at Google ScholarTitle data
Fellner, Lea ; Kraus, Marian ; Walter, Arne ; Duschek, Frank ; Bocklitz, Thomas ; Gabbarini, Valentina ; Rossi, Riccardo ; Puleio, Alessandro ; Malizia, Andrea ; Gaudio, Pasquale:
Determination of Composition of Mixed Biological Samples Using Laser-Induced Fluorescence and Combined Classification/Regression Models.
In: The European Physical Journal Plus.
Vol. 136
(2021)
.
- 1122.
ISSN 2190-5444
DOI: https://doi.org/10.1140/epjp/s13360-021-02019-1
Abstract in another language
Laser-induced fluorescence (LIF) provides the ability to distinguish organic materials by a fast and distant in situ analysis. When detecting the substances directly in the environment, e.g., in an aerosol cloud or on surfaces, additional fluorescence signals of other fluorophores occurring in the surrounding are expected to mix with the desired signal. We approached this problem with a simplified experimental design for an evaluation of classification algorithms. An upcoming question for enhanced identification capabilities is the case of mixed samples providing different signals from different fluorophores. For this work, mixtures of up to four common fluorophores (NADH, FAD, tryptophan and tyrosine) were measured by a dual-wavelength setup and spectrally analyzed. Classification and regression are conducted with neural networks and show an excellent performance in predicting the ratios of the selected ingredients.
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 |
| Result of work at the UBT: | No |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science 500 Science > 530 Physics |
| Date Deposited: | 11 May 2023 09:05 |
| Last Modified: | 11 May 2023 09:05 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76409 |