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The characterization and interpretation of the spectral properties of Karenia brevis through multiwavelength spectroscopy

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Title:
The characterization and interpretation of the spectral properties of Karenia brevis through multiwavelength spectroscopy
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English
Creator:
Spear, Adam H
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University of South Florida
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Subjects / Keywords:
Harmful algal bloom
Detection
Absorbance
Scattering
Optics
Dissertations, Academic -- Marine Science -- Masters -- USF   ( lcsh )
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non-fiction   ( marcgt )

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Abstract:
ABSTRACT: Optical research has shown that Karenia brevis has distinct spectral characteristics, yet most studies have focused exclusively on absorption and chemical properties, ignoring the size, shape, internal structure, and orientation, and their effect on scattering properties. The application of a new spectral interpretation model to K. brevis is shown to provide characterization of unique spectral information, not previously reported, through the use of scattering and absorption properties. The spectroscopy models are based on light scattering and absorption theories, and the approximation of the frequency-dependent optical properties of the basic constituents of living organisms. The model uses the process of mathematically separating the cell into four components, while combining their respective scattering and absorption properties, and appropriately weighted physical and chemical characteristics. The parameters for the model are based upon both reported literature values, and experimental values obtained from laboratory grown cultures and pigment standards. Measured and mathematically derived spectra are compared to determine the adequacy of the model, contribute new spectral information, and to establish the proposed spectral interpretation approach as a new detection method for K. brevis. Absorption and scattering properties of K. brevis, such as cell size/shape, internal structure, and chemical composition, are shown to predict the spectral features observed in the measured spectra. This research documents for the first time the exploitation of every spectral feature produced by the interaction of light with the cellular components and their contribution to the total spectrum of a larger (20-40 μm) photosynthetic eukaryote, K. brevis. Overall, this approach could eventually address the detection deficiencies of current optical detection applications and facilitate the understanding of K. brevis bloom ecology.
Thesis:
Thesis (M.S.)--University of South Florida, 2009.
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Includes bibliographical references.
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by Adam H. Spear.
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Title from PDF of title page.
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Document formatted into pages; contains 58 pages.

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The Characterization and Interpretati on of the Spectral Properties of Karenia brevis through Multiwavelength Spectroscopy by Adam H. Spear A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science College of Marine Science University of South Florida Co-Major Professor: Kendra Daly, Ph.D. Co-Major Professor: Luis Garcia-Rubio, Ph.D. Debra Huffman, Ph.D. Cindy Heil, Ph.D. Mya Breitbart, Ph.D. Date of Approval: March 16, 2009 Keywords: Harmful Algal Bloom; Detection; Absorbance; Scattering; Optics Copyright 2009, Adam H. Spear

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Acknowledgements First and foremost, I would like to thank my committee for all their expertise and encouragement. I would like to thank everyone at Claro Scientific LLC, for their help despite being extremely busy. Most importantl y, I thank my beautiful wife, Jessica, for her love and support.

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i Table of Contents List of Tables ii List of Figures iii List of Symbols vi Abstract viii 1. Introduction 1 1.1 Optical research involving Karenia brevis 1 1.2 New Optical modeling approach 3 1.3 Research objectives 5 2. Background 7 2.1 Spectroscopy measurements and Beer’s Law 7 2.2 Theoretical 8 2.3 Examples of modeling particles 12 3. Methods 15 3.1 Growth curve experiments and baseline spectral measurement 15 3.2 Model identification 17 3.2.1 Physical structure and chemical composition of K. brevis 18 3.2.2 Assumptions and approximations 20 3.2.3 Optical properties: parameter estimation and measurement 23 4. Results and Discussion 26 4.1 Growth experiments and baseline spectrum 26 4.2 Optical Properties 28 4.2.1 Refractive index and absorption coefficient of chromophores 31 4.3 SIM results 33 4.3.1 Macrostructure, nucl eus, and lipid globules 33 4.3.2 Chloroplasts 36 4.3.3 Calculated spectrum of K. brevis 40 4.4 Comparison of predicted and measured spectra 41 5. Conclusions and Future Work 43 5.1 Conclusions 43 5.2 Future work List of References 48 Appendices 52 Appendix A: Refractive Index and Absorption Coefficient of Chromophores 53

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ii List of Tables Table 1 Karenia brevis cell parameters 29 Table 2 Chloroplast parameters 30 Table 3 Chloroplast chromophoric pigment content 32

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iii List of Figures Figure 1. The process for obtaini ng spectroscopy measurements. 7 Figure 2. Simulation of 6 m, 7 m, and 10 m particles. 13 Figure 3. Simulation of 10 m and 15 m particles. 13 Figure 4. Simulation of 15 m and 20 m particles with similar spectra as a result of the chemical composition. 14 Figure 5. Transmission electron photomic rograph (TEM) of a longitudinal section of K. brevis from Steidinger et al ., 1978. Note the cellular complexity and the different organelles, including the chloroplast (c), nucleus (n), pyrenoid (py), lipi d (l), transverse flagellum (tf), vesicles (v), and trichocys t (arrow). Scale bar = 1 m. 19 Figure 6. The four main components to represent the absorption and scattering properties of the cell, including th e macrostructure, and the internal structures: chloroplasts, nucle us, and the lipid globules. 21 Figure 7. The three main components to represent the absorption and scattering properties of the chloroplast, including the macrostructure, and the intern al structures: pyrenoid and nucleotides. 23 Figure 8. UV-Vis spectra culture expe riments. To illustrate corresponding spectral changes over time, and to select a baseline spectral measurement, the culture experiment data was split into the lag, log, and stationary phases of growt h. Note that each phase has its own distinct spectral signature. 27 Figure 9. The baseline spectra l measurement determined from growth experiments. Note the spectral features indicated by the red circles.These are the features that represent where light is being absorbed by pigments through a scattering element. The sharp feature indicated by the arrow is an artifact of the instrument and is subsequently ignored. 28 Figure 10. Refractive index a nd absorption coefficient of chlorophylla. 33

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iv Figure 11. The spectral interpretati on of the macrostructure and the contribution of scattering and absorption to the total O.D. spectrum. Notice the absorption bands from 280-320 nm resulting from nucleotides in combinati on with scattering. Scattering dominates the visible wavelengths from 500-900 nm. The Total O.D. (blue line) is overlapped by the scattering (green line) after approximately 320 nm. 34 Figure 12. The modeled nucleus and the contribution of scattering and absorption to the total optical density spectrum. Notice the absorption bands from 280-350 nm resulting from nucleotides in combination with scattering. The scattering from the relatively large nucleus (10 m) dominates the visible wavelengths. 35 Figure 13. The modeled lipid globules and th e contribution of s cattering to the total optical density sp ectrum. The contribution from absorption is minimal or close to zero. 36 Figure 14. The contribution of scatteri ng and absorption to the total optical density spectrum of th e modeled chloroplast. Note the contribution of the combination of the absorbance bands of chla diadinoxanthin, diatoxanthin, b-carotene, chlorophyll-c2, chlorophyll-c3, 19’-hex fucoxa nthin, 19’-but fucoxanthin, fucoxanthin, and gyroxanthin-diester from 350-550 nm. Chlorophylla chlorophyll-c2, and chlo rophyll-c3 has additional absorbance bands between 600-700 nm. 37 Figure 15. The calculated total optical dens ity for the chloroplasts and the contribution from each of the components including the macrostructure, pyrenoid, and nucleoids. 38 Figure 16. Photograph of K. brevis cell from laboratory cultures. Note the large appearance of the chloroplasts due to aggregation within the cell. 39 Figure 17. Spectrum of calculated chloroplas ts and the contribution of each chloroplast component to the total calculated. Notice the addition of aggregated (larger) chloropl asts, contributing to increased scattering and shift toward larger wavelengths, in combination with individual chloroplasts. 40

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v Figure 18. The calculated to tal optical density for K. brevis and the contributing components. 41 Figure 19. Comparison of measured and cal culated total optical density for K. brevis The most notable absorp tion features, including nucleotides, chla, chlc2, chlc3, and fucoxanthin, are apparent in both spectra. 42

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vi List of Symbols A Absorbance [AU] a The major lengths of the semiaxes of the ellipsoid b The minor lengths of the semiaxes of the ellipsoid c Concentration D Diameter f (D) Frequency distribution H Covariance matrix I0 Incident intensity I Scattered intensity for unpolarized light k Imaginary part of the complex refractive index l Pathlength m Complex refractive index M Number of discrete data points taken n Real part of the complex refractive index Np Number of particles per unit volume Qext Total extinction efficiency T Transmittance Ve Volume of an ellipsoid x Number fraction for each component Extinction coefficient and error

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vii Wavelength of light Turbidity Angular frequency Regularization parameter Angular frequency in Kramers-Kronig transform

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viii The Characterization and Interpretati on of the Spectral Properties of Karenia brevis through Multiwavelength Spectroscopy Adam Henry Spear ABSTRACT Optical research has shown that Karenia brevis has distinct spectral characteristics, yet most studies have fo cused exclusively on absorption and chemical properties, ignoring the size, sh ape, internal structure, and orientation, and their effect on scattering properties. The a pplication of a new spectra l interpretation model to K. brevis is shown to provide charact erization of unique spectral information, not previously reported, through the use of scattering a nd absorption properties. The spectroscopy models are based on light scattering and absorp tion theories, and the approximation of the frequency-dependent optical properties of the basic constituents of living organisms. The model uses the process of mathematically se parating the cell into four components, while combining their respective scattering a nd absorption properties, and appropriately weighted physical and chemical characteristi cs. The parameters for the model are based upon both reported literature valu es, and experimental values obtained from laboratory grown cultures and pigment standards. Measur ed and mathematically derived spectra are compared to determine the adequacy of the model, contribute new spectral information, and to establish the proposed spectral interpretation appro ach as a new detection method for K. brevis Absorption and scattering properties of K. brevis such as cell size/shape,

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ix internal structure, and chemical composition, are shown to predict the spectral features observed in the measured spectra. This research documents for the first time the exploitation of every spectral f eature produced by the interaction of light with the cellular components and their contribu tion to the total spectrum of a larger (20-40 m) photosynthetic eukaryote, K. brevis Overall, this approach could eventually address the detection deficiencies of current optical detection applications and facilitate the understanding of K. brevis bloom ecology.

