研究领域

Environmental Chemistry

Dr. Vogt's research interests are interdisciplinary and focus on optical sensing techniques and statistical data analysis. Optical sensing techniques: Our research will open new analytical perspectives for investigating heterogeneous and dynamic chemical systems like atmospheric pollution, complex biological and industrial samples. Conventional sensors, which perform spot analyses or extract samples, are in general insufficient for such sensing tasks, as locally gained information is usually not representative. In order to gain comprehensive information about heterogeneous systems, distributions of analytes will be studied by means of spatially resolved spectroscopy. We will develop innovative sensing concepts, which combine optical imaging techniques with wavelength dispersion. In order to ensure a broad range of applications, the wavelength coverage will range from the visible to the long wave infrared. Within seconds such chemical imaging sensors will acquire tens or even hundreds of thousands of spectra in parallel. This will establish spectroscopic studies at high spatial and high temporal resolution. Spectroscopic imaging will be realized in various different modes; for instance as passive remote sensing analyzing thermal radiation emitted from extended areas or microscopic studies of fluorescence. Sensor development includes the design of optical setups, electronic control systems and software. The next step will be combining two or more spectroscopic analyzers in order to extend these concepts to stereo-imaging, i.e. sensing in three spatial dimensions. Based on this innovative approach we will investigate fundamentals of 3-dimensional processes like chemical interactions at surfaces or porous media. Statistical data analysis (chemometrics): Chemometrics computations are based on multivariate regression techniques and in short establish parallel quantification of numerous analytes. Conventional chemometric techniques, however, require well-known and highly reproducible measurement conditions. If disturbances or unknown analytes interfere, the analysis results become worthless. Because such situations are very common e.g. in remote open-path sensing, we will develop innovative chemometric methods, which are robust and reliable enough for such challenging tasks. Studying spatial distributions of spectroscopic signatures introduces a lot of additional information. Our research in statistical data analysis will also open a new field in chemical classification and qualitative sample characterization. These techniques will enable us to investigate substructures like texture found in heterogeneous samples; these methods will ensure that chemical analyses are succeeding even if spectroscopic information alone is insufficient for characterization. This combination of novel sensing techniques and chemometric data evaluation will result in comprehensive methods in analytical chemistry.

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Morgan B. McConico, Frank Vogt. Assessing Impacts of Nutrient Competition among Microalgae Species on their Chemical Composition Analytica Chimica Acta (2013), submitted. R. Horton, M. McConico, C. Landry, T. Tran, F. Vogt Introducing Nonlinear, Multivariate ‘Predictor Surfaces’ for Quantitative Modeling of Chemical Systems with Higher-order, Coupled Predictor Variables. Analytica Chimica Acta 746 (2012), 1-14. M. McConico, R. Horton, K. Witt, F. Vogt, Monitoring Chemical Impacts on Cell Cultures by means of Image Analyses. Journal of Chemometrics 26 (2012),585–597. M. McConico, F. Vogt, Modeling Nutrient Impacts on Microalgae Cells via Image Analyses. Journal of Chemometrics (2012), accepted. Vogt, F.; Gritti, F.; Guiochon, G. Polynomial multivariate least-squares regression for modeling nonlinear data applied to in-depth characterization of chromatographic resolution. J. Chemom. 2011; 25: 575–585. M. Giordano, S. Ratti S, A. Domighin, F. Vogt. Spectroscopic Classification of 14 Different Microalgae Species – First Steps towards Spectroscopic Measurement of the Biodiversity in Marine Environments Plant Ecology and Diversity 2, 155 (2009) M. Gilbert, C. Frick, A. Wodowski, F. Vogt. Spectroscopic Imaging for Detection and Discrimination of Different E.Coli strains Applied Spectroscopy 63, 6 (2009) J. Cramer, F. Vogt, K. Booksh Smart Sensors chapter in ‘Comprehensive Chemometrics’, Elsevier, 2008, ISBN-13: 978-0-444-52702-8 R. Luttrell, F. Vogt. Accelerating Kernel Principal Component Analysis (KPCA) by Utilizing 2-Dimensional Wavelet Compression: Applications to Spectroscopic Imaging. Journal of Chemometrics 22, 510 (2008) M. Gilbert, R. Luttrell, D. Stout, F. Vogt. Introducing Chemometrics to the Analytical Curriculum – Combine Theory and Lab Experience. Journal of Chemical Education 85, 135 (2008) M. Gilbert, F. Vogt. Augmenting Spectroscopic Imaging for Analyses of Samples with Complex Surface Topographies Analytical Chemistry 79, 5424 (2007) R. Luttrell, M. Gilbert, F. Vogt. Composing Hybrid Wavelets for Optimum Representation and Accelerated Evaluation of N-way Data Sets. Journal of Chemometrics 21, 65 (2007) F. Vogt, K. Booksh. Chemometrics Encyclopedia of Chemical Technology, concise 5th ed., John Wiley&Sons, New York, 2007, ISBN 978-0-470-04748-4 F. Vogt. Trends in Remote Spectroscopic Sensing – Experimental Techniques and Chemometric Concepts. Current Analytical Chemistry 2, 107 (2006) Accelerating the Analyses of 3-way and 4-way PARAFAC Models Utilizing Multi-dimensional Wavelet Compression. J. Cramer, Y-C Kim, F. Vogt, and K. Booksh, J. Chemometrics 19, 593 (2005). Spectrophotometry: Derivative techniques. F. Vogt in Encyclopedia of Analytical Science (2nd ed.), edited by P. Worsfold, A. Townshend, and C. Poole, Elsevier, Oxford, Vol. 8, pp. 335-343, 2005. Utilizing 3-dimensional wavelet transforms for accelerated evaluation of hyperspectral image cubes. F. Vogt, S. Banerji, and K. Booksh, J. Chemometr. 18, 350 (2004). Chemometric methods for data analysis. F. Vogt and K. Booksh in Kirk-Othmer Encyclopedia of Chemical Technology (4th online ed.), edited by A. Seidel, John Wiley & Sons, New York, 2004. Chemometric correction of drift effects in optical spectra. F. Vogt, H. Steiner, K. Booksh, and B. Mizaikoff, Appl. Spectrosc. 58, 683 (2004). Introduction and application of secured principal component regression (sPCR) for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing. F. Vogt and B. Mizaikoff, Anal. Chem. 75, 3050 (2003). Dynamic determination of the dimension of PCA calibration models using F-statistics. F. Vogt and B. Mizaikoff, J. Chemometr. 17, 346 (2003). First results on infrared attenuated total reflection spectroscopy for quantitative analysis of salt ions in seawater. F. Vogt, M. Kraft, and B. Mizaikoff, Appl. Spectrosc. 56, 1376 (2002). An ultraviolet spectroscopic method for monitoring aromatic hydrocarbons dissolved in water. F. Vogt, M. Tacke, M. Jakusch, and B. Mizaikoff, Analyt. Chim. Acta 422, 187 (2000). Optical UV derivative-spectroscopy for monitoring gaseous emissions. F. Vogt, U. Klocke, K. Rebstock, G. Schmidtke, V. Wander, and M. Tacke, Appl. Spectrosc. 53, 1352 (1999).