Analysis of Raman spectra using the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm

The development of the disease leads to changes in the biochemical composition of biological tissues. Therefore, determination of the composition is important for medical diagnostics. In recent years, Raman spectroscopy has been used to study biological tissues. However, Raman spectra of most tissue components overlap significantly, and it is difficult to separate individual components. The aim of our study is to investigate the possibilities of the multivariate curve resolution alternating least squares method for the analysis of in vivo Raman spectra. We used a portable conventional spectroscopy setup. The analysis of Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma and pigmented nevus was performed. As a result, we obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The classification of the Raman spectra of various diseases (malignant vs. benign neoplasms, malignant melanoma vs pigmented neoplasms) by the contribution of the spectra of the components shows the classification accuracy about 70%. The obtained results show the possibility of unmixing several spectrally similar components using the multivariate curve resolution alternating least squares analysis even under noisy conditions of the recorded Raman spectra. The method may be used for the analysis of Raman spectra with a low signal-to-noise ratio.