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1 1. Introduction 1.1 Optical research involving Karenia brevis The use of spectral information from Karenia brevis, a species of toxic dinoflagellate that blooms frequently in the Gulf of Mexico, for optical detection has been limited by the inability to interpret bulk optical signatures of a given water mass and subsequently discriminate a specific phytoplankton species among a mixed community (Millie et al., 1997; Schofield et al., 1999; Kirkpatrick et al., 2000). Remote sensing technology can monitor harmful algal bloom s (HABs) over broader spatial areas; however, the inability to detect most HAB s without ground truthing, inability for lower density detection, lost data due to weathe r/clouds, and below surface measurements are considered major deficiencies in HAB de tection through satellite remote sensing. In situ technology, such as moored optical instru ments and optically equipped autonomous vehicles, addresses some of these deficiencies and provides ground truthing for remote sensing data through rapid detection of a broader range of concentrations of K. brevis at or below the surface (Robbins et al., 2006). However, current optical detection of Karenia species has been limited to bloom c oncentrations and the use of a unique photpigment, gyroxanthin-diester (observed in a small number of toxic dinoflagellates) (Millie et al., 1997). The lack of successful detection of K. brevis at low concentrations limits the impact and potential mitigation procedures, highlighting the need to understand non-bloom conditions. Optical detection me thods are further complicated by the

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2 coexistence of five other known Karenia species in the Gulf of Mexico (Heil et al., in press). Fully understanding HAB dynamics require detection of a single HAB species surrounded by mixed communities throughout the water column at a wide range of concentrations (bloom and non-bloom concentr ations) (Steidinger et al., 1998; Schofield et al., 1999). Current optical detect ion deficiencies of K. brevis are mostly due to a lack of full characterization of its spectral propertie s. Optical research has shown that K. brevis has distinct spectral characteristics, yet most st udies have focused on absorption and chemical properties, while ignoring size, shape, internal structure, and orientat ion, and their effect on scattering properties (Millie et al., 1997; Kirkpatrick et al., 2000). Neglecting scattering properties is mainly a result of the empirical appr oach that has been taken in the spectral data analysis. Previous in situ optical research has focused on the collection of cells through filtration and subsequent extr action of pigments for absorbance readings. For example, Millie et al. (1997) and Kirkpa trick et al. (2000) de veloped a method that correlates the fraction of chlo rophyll biomass contributed by K. brevis among a phytoplankton community and a fourth derivative absorption-based similarity index. This absorption-only analysis of the visible spectrum allowed for the quantification of gyroxanthin-diester, a rare acc essory pigment found in only a few dinoflagellate species. Gyroxanthin-diester was determined to be a consistent predictor of the presence of Karenia species when correlated with chlor ophyll biomass (Millie et al., 1997; Kirkpatrick et al., 2000). A study by Mahoney (2003), focused on th e spectral characterization of K. brevis and did, in fact, include scattering. Mahoney (2003) implemented the Mie solution, which

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3 is used to explain the eff ects of larger particles on forward scattering. The Mahoney (2003) approach involved average bulk cell m easurement values from laboratory cultures of K. brevis such as absorption, particle size an d density, and refractive index. These values were then applied to Mie solutions to model the spectra. Because their spectra came from bulk cell measurements, characteri zation and interpretati on of spectra was not possible due to the inability to adjust to spectral variabil ity. For example, a change in physical conditions, such as irradiance, coul d potentially increase or decrease spectral absorption due to changing amounts of chlorophylla within the cell. Since the Mahoney method used bulk cell properties from a single laboratory culture, a new experiment with differing physical conditions w ould be required to provide new measurements for the model. In fact, new experiments and averages w ould be required to represent the effect of a full range of environmental variability on cells, which would be uneconomical and likely ineffective at characterizing K. brevis spectra. 1.2 New optical modeling approach Alupoaei and Garcia-Rubio (2005) addre ssed the difficulty in characterizing absorption and scattering properties through de velopment of a new sp ectral interpretation model (SIM) for microorganisms specifically Escherichia coli They interpreted the spectra of microorganisms at a wider range of wavelengths (UV-Vis, 220-900 nm) and a higher frequency (1 nm) than previously employed, which allowed for additional information to be extracted from the spect ra. In contrast, Mahoney (2003) examined spectral properties across the visible sp ectrum (412-730 nm) only, with a relatively low spectral resolution of 3.3 nm. Alupoaei and Garcia-Rubio (2005) were able to

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4 characterize the spectra of microorganisms through mathematically separating the cell into multiple components combining their respective scattering and absorption properties and appropriately weighting physical and chemical characteristics. Briefly, the SIM is based on light scattering and absorpti on theories, coupled with an approximation of the frequency-dependant op tical properties of th e basic constituents of living organisms. The SIM applies mathema tical interpretations of the Mie theory and the spectral representation of the cell’s chemical and physical components. The total optical density is assumed to be additive in terms of the volume fractions of the internal structures and macrostructure (Alupoaei & Garcia-Rubio, 2005). Within each of these structures the optical properties are added together and weighted by the mass concentration (see methods section below). The absorption and scattering properties of microorganisms, such as size/shape, intern al structure, and ch emical composition, was reported to have a predictable infl uence over their observed spectra. The power of the SIM is highlighted by th e potential for many i ndividual spectral features to be accounted for and underst ood. This is demonstrated through spectral information given by the SIM, such as which structural component w ithin the cell affects the spectrum, as well as each component’s chem ical and physical characteristics. In fact, given the earlier example of a change in irra diance, the approach of Alupoaei and GarciaRubio (2005) could interpret the change in absorption due to th e ability to adjust the size, volume fraction and chemical composition of the cell component affected. In addition to bacteria, the approach of Alupoaei and Garcia-Rubio (2005) has demonstrated accuracy in characterizing spectr al properties of larger (2-10 m), more complex cells, such as human blood platelet s, and protozoa (M attley et al., 2000;

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5 Alupoaei, 2001; Callahan et al., 2003; Alupo aei & Garcia-Rubio, 2004; Alupoaei et al., 2004; Alupoaei & Garcia-Rubio, 2005). These studi es demonstrated accurate spectral characterization through mathematically dividing the complex cell into multiple components and combining their respectiv e scattering and absorption properties The results of Alupoaei and Garcia-R ubio (2005), combined with previous research efforts using the same spectral inte rpretation approach, indi cate the potential for multiwavelength spectroscopy to be used as a rapid analytical techni que with application in detection, identification, and quantification of microorganisms (Mattley et al., 2000; Alupoaei, 2001; Callahan et al., 2003; Alupo aei & Garcia-Rubio, 2004; Alupoaei et al., 2004). As of yet, the SIM has not been applie d to larger (20-40 m), more complex cells, such as photosynthetic dinoflagellates. Theref ore, successful reproduction of the spectral features of K. brevis through the application of the SI M would represent novel research. 1.5 Research objectives As discussed above, more defined spectral information is needed for early bloom detection and prediction of K. brevis The application of the SIM to K. brevis could provide characterization of uni que spectral information not yet reported for this HAB species, as well as a better understanding of its optical properties. The objective of this research is to evolve the SIM, propo sed by Alupoaei and Garcia-Rubio (2005), for application to K. brevis to determine whether it can be us ed as an interpretation tool that will enable the characterization of K. brevis spectra. The initial evolution of the SIM should provide us with a better unders tanding of the spectral properties of K. brevis,

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6 which is an important and necessary first st ep to providing more accurate and sensitive detection. Here we report th e application of the SIM to K. brevis

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7 2. Background 2.1 Spectroscopy measurements and Beer’s Law The SIM relies on fundamental light s cattering and absorption theories. The measurement of transmitted light involves the projection of a broadband light source through suspended particles, followed by th e wavelengths of transmitted light being measured simultaneously using a spectropho tometer (Figure 1). The spectrophotometer measures the amount of light transmitted and reports the percentage of light transmitted relative to the incident light. Provided absorption is the only interaction with the medium, the transmitted light that is measured by the spectrophotometer should be less than the amount of incident light. There are also cases in which partic les (in this case cells) emit Figure 1. The process for obtaining spectroscopy measurements.

