemos um sensor ótico analisando os espectros de reflexão das amostras. Uma resposta aprimorada por polarização elétrica das amostras mostrou aumento da sensibilidade e diminuição da linearidade do sensor em função da variação da polarização, fazendo necessária a obtenção de um ponto ótimo de trabalho. / The study of sensors is one of the most important in the technological and scientific modern world. Continuous monitoring of processes, using variables of different types, is present in such areas as industry, agriculture, biology, environment, and research centers. Chemical pH sensors are part of this group due to its capability to analyze important parameter in many fields. In this MS we investigated the use of electrodeposited polyaniline (PANI) thin films as pH sensors. Two configurations of the EGFET (Extended Gate Field-Effect Transistor) sensor were studied: the Single-EGFET (S-EGFET) and the Instrumental Amplifier-EGFET (IA-EGFET). The films were analyzed in both systems and the sensitivity and linearity of each sensor were compared. Initial sensitivities measured in the IA-EGFET were reduced due to polymer bulk protonation after sequential measurement in the S-EGFET system. A relationship between the amount of deposited polymer and the degree of sensitivity change was observed. The films of PANI were studied in IA-EGFET sensors prior to its application in differential mode sensors, the Differential-IA-EGFET (D-IA-EGFET). The differential mode of operation was developed due to its advantage of being insensitivity to noise (temperature, electronic, time and buffer concentration variations) over the single one. The sensor has a principal film possessing high sensitivity to the target ion and a contrast film with a low sensitivity to the target ion, and both with the same sensitivity to the noise sources. These films were made of PANI, protonated and non-protonated, fluorine tin oxide, and titanium dioxide. Differential measurements as function of temperature, buffer concentration and time showed that similarity in ion-sensing mechanisms and electrical properties of the single films is necessary for the fabrication of good and stable differential sensors. PANI is a polyelectrochromic material, in other words, its oxidation state changes its color. Thin films of PANI (which can be protonated or deprotonated in contact to acid or basic solutions) were used in optical sensors by means of its reflective spectra. A bias-enhanced reflective response increased the films sensitivity and decreases its linearity, inducing the determination of an optimized working point.

Diversas aplicações são responsáveis por gerar dados ao longo do tempo de maneira contínua, ordenada e ininterrupta em um ambiente dinâmico, denominados fluxo de dados. Entre possíveis tarefas que podem ser realizadas com estes dados, classificação é uma das mais proeminentes. Devido à natureza não estacionária do ambiente responsável por gerar os dados, as características que descrevem os conceitos das classes do problema de classificação podem se alterar ao longo do tempo. Por isso, classificadores de fluxo de dados requerem constantes atualizações em seus modelos para que a taxa de acerto se mantenha estável ao longo do tempo. Na etapa de atualização a maior parte das abordagens considera que, após a predição de cada exemplo, o seu rótulo correto é imediatamente disponibilizado sem qualquer atraso de tempo (latência nula). Devido aos altos custos do processo de rotulação, os rótulos corretos nem sempre podem ser obtidos para a maior parte dos dados ou são obtidos após um considerável atraso de tempo. No caso mais desafiador, encontram-se as aplicações em que após a etapa de classificação dos exemplos, os seus respectivos rótulos corretos nunca sã disponibilizados para o algoritmo, caso chamado de latência extrema. Neste cenário, não é possível o uso de abordagens tradicionais, sendo necessário o desenvolvimento de novos métodos que sejam capazes de manter um modelo de classificação atualizado mesmo na ausência de dados rotulados. Nesta tese, além de discutir o problema de latência na tarefa de classificação de fluxo de dados não estacionários, negligenciado por boa parte da literatura, também sã propostos dois algoritmos denominados SCARGC e MClassification para o cenário de latência extrema. Ambas as propostas se baseiam no uso de técnicas de agrupamento para a adaptação à mudanças de maneira não supervisionada. Os algoritmos propostos são intuitivos, simples e apresentam resultados superiores ou equivalentes a outros algoritmos da literatura em avaliações com dados sintéticos e reais, tanto em termos de acurácia de classificação como em tempo computacional. Aléem de buscar o avanço no estado-da-arte na área de aprendizado em fluxo de dados, este trabalho também apresenta