Chemometrics for the description and modeling of advanced spectroscopy data
Event details
| Date | 21.05.2014 |
| Hour | 10:15 › 11:15 |
| Speaker | Cyril Ruckebusch |
| Location | |
| Category | Conferences - Seminars |
Abstract
In this presentation, we emphasize on the application of chemometric approaches in ultrafast spectroscopy and super-resolution imaging. From the measurement, chemometric analysis involves i) preprocessing steps, such as baseline correction or artifact elimination, ii) exploratory steps, involving data representation, iii) soft-modeling, to provide data description, and, iv) data fitting, from the postulation of a reasonable model resulting from the previous steps.
We first review the application of multivariate curve resolution for the analysis of ultrafast time-resolved spectroscopy data, to recover the time dependent profiles and the transient spectra related to the photochemical species involved in the reaction dynamics. We then tackle current issues in single-molecule super-resolution fluorescence imaging, where the rendered image directly depends on the algorithm and method chosen for data analysis.
Bio: Prof. Ruckebusch's research focuses on methodological developments and applications in the field of chemometrics for the treatment of multivariate spectroscopic data. His goal is the characterization and understanding of reactions or complex molecular mechanisms by time-resolved spectroscopy coupled to specific methods for analyzing mixtures (multivariate curve resolution), in particular the study of photoinduced processes of molecules / innovative photochromic systems. In parallel, he works at the development of multivariate models (multivariate calibration) in analytical spectroscopy for the quantitative prediction or classification of complex samples.
In this presentation, we emphasize on the application of chemometric approaches in ultrafast spectroscopy and super-resolution imaging. From the measurement, chemometric analysis involves i) preprocessing steps, such as baseline correction or artifact elimination, ii) exploratory steps, involving data representation, iii) soft-modeling, to provide data description, and, iv) data fitting, from the postulation of a reasonable model resulting from the previous steps.
We first review the application of multivariate curve resolution for the analysis of ultrafast time-resolved spectroscopy data, to recover the time dependent profiles and the transient spectra related to the photochemical species involved in the reaction dynamics. We then tackle current issues in single-molecule super-resolution fluorescence imaging, where the rendered image directly depends on the algorithm and method chosen for data analysis.
Bio: Prof. Ruckebusch's research focuses on methodological developments and applications in the field of chemometrics for the treatment of multivariate spectroscopic data. His goal is the characterization and understanding of reactions or complex molecular mechanisms by time-resolved spectroscopy coupled to specific methods for analyzing mixtures (multivariate curve resolution), in particular the study of photoinduced processes of molecules / innovative photochromic systems. In parallel, he works at the development of multivariate models (multivariate calibration) in analytical spectroscopy for the quantitative prediction or classification of complex samples.
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