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8 light, such as chemoluminescence or fluores cence.Thus, light emission can potentially result in more transmitted light than exp ected. Beer’s law relates the absorbance and concentration of a particle suspensi on and is generally written as: cl (1) where A is the measured absorbance, 0) is a wavelength-dependent extinction coefficient, c is the concentration, and l is the pathlength. The extinction coefficient can be described as: 4 (2) where is the imaginary part of the comple x refractive index. Absorbance (A) or transmittance (T) can also be described as the lo garithm of the ratio of the intensity of the light striking the suspension ( I0) to that passing through the specimen ( I ): T I I log ) log(0 (3) 2.2 Theoretical The general solution of li ght scattering by homogeneous and isotropic spherical particles contained in a nonabsorbing medium is describe d by Mie theory. In cases where particles under cons ideration are not spherical, such as ellipsoids, Mie theory has been applied to derive the solutions for characterization (Kerker, 1969; Bohren and Huffman, 1983). Therefore, interpretation of the Mie solution provides important quantitative information when applie d to particle characterization.

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9 A spectrophotometer is used to measure th e amount of light transmitted relative to the incident light. This can al so be referred to as a measurement of turbidity (optical density). Turbidity measurements are based on the fact that particle suspensions scatter light. Therefore, turbidity can be defined as an attenuation coefficient or the measure of light that is scattered and absorbed rather than transmitted. The equation that relates the turbidity ((0)) measured at a given wavelength 0 and the normalized particle size distribution for spherical particles (f(D)) is given by (van der Hulst, 1957; Kerker, 1969): where D is the effective particle diameter, Qext(m(0),D) corresponds to the Mie extinction coefficient, l is the pathlength, and Np is the number of particles per unit volume. The Mie extinction coefficient is a function of the optical properties of the particles and the suspending medium through the complex refractive index (m(0)) given in Equation 5: where n(0) and n0(0) correspond to the refractive i ndex of the particles and the suspending medium, respectively. The absorption coefficient of the suspended particles is represented by (0) The real and imaginary parts of the complex refractive index are functions of the chemical composition and can be calculated from the mass fraction of the 0 2 0 0, 4 dD D f D D m Q Nplext (4) ) ( n ) ( i ) ( n ) ( m0 0 0 0 0 (5)

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10 chromophores within each population. In other words, the optical properties are additive at the molecular level (Alupoaei & Garcia -Rubio, 2005). Equation 4 can be written in matrix form by discretizing the integral w ith an appropriate qu adrature approximation (Elicabe and Garcia-Rubio, 1989) ; (Elicabe and Garcia-Rubi o, 1990), given by Equation 6 as: f A (6) where represents a composite of experimental errors, which are errors due to the SIM approximations and errors introduced by th e discretization procedure (Elicabe and Garcia-Rubio, 1990). Equation 7 gives the regularized solution to Equation 6 as: where H is a covariance matrix that esse ntially filters the experimental and the approximation errors () and is the regularization parameter estimated using the Generalized Cross-Validation technique (G CV) (Golub et al., 1979). The Generalized Cross-Validation technique requires the mini mization of Equation 6 with respect to (Golub et al., 1979); (Eli cabe and Garcia-Rubio, 1990): where Nob represents the number of disc rete turbidity measurements. T 1 T ^A ) H A A ( ) ( f (7) 2 1 2 1] ) ( [( ) ( ( ) (T T TA H A A A I Trace H A A A I Nob V (8)

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11 Equations 4-8 can be used in a variety of ways depending on the information required and the available data. For example, if the optical properties are known as functions of wavelength, Equations 7-8 can be used to estimate the particle size distribution. If the particle size distribution is known, from microscopy, Coulter Counter, or other techniques, then E quations 4, 5 and 7 can be used to estimate the optical properties, and thus the chemical composition of the particles. Overall, these optical property values are equivalent to a calibration and can be used as fingerprints to classify and identify distinct particle populations (Mattley et al., 2000). Scattering of light is due to the difference between the real refractive index of the medium in which the particles are suspended, no, and the refractive index of the particle, n The absorption coefficient, k, accounts for attenuation of light. To interpret absorbed and scattered light, accurate estimates of the optical properties are needed. The complex refractive index, which contains the optical prope rties of the particle, will be affected if the particle’s composition (chemical or stru ctural) are changed. Th e real and imaginary parts of the complex refractive index are not independent; they are connected at every frequency through the mathematical relati ons called Kramers–Kr onig or dispersion relations (Bohren and Huffman, 1983), 0 2 2 1 12 1 d k P n (9) 0 2 2 1 12 d n P k (10)

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12 where k1 is the imaginary part of the refractive index, n1 is the real part of the refractive index, is the frequency of the measurement, is the variable representing frequency in the integral, and P is the principal value of the in tegral (Bohren and Huffman, 1983). 2.3 Examples of modeling particles Figure 2 represents a simulation of diffe rent sized particles of structural nonchromophoric protein and water. These examples illustrate the effects of size and chemical composition on absorption and s cattering. Spectra were normalized with the average optical density between 240-900nm (see methods). Note the spectra of the 6 m, 7 m, and 10 m particles. As the partic les become larger, the scattering increases generally in the visible wavelength regions (approximately 400-900 nm). The 10 m and 15 m particles, shown in Figure 3, demons trate further scattering in the visible wavelengths, as well as an increase in nonlinea r wave action in the ul traviolet wavelength regions (approximately 240-400 nm). Si nce scattering and absorption are not independent, the size of the par ticle is not the only importa nt factor. The composition of water and structural protein are also importa nt. For example, as shown in Figure 4, 15 m and 25 m particles look remarkably sim ilar despite having different sizes. This similarity in spectra from particles of diffe rent sizes is a result of decreased scattering from the chemical composition (less structural protein, more water) in the larger particle.

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13Figure 2. Simulation of 6 m, 7 m, and 10 m particles. Figure 3. Simulation of 10 m and 15 m particles. 240 300 400 500 600 700 800 900 Wavelength (nm)Normalized O.D. (AU) 6 m 7 m 10 m 240 300 400 500 600 700 800 900 Wavelength (nm)Normalized O.D. (AU) 10 m 15 m

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14Figure 4. Simulation of 15 m and 20 m particles with similar spectra as a result of the chemical composition. 240 300 400 500 600 700 800 900 Wavelength (nm)Normalized O.D. (AU) 15 m 85% water 25 m 89% water + 4% Nucleotides

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15 3. Methods 3.1 Growth curve experiments and baseline spectral measurement The differences in spectra as a function of time have the potential to be quantified through the SIM. Changes in the cell populati on over each growth phase can be further separated into changes in number of cells cell size, shape, chemical composition, and internal structure. The growth analysis e xperiments can be used to validate the SIM through 1) successful spectral prediction of different growth phases, 2) successful spectral prediction of different concentrati ons, and 3) successful spectral prediction of different cell size. Eventually, the growth e xperiments can provide the assessment of the SIM through the comparison of modeled spectr a to data outside a set of experimental design parameters. The growth analysis reported here is qualitative only. K. brevis isolate cultures, Apalachicola (C6), were obtained from the Florida Fish and Wildlife Research Institute (FWRI) to obser ve changes in spectral features over time caused by cell growth and mortal ity. Cultures with an initial concentration 30-60 cells ml-1 were held under fluorescent lamps at an irradiance of ~50 mol quanta m-2 s-1 within 14:10 L:D cycles at 24oC for two months. The initial cu lture consisted of 1.5 liters of sterilized filtered seawater and modified general purpose (GSe) medium (Blackburn et al., 2001). Culture cells were harvested and the UV-Vis spectra were recorded on an Agilent 8453 diode array spectrophotometer as described below. The same filtered seawater was used as a spectral backgr ound subtraction. Spectral measurements of K.

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16 brevis cells were recorded approximate ly every day, 1-2 times per day. After spectral measurement, cell density and size was meas ured using a Beckman Z2 Coulter Counter, as well as an ocular micrometer and a Neubauer hemacytometer for visual ground truthing. UV-Vis spectra from pigment standards we re recorded on an Agilent 8453 diode array spectrophotometer with an acceptance angle less than 2o. Spectral measurements were conducted at approximately 24oC using a 1 cm path length, 1 nm resolution, in a 3.5 ml volume quartz cuvette. Prior to meas uring the spectrum of each sample, the spectrophotometer was zeroed to account for background light. The original suspending medium was used as a background spectrum a nd subtracted from the sample spectrum. Transmission measurements were normalized with the average optical density between 240-900nm. Normalization allows for the elimination of the effect of the concentration and number of pa rticles, therefore the resul ting spectral features can be limited to changes in the si ze distribution and chemical composition (Alopuei, 2001). Normalized turbidity () is defined as: (11) where 0 () represents the turbidity or optical density as a function of wavelength, M represents the number of disc rete data points taken, and 0i represents the turbidity measured at the ith wavelength. M i iM11 ) ( ) (

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17 To begin modeling, a baseline spectrum of K. brevis must be selected as a spectral fingerprint. A baseline spectral measuremen t was taken once the late log to early stationary phase was determined. It is assu med that the late log or beginning of the stationary growth phase is the point at which growth, cell division and cell nutrient uptake is minimal over the relatively shor t period of time it takes to obtain the measurements. 3.2 Model identification Model identification involves the determ ination of the structural components and the parameters for the optical properties of each component. Parameters such as size, shape, and chemical composition, are measur ed or obtained from literature. Using Equations 4-8, the spectra are calculated for each component and subsequently summed by their respective volume frac tions to obtain a total optic al density spectrum for a population of K. brevis. A series of iterations allow for corrections to be made to parameters, such that the SIM is able to predict a baseline spectrum of K. brevis. Comparison of the predicted to measured spectrum has major implications, in that it is a major necessary first step to further research. It allows us to 1) determine the degree of model adequacy and the accuracy of the estimation of the optical properties and 2) determine the major contributors to the total optical density through spectral deconvolution. Values such as cell concentration and size must be similar between measured cell populations and estimated valu es. Further validation is provided if the parameter values used in the calculated spec tra are within a statistically significant range of literature-found parameters.

