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Guo, Shuxia ; Heinke, Ralf ; Stöckel, Stephan ; Rösch, Petra ; Popp, Jürgen ; Bocklitz, Thomas:
Model transfer for Raman-spectroscopy-based bacterial classification.
In: Journal of Raman Spectroscopy.
Bd. 49
(2018)
Heft 4
.
- S. 627-637.
ISSN 1097-4555
DOI: https://doi.org/10.1002/jrs.5343
Abstract
Raman spectroscopy has gained increasing attention in biomedical diagnostics thanks to instrumental developments and chemometric models that enhance the accuracy and speed of this technique. In particular, a model transfer procedure is needed if the chemometric models are utilized to predict a new dataset measured under (secondary) conditions different to the training data (primary). The model transfer methods try to achieve satisfactory prediction on the secondary dataset with minimal or no training samples measured under secondary conditions. Model transfer methods that have been reported are mostly applied for near-infrared spectroscopy and in regression problems. The investigation of model transfer in Raman spectroscopy and classification is rare. Our recently reported Tikhonov regularization based on partial least squares regression (TR-PLSR) was utilized for model transfer of Raman-based classification models for spore species. In the present work, we show that the TR-PLSR also works for Raman spectra of vegetative bacteria, even though the Raman spectra of 3 species of bacteria were acquired on 3 different Raman spectrometers. Additionally, we report 2 newly developed model transfer methods for Raman spectra: movement of principal components scores and spectral augmentation. Both methods were validated based on the Raman spectra of bacterial spores and vegetative bacteria, where a significant improvement of the model transferability was observed. The movement of principal components scores method yielded results comparable with those of the TR-PLSR. However, the new methods are superior to TR-PLSR in 2 ways: No training samples in the secondary conditions are necessary, and the methods are not restricted to partial least squares regression but can also be applied to other models. Both advantages are important in real-world applications and represent a large step for improving the model transfer of Raman spectra.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Eingestellt am: | 17 Mai 2023 13:19 |
| Letzte Änderung: | 17 Mai 2023 13:19 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76315 |