contribuições para uma importante aplicação tecnológica com impacto social e na saúde pública. Especificamente, explorou-se um sensor óptico para a identificação automática de espécies de insetos a partir da análise de informações provenientes do batimento de asas dos insetos. Para a descrição dos dados, foi verificado que os coeficientes Mel-cepstrais apresentaram os melhores resultados entre as diferentes técnicas de processamento digital de sinais avaliadas. Este sensor é um exemplo concreto de aplicação responsável por gerar um fluxo de dados em que é necessário realizar classificações em tempo real. Durante a etapa de classificação, este sensor exige a adaptação a possíveis variações em condições ambientais, responsáveis por alterar o comportamento dos insetos ao longo do tempo. Para lidar com este problema, é proposto um Sistema com Múltiplos Classificadores que realiza a seleção dinâmica do classificador mais adequado de acordo com características de cada exemplo de teste. Em avaliações com mudanças pouco significativas nas condições ambientais, foi possível obter uma acurácia de classificação próxima de 90%, no cenário com múltiplas classes e, cerca de 95% para a identificação da espécie Aedes aegypti, considerando o treinamento com uma única classe. No cenário com mudanças significativas nos dados, foi possível obter 91% de acurácia em um problema com 5 classes e 96% para a classificação de insetos vetores de importantes doenças como dengue e zika vírus. / Many applications are able to generate data continuously over t ime in an ordered and uninterrupted way in a dynamic environment , called data streams. Among possible tasks that can be performed with these data, classification is one of the most prominent . Due to non-stationarity of the environment that generates the data, the features that describe the concepts of the classes can change over time. Thus, the classifiers that deal with data streams require constants updates in their classification models to maintain a stable accuracy over time. In the update phase, most of the approaches assume that after the classification of each example from the stream, their actual class label is available without any t ime delay (zero latency). Given the high label costs, it is more reasonable to consider that this delay could vary for the most portion of the data. In the more challenging case, there are applications with extreme latency, where in after the classification of the examples, heir actual class labels are never available to the algorithm. In this scenario, it is not possible to use traditional approaches. Thus, there is the need of new methods that are able to maintain a classification model updated in the absence of labeled data. In this thesis, besides to discuss the problem of latency to obtain actual labels in data stream classification problems, neglected by most of the works, we also propose two new algorithms to deal with extreme latency, called SCARGC and MClassification. Both algorithms are based on the use of clustering approaches to adapt to changes in an unsupervised way. The proposed algorithms are intuitive, simpleand showed superior or equivalent results in terms of accuracy and computation time compared to other approaches from literature in an evaluation on synthetic and real data. In addition to the advance in the state-of-the-art in the stream learning area, this thesis also presents contributions to an important technological application with social and public health impacts. Specifically, it was studied an optical sensor to automatically identify insect species by the means of the analysis of information coming from wing beat of insects. To describe the data, we conclude that the Mel-cepst ral coefficients guide to the best results among different evaluated digital signal processing techniques. This sensor is a concrete example of an applicat ion that generates a data st ream for which it is necessary to perform real-time classification. During the classification phase, this sensor must adapt their classification model to possible variat ions in environmental conditions, responsible for changing the behavior of insects. To address this problem, we propose a System with Multiple Classifiers that dynamically selects the most adequate classifier according to characteristics of each test example. In evaluations with minor changes in the environmental conditions, we achieved a classification accuracy close to 90% in a scenario with multiple classes and 95% when identifying Aedes aegypti species considering the training phase with only the positive class. In the scenario with considerable changes in the environmental conditions, we achieved 91% of accuracy considering 5 species and 96% to classify vector mosquitoes of important diseases as dengue and zika virus.

Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2017-02-01T18:32:19Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DM Henrique Patriota Alves.pdf: 2961953 bytes, checksum: cd856b581753170ffb1c56ff445144f8 (MD5) / Made available in DSpace on 2017-02-01T18:32:19Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DM Henrique Patriota Alves.pdf: 2961953 bytes, checksum: cd856b581753170ffb1c56ff445144f8 (MD5) Previous issue date: 2016-07-22 / CNPQ / Esta dissertação apresenta uma modelagem computacional da dinâmica de rugosidade de superfície gerada durante o processo de corrosão de um elemento transdutor metálico do sensor à fibra óptica. Tal modelagem é implementada num software dedicado de simulação multifísica que faz uso do Método dos Elementos Finitos. Considerando que a rugosidade de superfície pode ser definida por geometria fractal, que pode ser complexa, é proposta nesta dissertação uma forma alternativa e simples para modelar a superfície rugosa e a sua dinâmica sob corrosão. Para a modelagem computacional, é definida uma função periódica, chamada de função rugosidade, onde seus parâmetros são ajustados com dados experimentais do processo de corrosão. Os resultados da simulação computacional são comparados com um modelo analítico, que faz uso das equações de Fresnel. Nos resultados da simulação, é observado o acoplamento do sinal óptico na superfície rugosa do filme metálico. Esse acoplamento observado é explicado pelo fenômeno de Ressonância de Plásmon de Superfície. A modelagem proposta nesta dissertação apresenta excelente ajuste aos resultados experimentais. / This dissertation presents a computer modeling of the surface roughness dynamics generated during the corrosion process of a metallic element transducer of the fiber-optic sensor. This modeling is implemented in dedicated software that uses the Finite Element Method. Since the surface roughness can be defined by the fractal geometry, which can be complex, it is proposed in this dissertation an alternative and simple way to model the surface roughness and its dynamics under corrosion. The proposed modeling uses a roughness function that is defined by a periodic function, where their parameters are obtained from experimental data of the corrosion process. The computer simulation results are compared to an analytical model, which uses the Fresnel’s equations. In the simulation results, it is observed the optical signal coupling with the rough surface of the metallic film. This observed coupling is explained by the phenomenon of Surface Plasmon Resonance. The proposed modeling and simulations presents excellent fitting to the experimental results.

Orientador: Ivo Milton Raimundo Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Quimica / Made available in DSpace on 2018-08-04T22:55:02Z (GMT). No. of bitstreams: 1 Pinheiro_SilviaCristinaLopes_M.pdf: 5873346 bytes, checksum: 978465ffcf96f296fbc2acb75ab9102a (MD5) Previous issue date: 2005 / Mestrado / Quimica Analitica / Mestre em Química

Deer Creek Reservoir in Utah has a history of high algae concentrations. Despite recent nutrient reduction efforts, seasonal algae continue to present problems. Cost effective, accurate, and comprehensive monitoring is important to understand the reservoir processes driving this problem and characterizing the algae spatial and temporal distributions are an important part of this effort. Current laboratory methods for accurately measuring algae are expensive and time consuming and are based on water samples taken in the field and transported to the laboratory. This approach only provides data for relatively few point samples because of the time and expense of sample collection and analysis. These relatively few samples do not describe the complex spatial and temporal trends in the algal data. Algae exhibit non-uniform distributions, especially in the vertical direction. In situ probes are able to measure chlorophyll-a and provide a less expensive measuring alternative than laboratory methods. These probes provide relatively quick, high resolution vertical profile measurements, which allows for more comprehensive horizontal and temporal sampling. To have confidence in the probe data, good correlations between in situ chlorophyll-a measurements and laboratory algae or chlorophyll measurements are important, but these correlations can be reservoir and time dependant as reservoir conditions change. Therefore, they must be developed for each study site. This study reports on efforts at Deer Creek Reservoir to develop these correlations and provide a general description of the dynamic reservoir algal processes. I found that chlorophyll-a is weakly correlated to most algae species in the reservoir. However, it correlated well with total phytoplankton biovolume and the dominant algal species, which for this study was the diatom. Variations in correlation strength among the several algae species was assumed to most likely be affected by environmental factors, sample methods, algae species diversity, and the accuracy of the optical chlorophyll-a sensor. The data analysis indicate that the field methods used to obtain laboratory samples may have been a significant source of error because of the difficulty of matching the location of a probe measurement to the location of a sample. Field samples were not taken at the same depths as probe measurements and field samples from two locations were either mixed before laboratory analysis or the sample was a composite over a 2-meter range. Based on my observations, I have made several recommendations to improve the accuracy of the correlation between algae and chlorophyll-a.