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18 3.2.1 Physical structure and chemical composition of K. brevis As indicated in the previous chapters, in theory, the transmission spectrum of particle suspensions contains in formation that can be used to estimate particle size, shape and chemical composition. Therefore, the first step is to understand the physical and chemical properties of the organism of interest, K. brevis. K. brevis a eukaryotic dinoflagellate, typica lly ranges from 20-40 m in length (Steidenger et al. 1978). It is a unicellular organi sm that has several organelles and/or internal structures (Figure 5) including: a nucleus, vesicl es, lipid globules, transverse flagellum, trichocyst, and chloroplasts; within the chloroplast there is occasionally a dense pyrenoid structure. All of these struct ures can be considered main scattering and absorption elements of the cell. Water accounts for approximately 70 percen t of the total mass of all living cells (Poindexter, 1971, Lim, 1989). The remaining 30 pe rcent of the total mass is represented by dry components, such as proteins, nucleic acids, polysaccharides, lipids, etc. These dry components can be split into two categories, chromophoric (absorbi ng or of color) and non-chromophoric. Nucleotide chromophores are present within the nucleus and cytoplasm of the cell (Rizzo et al ., 1982). The main chromophores of K. brevis chloroplasts include one primary photopigment, chlorophylla (chla ), and several accessory pigments, such as fucoxanthin, diadinoxanthin, b-carotene, chlorophyll-c2, chlorophyll-c3, 19’-hex fucoxanthin, 19’-but fucoxanthin, and di atoxanthin, gyroxanthin diester (Bjornland et al., 2003, rnlfsdttir et al., 2003). Previous research suggests that K. brevis chloroplasts have nucle otides concentrated within several small (~1 m) nucleoids (Kite & Dodge, 1985). In addition, to adjust to changing light intensities,

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19 phytoplankton will modify pigment concentrations relatively quickly (hours to days) for photoprotection or photosynthesis (Falkowski & Raven, 1997; Evens et al., 2001; Staehr et al., 2002, Staehr & Cullen, 2003). Increases in light intensity may result in a decrease in cellular chlorophylla and an increase in photoprotective pigment concentrations. Decreases in light intensity may lead to an increase in chlorophyll and a decrease in photoprotective pigments. In addition, phytopl ankton may increase th e size of the cell and the chloroplast, increase the thylakoi d density within the chloroplast, and subsequently increase the amount of chla and accessory pigments in response to decreased light intensity (Falkowski & Ra ven, 1997; Staehr et al., 2002, Staehr & Cullen, 2003). Figure 5. Transmission electron photomicrograph (TEM) of a longitudinal section of K. brevis from Steidinger et al., 1978. Note the cellular complexity and the different organelles, including the chloroplast (c), nucleus (n), pyrenoid (py), lipid (l), transverse fl agellum (tf), vesicles (v), and trichocyst (arrow). Scale bar = 1 m.

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20 Therefore, due to the complexity of these chloroplast internal interactions, it can be approximated that all pigments described above are concentrated w ithin the chloroplast only. The complexity and highly chromophoric pr operties of the chloroplast also suggests that it is the primary contributor to absorption and scattering properties of K. brevis spectra. 3.2.2 Assumptions and approximations Under the assumption of volume additivity, the SIM allows for the cell to be divided into multiple components. The comp lex cell structure shown in Figure 5 was approximated by dividing the cell into four main components, each of which will be characterized by scattering and absorption prop erties. These four components include the main body of the cell, or (1) macrostructure, a nd the internal structures, such as the (2) nucleus, (3) chloroplasts, and (4) lipid globul es (Figure 6). These four components were chosen due to the fact that they are the largest structures and have the most unique chemical composition. Therefore, these com ponents will likely contribute the most to absorption and scattering. Add itional cell components can be added if they are shown to significantly contribute to ab sorption and scattering. Assuming that the spectrum of K. brevis can be approximated as four structural components, along with the assumption of vol ume additivity then Equation 4 can be expanded to:

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21 Macrostructure Chloroplasts Nucleus Lipid Globule (12) where mi (i=1-4) is the complex refrac tive index for each component and xi (i=1-4) is the number fraction for each component. Figure 6. The four main components to represent the absorption and scattering properties of the cell, including the macrostructure, and the internal structures: chloroplasts, lipid globules, and nucleus. ) (0 3 2 0 3 3 3 dD D f D D m Q xext ) (0 2 2 0 2 2 2 dD D f D D m Q xext ) ( 40 1 2 0 1 1 1 0 dD D f D D m Q x Nplext ) (0 4 2 0 4 4 4 dD D f D D m Q xextNucleus Macrostructure (including cytoplasm) + = +Chloroplasts Lipid globules+Nucleus Macrostructure (including cytoplasm) + = +Chloroplasts Lipid globules+Macrostructure (including cytoplasm) + = +Chloroplasts Lipid globules+

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22 Also, under the assumption of volume additi vity, the SIM allows for each internal structure, if needed, to be divided into mu ltiple components. The complexity and highly chromophoric properties of the chloroplast indi cates that a more specific sub-model is necessary. The complex structure of the chloroplast, shown in Figure 7, can be approximated by dividing it into at least three main components, each of which may be characterized by scattering and absorption pr operties. These three components include the main body of the chloroplast, or macrostr ucture, which include pigments, and internal structures, such as nucleotides and a pyre noid (Figure 7). With the application of assumption that the spectrum of K. brevis chloroplasts can be approximated as three structural components, along with the assumption of volume additivity, Equation 4 can be expanded to: Macrostructure Pyrenoid Nucleotides (13) where mi (i=1-3) is the complex refrac tive index for each component and xi (i=1-3) is the number fraction for each component. ) (0 3 2 0 3 3 3 dD D f D D m Q xext ) (0 2 2 0 2 2 2 dD D f D D m Q xext ) ( 40 1 2 0 1 1 1 0 dD D f D D m Q x Nplext

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23Figure 7. The three main components to represen t the absorption and scattering properties of the chloroplast, including the macrostructure, and the internal structures : pyrenoid and nucleotides Lastly, K. brevis is approximated as an oblate ellipsoid, based on the characteristic dimensions reported in the lite rature (Steidinger et al, 1978), and cells are assumed to be homogenously distri buted throughout the cell suspension. K. brevis is represented as an obl ate ellipsoid by: 6 3 43 2D ab Ve (14) where Ve is the volume of the ellipsoid, a and b are the major and minor lengths, respectively, of the semi-axes of the ellipsoid, and D is the approximated diameter of the cell. As described earlier, the diameter of K. brevis typically ranges from 20-40 m, which is a relatively large cel l size for this application. 3.2.3 Optical properties: paramete r estimation and measurement The parameter estimates were obtained fr om previous literature values. These include the average size of the macrostructu re, the average size and volume fraction of the internal structures (chloroplasts, nucleus ), and the chemical composition in terms of the mass concentrations of nucleotides, photo/accessory pigments, non-chromophoric DNA, RNA Pyrenoid Macrostructure+ = + DNA, RNA Pyrenoid Macrostructure+ = +

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24 proteins, and water content. When informati on is unavailable from previous literature, results from lab experiments and/or educated assumptions were used to estimate model parameters. As described earlier, K. brevis chloroplasts have one primary photopigment, chlorophylla (chla ), and several accessory pigments A serial dilution of all major characteristic photopigments (chla, c2, c3, fucoxanthin, gyroxanthin diester, diadinoxanthin, b-carotene, 19’-hex fucoxanthi n, 19’-but fucoxanthi n, and diatoxanthin) were analyzed to better characterize and interpret K. brevis cell optical characteristics. Isolated chla was purchased from Sigma. The remaining accessory pigments were purchased from DHI Water and Environment. The absorption spectra of all pigments were converted to extinction coefficients ac cording to the Beer-Lambert law using the known concentration and pathle ngth. Kramers-Kroni g relations (Equations 9 & 10) were used to calculate the refractive index fr om the measured extinction spectrum. UV-Vis spectra from pigment standards we re recorded on an Agilent 8453 diode array spectrophotometer, having an acceptance angle smaller than 2o. Spectral measurements were conducted at approximately 24oC using a 1 cm path length, 1 nm resolution, in a 3.5 ml volume quartz cuvette. Prior to measuring the spectrum of each sample, the spectrophotometer was zeroed to account for background light. The original suspending medium was used as a background spectrum and subtracted from the sample spectrum. Values reported by Alupoaei and Garcia -Rubio (2005) were used to approximate the optical properties of the nucleotides a nd non-chromophoric groups (lipids, structural protein, etc.). Finally, the comb ination of parameter values and optical properties files

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25 were then applied to the calculation of each structural component’s spectrum, followed by the calculation of the tota l optical density spectrum.

