at Google ScholarTitle data
Ryabchykov, Oleg ; Bocklitz, Thomas ; Ramoji, Anuradha ; Neugebauer, Ute ; Foerster, Martin ; Kroegel, Claus ; Bauer, Michael ; Kiehntopf, Michael ; Popp, Jürgen:
Automatization of spike correction in Raman spectra of biological samples.
In: Chemometrics and Intelligent Laboratory Systems.
Vol. 155
(2016)
.
- pp. 1-6.
ISSN 1873-3239
DOI: https://doi.org/10.1016/j.chemolab.2016.03.024
Abstract in another language
Raman spectroscopy as a technique has high potential for biological applications, e.g. cell and tissue analysis. In these applications, large data sets are normally recorded which require automated analysis. Unfortunately, a lot of disturbing external influences exist, which negatively affect the analysis of Raman spectra. A problematic corrupting effect in big data sets is cosmic ray noise, which usually produces intense spikes within the Raman spectra. In order to exploit Raman spectroscopy in real world applications, detection and removing of spikes should be stable, data-independent and performed without manual control. Herein, an automatic algorithm for cosmic ray noise correction is presented. The algorithm distinguishes spikes from spectra based on their response to a Laplacian, e.g. their sharpness. Manual rating of the spike presence was used as a benchmark for algorithm validation. The algorithm's sensitivity was estimated to be above 99%.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 15 May 2023 12:04 |
| Last Modified: | 07 Sep 2023 13:47 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76361 |