Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics
Abstract
:1. Introduction
2. Results and Discussion
2.1. Histology Reference Data
2.2. FTIR Spectral Data
2.3. Preprocessing Strategies for the Broadband Spectra
2.4. Preprocessing Strategies for the Sparse Spectra
3. Methods
3.1. Measured Data
3.1.1. Bovine Broadband Spectra
3.1.2. Human Broadband Spectra
3.1.3. Histology
3.2. Simulated Broadband Spectra
- PCA decomposition of matrix as was done, where are scores and are loadings of matrix .
- Calculation of mean and standard deviation of scores for the chosen number of loadings A.
- New scores were drawn randomly from the respective normal distribution calculated for each score . The random drawing had a feedback loop which was activated if scores higher than the maximum or lower than the minimum obtained in experimental dataset were drawn. This was done to prevent very unrealistic score values being drawn.
- The first set of simulated data were obtained by . These spectra generated for healthy and damaged groups separately were further merged into one dataset and corrected again by the EMSC1 method to avoid creating artificial physical effects by random recombination of loadings in the simulation.The resulting dataset contained the final simulated pure absorbance spectra. To simulate apparent spectra which are “perturbed” by physical effects naturally present in the real data, the following was done.
- Group specific EMSC1 variations were added to simulated pure spectra using parameters drawn from the distributions .
- The spectra were merged into one dataset and white noise vectors w were also added by randomly drawing from a uniform distribution with the level similar to experimental dataset.
3.3. Sparse Spectra
3.4. Spectral Preprocessing and Preclassification Strategies
3.5. Classification Modelling
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Tafintseva, V.; Lintvedt, T.A.; Solheim, J.H.; Zimmermann, B.; Rehman, H.U.; Virtanen, V.; Shaikh, R.; Nippolainen, E.; Afara, I.; Saarakkala, S.; et al. Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics. Molecules 2022, 27, 873. https://doi.org/10.3390/molecules27030873
Tafintseva V, Lintvedt TA, Solheim JH, Zimmermann B, Rehman HU, Virtanen V, Shaikh R, Nippolainen E, Afara I, Saarakkala S, et al. Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics. Molecules. 2022; 27(3):873. https://doi.org/10.3390/molecules27030873
Chicago/Turabian StyleTafintseva, Valeria, Tiril Aurora Lintvedt, Johanne Heitmann Solheim, Boris Zimmermann, Hafeez Ur Rehman, Vesa Virtanen, Rubina Shaikh, Ervin Nippolainen, Isaac Afara, Simo Saarakkala, and et al. 2022. "Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics" Molecules 27, no. 3: 873. https://doi.org/10.3390/molecules27030873