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26 4. Results and Discussion 4.1 Growth experiments and baseline spectrum To illustrate corresponding spectral change s over time, and to select a baseline spectral measurement from which to model, th e data from the culture experiments were split into the lag, log, and stationary phases of growth (Figure 8). Within the log phase, there is a noticeable gradua l progression of an increase in scattering from 400-900 nm over time. This increase in sc attering is likely due to the corresponding increase in cell size, as larger particles tend to forward scatter at longer wavelengths. Another explanation for the gradual increase in optical density from 400-900 nm could be a corresponding increase in non-chromophoric prot ein or lipids over time. On average, the cell wall could be thickening or the amount of lipids could be increasing etc., as the cell gets older, resulting in an increase in s cattering. An increase in organelle size or number can also result in a decrease in the amount of light that passes through the particle suspension. An increasing averag e cell size can also be an ex planation for the decrease in optical density from 240-340 nm, a result of forw ard scattering. Overall, note that each phase has its own distinct spectral signatur e. For example, note the dramatic difference between death and the lag, log, and stationary phase across the entire spectrum. This difference among phases can be assumed to be at tributed to the fact that in the death phase the number of dead or dying cells will increase. Specifically, the dead or decaying cells break up and reintroduce proteins and lipids back into the water column and are

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27Figure 8. UV-Vis spectra culture experiments. To illustrate corresponding spectral changes over time, and to select a baseline spectral measurement, the culture experiment data was split into the lag, log, and stationary phases of growth. Note that each phase has its own distinct spectral signature. subsequently measured along with live cells. A large absorption peak around 240-300 nm, followed by sharp decline around 350-400 nm, and a gradual decrease in scattering from 400-900 nm are typical spectral features of small particles c onsisting of mostly protein and water; an exampl e of this would be a typica l bacteria spectrum (Alupoaei, 2001; Alupoaei & Garcia-Rubio, 2004; Alupoaei & Garcia-Rubio, 2005). Finally, to begin modeling, a baseline spectrum of K. brevis was selected as a spectral fingerprint. The baseline spectral measurement, shown in Figure 9, was taken once the late log to early stati onary phase was determined. It is assumed that the late log or beginning of the stationary growth phase is the point at which growth, cell division and cell nutrient uptake is minimal over the relativ ely short period of time it takes to obtain the measurements. Note the spectral features indicated by the red circles. These are the 240 300 360 420 480 540 600 660 720 780 840 900 0 0.5 1 1.5 2 2.5 3 3.5 4 Wavelength (nm)Normalized O.D. (AU) Lag & Early Log Late Log & Early Sta. Lag & Early Log Late Log Lag Log Sta Death

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28Figure 9. The baseline spectral measurement determ ined from growth experiments. Note the spectral features indicated by the red circles. These are the features that represent where light is being absorbed by pigments through a scattering elemen t. The sharp feature indicated by the arrow is an artifact of the instrument and is subsequently ignored. main spectral features that are to be repr oduced using the SIM. The features represent where light is being absorbed by pigments through a scattering element, therefore the optical density increases at th ese wavelengths. The sharp feature indicated by the arrow is an artifact of the instrument and is subsequently ignored. 4.2 Optical properties Literature values were initially used for model parameters. Estimated model parameter values were then determined thr ough multiple iterations of matching calculated and measured spectra of K. brevis. Once the calculated and measured spectra matched, estimated parameter values used to model th e spectra were recorded (see section 5.2 for a 240 300 400 500 600 700 800 900 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 Wavelength (nm)Normalized O.D.(AU)

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29 more quantitative procedure for determination of parameter values). The exact percent water and structural protein of the cell a nd cell components are ty pically not given in literature reports. These values were estim ated based on the approximation that water accounts for 70 percent of the to tal mass of all living cells and the remaining 30 percent of the total mass is represented by dry co mponents (Lim, 1989; Poindexter, 1971). This 70:30 water to dry component ratio was used as initial model parameter values. The final percent water and protein was then determin ed through multiple iter ations of matching calculated and measured spectra of K. brevis. Table 1 shows the estimated and literatureobtained values used to model the cell of K. brevis. It is worth noting that the estimated values are very similar to literature-obtaine d values, which validates the model approach. Table 1: Karenia brevis cell parameters, including: size (m) and chromophoric concentrations (pg/component), % mass of water, and % mass of non-chromophoric protein aApproximation (see text); b Steidinger et al., 1978; cRizzo et al., 1982; d Evans et al., 2001; e Calculated from the fraction of DNA/cell and the amount of DNA/nucleus after Rizzo et al., 1982. Table 2 shows the estimated and literatur e-obtained values used to model the chloroplast of K. brevis. Note the close agreement between estimated values and the Diameter Nucleotides Water Protein Macrostructure 20-40b 113 e -9% e Nucleus 6-11 bc 157 c -16% c Literature Lipid Globule 4 b ---Macrostructure 20 113 87.4% a 9% Nucleus 10 141 61% a 12% Estimated Lipid Globule 4 -10% a 90% a

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30 values reported from the literature. The ch loroplast is split in to three components, including the macrostructure, nucleoids, and the pyrenoid. Interestingl y, literature reports suggest that K. brevis has nucleotides concentrated within several small (~1 m) nucleoids (Kite & Dodge, 1985). Since scatteri ng increases as particle size increases, nucleotides concentrated within nucleoids wi ll have a considerab le influence to the spectral properties. Pyrenoids, which have been documented to exist within K. brevis chloroplasts, are typically very dense bodies consisting of mostly protein (ribulose-1,5bisphosphate carboxylase/oxygenase (Rubisco)) (B orkhsenious et al., 1998). As a result of their density, pyrenoids ar e considered another potential important contributor to scattering (Steidinger et al., 1978). Table 2: Chloroplast Parameters, incl uding: size (m) and chromophoric concentrations (pg/component), % mass of water, and % mass of nonchromophoric Protein Diameter Nucleotide Water Protein Macrostructure 4-7 bcf ---Nucleoids 0.3-1.5 cf 0.012e --Literature Pyrenoid 2-3 bcdf --34% d Macrostructure 6 -75%a 14%a Nucleoids 0.6 0.2 45%a 35%a Estimated Pyrenoid 3 -47%a 53% a Approximation (see text); b Steidinger et al., 1978; cKite and Dodge, 1985; d Holdsworth, 1971; e Cattolico, 1978 & Ratio of DNA:RNA= 1:1; fapproximated from TEM/photos and given scale bar Table 3 shows the estimated and literatur e-obtained values used to model the chloroplast chromophoric pigment content All of the pigments ar e contained within the macrostructure or main body of the chloropl ast. Note that for most pigments, the

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31 estimated values are within range of the valu es reported from the l iterature. However, the estimated values are on the lower end of the ra nge of values reported from the literature. As discussed later, this c ould be an artifact of a mi ssing scattering component not accounted for within the model. If a scatte ring component were not accounted for, the higher concentration of pigments would ha ve more of an exaggerated effect on absorption properties. 4.2.1 Refractive index and absorpti on coefficient of chromophores The refractive index for chla was estimated from the literature to be 1.52 at 589 nm wavelength (Aas, 1996). The refractive i ndex for the remaining accessory pigments was estimated using the additive mo lar properties method reported in Properties of Polymers (Van Krevelen, 1990). The remaining accessory pigments were found to be near the chla refractive index value which, for consis tency, resulted in the use of 1.52 for all pigments. Recall the Kramers-Kronig relations (E quations 9 & 10) which allow us to calculate the refractive index from the meas ured absorption coefficient. As shown in Figure 10, chlorophylla was measured to determine the absorption coefficient. Through the absorption coefficient we are able to calculate the refractive index over the whole wavelength range from the refractive index at a single wavelength, which is typically determined from the literature. The refractive indices and absorption coefficients of the remaining chromophoric groups used in the SIM are shown in Appendix A.

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32 Table 3: Chloroplast chromophoric pigment content (pg/component) Chla Fuco. Diad. Gyro. Diat. Macrostructure 0.5212.85abcd 0.211.3abcd 0.52.7bcd 0.021.14bd -Nucleoids -----Literature Pyrenoid -----Macrostructure 1 0.24 0.02 0.02 0.02 Nucleoids -----Estimated Pyrenoid -----Table 3: (Continued) B-car. c2 c3 Hfuc. Bfuc. Macrostructure 0.0010.05bd 0.010.96 bd 0.010.56 bd --Nucleoids -----Literature Pyrenoid -----Macrostructure 0.02 0.07 0.02 0.02 0.02 Nucleoids -----Estimated Pyrenoid -----a Evans et al., 2001; b Millie et al., 1995; cMillie et al., 1997; d Assumed 7 chloroplasts; fucoxanthin (Fuco), diadinoxanthin (Diad), b-carotene (B-car), chlorophyll-c2 (c2), chlorophyll-c3 (c3), 19’-hex fucoxanthin (Hfuc), 19’-but fucoxanthin (Bfuc), and diatoxanthin (Diat), gyroxanthin diester (Gyro)

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33Figure 10. Refractive index and absorption coefficient of chlorophylla. 4.3 SIM results 4.3.1 Macrostructure, nucleus, and lipid globules Using published and experimentally derived parameters, an example of the calculated absorption, scattering, and to tal optical density spectra for the body of the cell (macrostructure) is shown in Figure 11. Note that the scattering dominates the visible portion of the spectra due to the relatively large particle size (20 m). In combination with the contribution of scattering from the large macrostructure, the nucleotide contribution to absorption is apparent in the UV portion (280-320 nm) of the spectrum. Typically, isolated chrom ophoric nucleotides have a maximum absorbance band at approximately 260 nm (Freifelder, 1982; Walth am et al., 1994; Tuminello et al., 1997; 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 3.5 Wavelen g th ( nm ) RI & Optical Density (AU) Absorption Refractive Index

