at Google ScholarTitle data
Luo, Ruihao ; Popp, Jürgen ; Bocklitz, Thomas:
Deep Learning for Raman Spectroscopy : A Review.
In: Analytica.
Vol. 3
(2022)
Issue 3
.
- pp. 287-301.
ISSN 2673-4532
DOI: https://doi.org/10.3390/analytica3030020
Abstract in another language
Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | deep learning; Raman spectroscopy; chemometrics; machine learning |
| 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: | 500 Science > 530 Physics |
| Date Deposited: | 31 May 2023 12:42 |
| Last Modified: | 31 May 2023 12:42 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/81069 |