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34Figure 11. The spectral interpretation of the macrostructure and the contribution of scattering and absorption to the total O.D. spectrum. Notice the absorption bands from 280-320 nm resulting from nucleotides in combination with scattering. Scattering dominates the visible wavelengths from 500-900 nm. The Total O.D. (blue line) is overlapped by the s cattering (green line) after approximately 320 nm. Alupaoei, 2001). The shift from 260 nm is most likely due to the presentation of the nucleotide absorbance band through a relatively large particle. The contribution of scattering and absorp tion to the calculated total optical density spectrum of the nucle us are shown in Figure 12. The spectral properties in the UV range are most influenced by the absorption from nucleotides, as seen by the peak around 280-350 nm. In contrast, the visible wavele ngth range (400-900 nm) is dominated by scattering from the size of the relatively larg e nucleus (10 m). The large feature from 400-600 nm and the gradual rise in optical density from 650-900 nm are dominated by scattering due to the large particle size. 240 300 400 500 600 700 800 90 0 Wavelen g th ( nm ) Optical Density (AU) Total O.D. Scattering Absorption

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35Figure 12. The modeled nucleus and the contribution of scattering and absorption to the total optical density spectrum. Notice the absorption bands from 280-350 nm resulting from nucleotides in combination with scattering. The scattering from the relatively la rge nucleus (10 m) dominates the visible wavelengths. Lipid content of K. brevis has been reported in previ ous literature (Mooney et al., 2007). Lipids are non-chromophoric through mo st of the UV-Vis spectrum; however, they contribute to the over all spectra of organisms thr ough the scattering caused by their relatively large refractive index (>1.55). Therefore, lipids ar e treated as a nonchromphoric group (see Alupoaei & Garcia -Rubio, 2005). The tota l optical density spectrum of the modeled lipid globules is shown in Figure 13. Note that the high refractive index of a mostly non-chromophoric pa rticle affects a total optical density that is dominated by scattering. The estimated contribution from absorption is minimal or close to zero. 240 300 400 500 600 700 800 900 Wavelength (nm)Optical Density (AU) Total O.D. Scattering Absorption

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36Figure 13. The modeled lipid globules and the contribution of scattering to the total optical density spectrum. The contributio n from absorption is minimal or close to zero. 4.3.2 Chloroplasts The contribution of scattering and absorpti on to the total optical density spectrum of the modeled chloroplast is shown in Figur e 14. Notice that this is an example of a chloroplast with pigments and nucleotides (not in nucleoid form) only and therefore lacking the pyrenoid and nucleoi d structures described earlier. This was done to highlight the importance of the co ntributions from scattering or l ack thereof, and the overwhelming contribution to absorption peaks from the ch romophoric groups. Note the impact of the absorption peaks from the pigments on the total optical density, which contributes spectral chararctertistics fr om 350-700 nm. Also note the ab sorbance feature resulting from chromophoric nucleotides is slightly shifted to 280-300 nm, which was most likely due to the presentation of the nucleotid e absorbance band thro ugh a relatively large 240 300 400 500 600 700 800 900 Wavelength (nm)Optical Density (AU)

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37 particle (recall that scattering increases as the particle size increases).In the region from 350-550 nm, there is an apparent combination of several different absorbance peaks of chla (maximum absorbance bands at 410 nm and 430 nm with additional maximum absorbance bands at approximately 618 nm and 660 nm), diadinoxanthin (maximum absorbance bands at 426 nm, 447 nm, 478 nm), b-carotene (maximum absorbance bands at 426 nm, 453.5 nm, and 480 nm), chlorophyll-c2 (maximum absorbance bands at 444.6 nm), chlorophyll-c3 (maximum absorbance bands at 452 nm), 19’-hex fucoxanthin (maximum absorbance bands at 418 nm, 444 nm, and 470 nm), 19’-but fucoxanthin (maximum absorbance bands at 420 nm, 444 nm, and 470 nm), and diatoxanthin (maximum absorbance bands at 427 nm, 454 nm, and 482 nm), fucoxanthin (maximum Figure 14. The contribution of scattering and absorption to the total optical density spectrum of the modeled chloroplast. Note the contribution of the combination of the absorbance bands of chla diadinoxanthin, diatoxanthin, b-carotene, chlorophyll-c2, chlorophyll-c3, 19’-hex fucoxanthin, 19’-but fucoxanthin, fucoxanthin, and gyroxanthin-diester from 350-550 nm. Chlorophylla chlorophyll-c2, and chlorophyll-c3 has additional absorbance bands between 600-700 nm. 240 300 400 500 600 700 800 900 Wavelength (nm)Optical Density Total O.D. Scattering Absorption

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38 absorbance bands at 446 nm and 468) (J effrey et al., 1997), a nd gyroxanthin-diester (maximum absorbance bands at 421 nm, 446 nm, 468 nm) (Millie etal., 1995). The contribution from the pyrenoid and nuc leoids are added to the normalized total optical density of the chloroplast in Figure 15. Representing the nucleotides as nucleoids makes a considerable difference in the spectrum versus adding nucleotides evenly throughout the whole ch loroplast (not shown). For ex ample, the absorption in the 260-300 nm range from the nucleotides is reduced, while scattering in the 300-900 nm range is increased. When the pyrenoid is a ssumed to have non-chromophoric protein and is represented as a 3 m pa rticle, its spectra is domina ted by scattering. The overall theoretical estimation of the combinati on of scattering and absorption from the chloroplast macrostructure, nucleotides, and pyrenoids demonstrate a potentially unique spectral signature. In Figures 14 and 15 the chloroplasts, as suggested by literature reports, are represented as a 6 m particle. However, as shown in Figure 16 the chloroplast appear to be much larger at around 12 m. This suggest s that the chloroplasts may, in fact, be aggregated together within the cell. The cha nge in size from 6 m to 12 m would cause an increase in scattering. Ther efore, a particle size distri bution should be determined for application to the SIM rather than a single, average size from literat ure reports. As shown in Figure 17, there is a significant increase in scattering in chloroplast spectra when both aggregated and individual chloroplasts ar e included in the tota l optical density.

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39Figure 15. The calculated total optical density for th e chloroplasts and the cont ribution from each of the components including the macrostructure, pyrenoid, and nucleoids. Figure 16. Photograph of K. brevis cell from laboratory cultures. No te the large appearance of the chloroplasts due to aggregation within the cell. 25 m 300 240 400 500 600 700 800 900 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Wavelength (nm)Normalized O.D. (AU) Total O.D. Macrostructure Pyrenoid Nucleotides

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40Figure 17. Spectrum of calculated chloroplasts and the contribution of each chloroplast component to the total calculated. Notice the addition of aggregated (larger) chloroplasts, contributing to increased scattering and shift toward larger wavelengths, in combination with individual chloroplasts When comparing individual and aggregated chloroplasts, note the slight shift in spectral feature from 450-550 nm shifte d to 500-600 nm respectively. The region between 400-700 nm is of particular importa nce due to representing the contribution from the pigments. 4.3.3 Calculated spectrum of K. brevis The calculated total optical density for K. brevis and the contribution from each of the components are shown (Figure 18). Clearly, the spectral features contributed by each modeled component, specifically the chromophoric groups su ch as pigments, have a significant effect on the total optical density. Note the ove rall contribution from each component over the entire spec tra. Of particular impor tance is the significant 300 240 400 500 600 700 800 900 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Wavelength (nm)Normalized O.D. (AU) Total O.D. Macrostructure Macrostructure Pyrenoid Nucleotides Aggregated Chloroplasts Individual Chloroplasts

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41Figure 18. The calculated total optical density for K. brevis and the contributing components. contribution from the cell macrostructure and chloroplasts which dominate the overall spectral features. 4.4 Comparison of predicted and measured spectra Note that all calculated spectra are cons idered initial predictions of measured baseline spectra of K. brevis A comparison of measured and modeled spectra shows promising results (Figure 19). In both spectra there is a significant agreement with the combination of nucleotide absorption, and scat tering resulting from size and shape in the 260-350 nm wavelength range (indicated by arro ws). Also note the agreement between both spectra in terms of the contribution from the combination of scattering and absorption from chla fucoxanthin, chlc2 and chlc3 The remaining carotenoids, which 240 300 400 500 600 700 800 900 0 0.2 0.4 0.6 0.8 1 Wavelength (nm)Normalized O.D.(AU) Total O. D. Macrostructure Chloroplast Nucleus Lipid Globules

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42 absorb in the 450-550 nm wavelength regi on, appear to have a relatively minor contribution due to their low concentration in combination with reduced effect from scattering dominance in that region. Overall, the theoretic al predictions reproduce the main features of the measured spectra and, ther efore, offer the possibi lity of identifying a fingerprint for K. brevis. Figure 19. Comparison of measured and calculated total optical density for K. brevis The most notable absorption features, incl uding nucleotides, chla, chlc2, chlc3, and fucoxanthin, are apparent in both spectra. 240 300 400 500 600 700 800 900 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 Wavelength (nm)Normalized O.D.(AU) Measured Total O.D. Chl-a Fucoxanthin Chl-a,c2,c3 Nucleotides

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43 5. Conclusions and Future Work 5.1 Conclusions The original spectral interpretation model was developed for single cell microorganisms, such as bacteria and small eukaryotes (<10 m) (Mattley et al., 2000; Alupoaei, 2001; Callahan et al., 2003; Alupo aei & Garcia-Rubio, 2004; Alupoaei et al., 2004; Alupoaei & Garcia-Rubio, 2005). This resear ch documents the first application of this approach to a larger ( 20-40 m) photosynthetic eukaryote, K. brevis. Additionally, this is the first reported research where th e contribution of the co mbination of scattering and absorption properties of the entire cell of K. brevis have been identified and understood. Finally, utilizing th e ultraviolet and visible portion (240-900 nm) of the spectrum has been shown to increase the de gree of sensitivity, with the ability to characterize additional spectral features, such as cell size, and nuc leotide and protein concentration. The approach of mathematically separati ng the cell into multiple components and combining their respective scattering and absorption properties through weighted physical and chemical characteristics has s hown to significantly predict all the major features within the K. brevis spectrum. Representation of pigment composition within the chloroplast, combined with physical features show substantial influence on the total optical density of the whole cell. Influe nce from the nucleotide content from the macrostructure and nucleus, combined with phys ical features, are al so apparent in the

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44 total optical density. Overall, it is now possi ble to predict with some degree of accuracy which parameter, based on both absorption and scattering components, is responsible for each feature observed in the measured baseline spectra of cultured K. brevis The modeling approach of partitioning th e cell into multiple components allows for the spectral properties of a complex organism to be characterized at a much more detailed level. The potential for a high le vel of characte rization of the cell through the SIM increases the possibility of separating a similar species of the same genus. This is an important attribute due to the earlier menti oned findings of Heil et al. (in press), which show that six different Karenia species were found to co-occur during a 2005 bloom in the Gulf of Mexico. K. brevis dominated total Karenia abundance from bloom initiation to termination, and on average comprised over 81% of Karenia cells in each sample. The second most dominant species, K. mikimotoi, still reached significant concentration levels of 107 cells L-1. Furthermore, the five less abundant Karenia species showed unique differences in spatial and temporal distri bution. The ecological impact of species cooccurring with K. brevis will depend on concentration and toxicity, but will ultimately remain unknown until better monitoring technol ogy is developed. Studies that have focused on K. brevis optical detection, such as Millie et al. (2007), Ki rkpatrick et al. (2000), Robbins et al. (2006), have yet to differentiate among similar species of the same genus. One significant and measurable difference among K. brevis and K. mikimotoi is the chemical composition of the nuclei. Rizzo et al. (1982) reported 113 pg nucleus-1 and a ratio of total protein to DNA of 0.76 for K. brevi s nuclei. In contrast, Wargo & Rizzo (2000) reported 47 pg nucleus-1 and a ratio of total protein to DNA of 1.47 for K.

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45 mikimotoi nuclei. Therefore, despite being extr emely similar species, there are some potential measurable spectral differences between K. brevis and K. mikimotoi Recall that the SIM allows for these slight differences to be represented through weighted physical and chemical characteristics. Therefore, the differences in protein and DNA in the nuclei of the two species allows for the possibility of the identification of distinct spectral features for both spec ies through the SIM. The objective of this research was to evolve the SIM, proposed by Alupoaei and Garcia-Rubio (2005), for applicability to K. brevis to determine whether it can be used as an interpretation tool that will enable the characterization of K. brevis spectra. It has been shown that the SIM is capable of in terpreting the spectral features of K. brevis, and has provided a better understanding of its spect ral properties. Characterization and interpretation of K. brevis spectra is an important and necessary first step to providing more accurate and sensitive detection. Re duction in the differences between the interpreted and measured spectra can be expected as be tter estimates of the optical properties become available. Recall that the SIM utilizes every spectra l feature produced by the interaction of light with the cellular com ponents and their contribution to the total spectrum of K. brevis Therefore, the information from the SIM, highlighted by the included scattering properties, can be applied to reflection measurements made by satellite remote sensing. In addition, recent technology has advanced su ch that multiwavelength spectrophotometers are almost as small as a cell phone and t hus easily deployable for rapid analytical measurement. In conclusion, the application of the SIM, in combination with advancing

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46 technology, could eventually address the det ection deficiencies of current optical detection technology and facili tate the understanding of K. brevis bloom ecology. 5.2 Future Work Future studies will need to focus on validation of the SIM. Validation of the SIM involves the determination of relevant spectra to conduct reproducibility experiments and obtain meaningful and reproducible data. This validation step ensures that the K. brevis spectrum is reproducible and relevant. Spect ral measurements are not a major sampling issue with regards to analytical precision of the spectrophotometer its elf. However, the sampling process and preparation, growth phase and the natural vari ability in terms of size, shape and chemical composition can introduce measurable differences in spectral features. Therefore, it is necessary to fix so me of these variables, while making replicate measurements, to provide a char acteristic spectral fingerprint, or meaningful spectra, of K. brevis for the development of the SIM. The design of reproducibility experiments will provide relevant spectra for the assessment of the SIM through the comparison of modeled spectra to data outside a set of experimental design parameters, such as di fferent growth phases. Changes in the cell population over each growth phase can be grouped in changes in number of cells, cell size, shape, and internal structure, whic h can be quantified through the SIM. Future studies that successfully predic t different concentrations, ce ll sizes, shape and internal structures will validate the SIM. The estimated values in this research repr esent the value that is used to reproduce a baseline spectrum of K. brevis In future work, once the reproducibility of K. brevis

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47 spectra is achieved (as descri bed above), future studies should implement a least-squares iterative procedure where a transmission sp ectrum is calculated with Equation 4 and compared with the measured transmission spectrum at each wavelength. The procedure can be repeated with new estimates of the parameters obtained using a least-squares algorithm (Alupoaei, 2001). This iterative pr ocedure can continue until convergence of calculated and measured spectra is achieved. In addition, ap plication of the SIM to the measured spectra should result in quantita tive differences between different growth phases, changes in light, etc. For this purpose, a least-squares algorithm should be used to estimate the parameters, such as the average size of the microorganism, the average size of the internal scattering stru ctures, the volume fraction of th e internal structure, and the chemical composition with regards to the total nucleotide and photopigment concentrations. Instrument sensitivity was a limiting fact or in this study and will need to be addressed in future studies. Due to a large cell size, K. brevis creates significant scattering, which leads to less obvious absorp tion features within the spectrum. This problem can be alleviated through an increase in pathlength, along with the use of a more powerful light source, which will result in more defined absorption features.

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48 List of References Aas, E. (1996) Refractive index of phyt oplankton derived from its metabolite composition. Journal of Plankton Research 18(12), 2223-2249. Alupoaei, C. E. (2001) Modeling of the Transmission Spectra of Microorganisms M.S. Thesis, University of South Fl orida, St. Petersburg, FL, 93 pp. Alupoaei, C. E., Garcia-Rubio, L. H. (2004) Growth of Behavior of Microorganisms using UV-Vis Spectroscopy: Escherichia coli Biotechnology and Bioengineering 85(2), 163-167. Alupoaei, C. E., Olivares, J.A., Garcia-Rubi o, L. H. (2004) Quantitative spectroscopy analysis of prokaryotic cells : vegetative cells and spores. Biosensors and Bioelectronics 19(8), 893-903. Alupoaei, C. E., Garcia-Rubio, L. H. (2005) An interpretation model for the UV-Vis Spectra of Microorganisms. Chemical Engineering Communications 192(2), 198-218. Bjornland, T., Haxo, F.T., Liaaen-Jensen, S. (200 3) Carotenoids of the Florida red tide dinoflagellate Karenia brevis. Biochemical Systematics and Ecology 31(10), 1147-1162. Blackburn, S. I., Bolch, C. J. S., Haskar d, K. A., and Hallegraeff, G. M. (2001) Reproductive Compatibility Among Four Global Populations of the Toxic Dinoflagellate Gymnodinium catenatum (Dinophyceae). Phycologia 40(1), 78-87. Bohren, C. F. & Huffman, D.R. (1983) Absorption and Scattering of Light by Small Particles John Wiley & Sons, New York. Borkhsenious, O. N., Mason, C. B., & Mo roney, J. V. (1998) The Intracellular Localization of Ribulose-1,5-Bispho sphate Carboxylase/Oxygenase in Chlamydomonas reinhardtii Plant Physiology 116(4), 1585-1591. Callahan M.R., Rose J.B., Garcia-Rubio L. (2003) Use of multiwav elength transmission spectroscopy for the characterization of Cryptosporidium parvum oocysts: quantitative interpretation. Environmental Science & Technology 37(22), 5254-5261. Cattolico, R. A. (1978) Variati on in Plastid Number: Effect on Chloroplast and Nuclear Deoxyribonucleic Acid Compleme nt in the Unicellular Alga Olisthodiscus luteus Plant Physiology 62(4), 558-562.

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49 Elicabe, G., & Garcia-Rubio, L. (1989) Latex particle size distribution from turbidimetric measurements combining regularization and generalized cross-va lidation techniques. Journal of Colloid and Interface Science 129(1), 192-200. Elicabe, G., & Garcia-Rubio, L. (1990) Latex Particle Size Distribution from Turbidimetry Using a Combination of Regul arization Techniques and Generalized CrossValidation, in Polymer Characterization: Physic al Property, Spectroscopic, and Chromatographic Methods, edited by Craver, C., Craver, C. D., Provder, T., Advances in Chemistry Series 227, pp. 83-104. Evans, T.J., Kirkpatrick, G., Millie, D., Chapman, D., Schofield, O. (2001) Photophysiological respons es of the toxic re d-tide dinoflagellate Gymnodinium breve (Dinophyceae) under natural sunlight Journal of Plankton Research 23(11), 1177-1193 Falkowski, P. G., & Raven, J. A., (1997) Aquatic Photosynthesis Blackwell Science, Maleden, MA, 384 pp. Freifelder, D. (1982) Physical Biochemistry 2nd ed. W.H. Freeman and Company. Ch. 14, pp. 504. Golub, G.H., Heath, M., & Wahba, G. (1979) Generalized Cross-Va lidation as a Method for Choosing a Good Ridge Parameter. Technometrics 21(2), 215-223. Heil, C., Truby, E., Wolny, J., Pigg, R., Richardson, B., Garrett, M., Haywood, A., Petrik, K., Flewelling, L., Stone, E., Cook, S., Sc ott, P., Steidinger, K., Landsberg, J. (In Press) The multi-species nature of the 2005 Karenia Bloom in the eastern Gulf of Mexico. Proceedings of the12th International Conference on Harmful Algae September 4-9, 2006, Copenhagen. Holdsworth, R. H. (1971) The Isolation a nd Partial Characterization of the Pyrenoid Protein of Eremosphaera viridis The Journal of Cell Biology 51(2), 499-513. Jeffrey, S.W., Mantoura, R.F.C., Bjrnland, T. ( 1997) Data for the identification of 47 key phytoplankton pigments, in Phytoplankton pigments in oceanography: guidelines to modern methods edited by Jeffrey, S.W., Mantoura, R.F.C., Wright, S.W., UNESCO, Paris, pp. 449-559. Kerker, M. (1969) The Scattering of Light and Other Electromagnetic Radiation. Academic Press, New York, 670 pp. Kirkpatrick, G.J., Millie, D.F., Moline, M.A ., Schofield, O. (2000) Optical discrimination of a phytoplankton species in natural mixed populations. Limnology and Oceanography 45(2), 467-471.

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50 Kite, G. C., & Dodge, J. D. (1985) Struct ural Organization of Plastid DNA in Two Anomalously Pigmented Dinoflagellates Journal of Phycology 21(1), 50-56. Lim, D. V. (1989) Microbiology West Publishing Comp any, St. Paul, 630 pp. Mahoney, K. (2003) Backscattering of light by Kare nia brevis and implications for remote sensing reflectance Ph.D. Dissertation, University of Southern Mississippi, Hattiesburg, MS, 135 pp. Mattley, Y., Leparc, G., Potte r, G., & Garca Rubio, L. (2000) Light Scattering and Absorption Model for the Quantitative Interpretation of Human Platelet Spectral Data. Photochemistry and Photobiology 71(5), 610-619. Millie, D.F., Kirkpatrick, G.J., Vinyard, B. T. (1995) Relating photosynthetic pigments and in vivo optical density sp ectra to irradiance for the Florida red-tide dinoflagellate Gymnodinium breve Marine Ecology Progress Series 120, 65-75. Millie, D.F., Schofield, O.M., Kirkpatrick, G.J ., Johnsen, G. Tester, P.A., Vinyard, B.T. (1997) Detection of harmful algal bloo ms using photopigments and absorption signatures: A case study of the Fl orida red tide dinoflagellate, Gymnodinium breve Limnology and Oceanography 42(5), 1240-1251. Mooney, B. D., Nichols, P.D., de Salas, M. F., Hallegraeff, G. M. (2007) Lipid, fatty acid, and sterol compositi on of eight species of Kareniaceae (Dinophyta): chemotaxonomy and putative lipid phycotoxins. Journal of Phycology 43(1), 101-111. rnlfsdttir, E.B., Pinckney, J.L., Tester, P.A. (2003) Quantification of the relative abundance of the toxic dinoflagellate, Karenia brevis (Dinophyta), using unique photopigments. Journal of Phycology 39(2), 449-457. Poindexter, J. S. (1971) Microbiology. An Introduction to Protists. Macmillan, New York, 582 pp. Rizzo, P.J., Jones, M., Ray, S.M. (1982) Isol ation and properties of isolated nuclei from the Florida red tide dinoflagellate Gymnodinium breve (Davis). Journal of Protozoology 29(2), 217–222. Robbins, I.C., Kirkpatrick, G.J., Blackwell, S. M., Hillier, J., Knight, C.A., Moline, M.A. (2006) Improved monitoring of HABs using autonomous underwater vehicles (AUV). Harmful Algae 5(6), 749-761. Schofield, O., Grzymski, J., Bisset, W.P., Ki rkpatrick, G.J., Millie, D.F., Moline, M., Roesler, C.S. (1999) Optical Monitoring and Forecasting for Harmful Algal Blooms: Possibility or Pipe Dream? Journal of Phycology 35(6), 1477-1496.

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51 Spear, A.H., Daly, K.L., Huffman, D.E., Garcia-Rubio, L.H. (2009) Progress in Develping a New Detecion Met hod for the Dinoflagellate, Karenia brevis using Multiwavlength Spectroscopy. Harmful Algae 8(2), 189-195. Staehr, P. A., Henriksen, P., & Markager, S. (2002) Photoacclimation of four marine phytoplankton species to irradian ce and nutrient availability. Marine Ecology Progress Series 238, 47-59. Staehr, P. A., & Cullen, J. J. (2003) Detection of Karenia mikimotoi by spectral absorption signatures. Journal of Plankton Research 25(10), 1237-1249. Steidinger, K. A., Truby, E. W., Dawes, C.J. (1978) Ultrastructure of the red tide dinoflagellate Gymnodinium breve I. General Description. Journal of Phycology 14(1), 72-79. Steidinger, K.A., Vargo, G.A., Tester, P.A ., Tomas, C.R. (1998) Bloom dynamics and physiology of Gymnodinium breve with emphasis on the Gulf of Mexico, in Physiological Ecology of Harmful Algal Blooms edited by Anderson, D.M., Cembella, A.D., Hallegraeff, G.M., Spri nger-Verlag, Berlin, pp.133-153. Tuminello, P. S., Arakawa, E.T., Khare, B.N., Wrobel, J.M., Querry, M.R., Milham, M.E. (1997) Optical Properties of Bacillus subtilis from 0.2 to 2.5 m. Applied Optics 36(13), 2818-2823. Waltham, C., Boyle J., Ramey B., Smith, J. (1994) Light scattering and absorption caused by bacterial activity in water. Applied Optics 33(31), 7536-7540. Wargo, M. J., & Rizzo, P. J. (2000). Characterization of Gymnodinium mikimotoi (dinophyceae) Nuclei and Identification of the Major Histone-Like Protein, Hgm. Journal of Phycology 36(3), 584-589. Van Der Hulst, H. (1957) Light Scattering by Small Particles. Wiley & Sons, New York, 470 pp. Van Krevelen, D.W., 1990 Properties of Polymers: Thei r Correlation with Chemical Structure, Their Numerical Estimation and Prediction from Additive Group Contributions Third Edition. Elsevier, New York, 875 pp.

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52 Appendices

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53 Appendix A: Refractive Index and Abso rption Coefficient of Chromophores 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 3.5 4 Wavlength (nm)RI & Optical Density (AU)Diadinoxanthin Absorption Refractive Index

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54 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 Wavlength (nm)RI & Optical Density (AU) 19’-but fucoxanthin Absorption Refractive Index 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 Wavelength (nm)RI & Optical Density (AU) 19’-hex fucoxanthin Absorption Refractive Index

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55 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 3.5 4 Wavlength (nm)RI & Optical Density (AU)Diatoxanthin Absorption Refractive Index 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 3.5 4 Wavlength (nm)RI & Optical Density (AU) Gyroxanthin-Diester Absorption Refractive Index

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56 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Wavelength (nm)RI & Optical Density (AU) Chlorophyll c2 Absorption Refractive Index 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Wavelength (nm) RI & O p ti ca l D ens it y (AU) Chlorophyll c3 Absorption Refractive Index

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57 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 Wavelength (nm) RI & O pt i ca l D ens i ty (AU) Fucoxanthin Absorption Refractive Index 240 300 400 500 600 700 800 900 0 0.5 1 1.5 2 2.5 3 Wavlength (nm)RI & Optical Density (AU)b-carotene Absorption Refractive Index

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58 240 300 400 500 600 700 800 900 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Wavelength (nm)RI & Optical Density (AU)Nucleotides (DNA,RNA) Absorption Refractive Index


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The characterization and interpretation of the spectral properties of Karenia brevis through multiwavelength spectroscopy
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ABSTRACT: Optical research has shown that Karenia brevis has distinct spectral characteristics, yet most studies have focused exclusively on absorption and chemical properties, ignoring the size, shape, internal structure, and orientation, and their effect on scattering properties. The application of a new spectral interpretation model to K. brevis is shown to provide characterization of unique spectral information, not previously reported, through the use of scattering and absorption properties. The spectroscopy models are based on light scattering and absorption theories, and the approximation of the frequency-dependent optical properties of the basic constituents of living organisms. The model uses the process of mathematically separating the cell into four components, while combining their respective scattering and absorption properties, and appropriately weighted physical and chemical characteristics. The parameters for the model are based upon both reported literature values, and experimental values obtained from laboratory grown cultures and pigment standards. Measured and mathematically derived spectra are compared to determine the adequacy of the model, contribute new spectral information, and to establish the proposed spectral interpretation approach as a new detection method for K. brevis. Absorption and scattering properties of K. brevis, such as cell size/shape, internal structure, and chemical composition, are shown to predict the spectral features observed in the measured spectra. This research documents for the first time the exploitation of every spectral feature produced by the interaction of light with the cellular components and their contribution to the total spectrum of a larger (20-40 m) photosynthetic eukaryote, K. brevis. Overall, this approach could eventually address the detection deficiencies of current optical detection applications and facilitate the understanding of K. brevis bloom ecology.
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