KEYWORDS: Raman spectroscopy, Skin, Proteins, Tissues, In vivo imaging, Biological research, Melanoma, Chemical analysis, Collagen, Analytical research
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.
Changes of concentrations of plasma free amino acids (PFAAs) is an essential feature of protein metabolic abnormalities in cancer patients. In this study, we aim to decompose Raman spectra of mixtures of amino acids. The effect of noise on the decomposition result is investigated. The experimentally measured spectra of amino acids and artificially simulated spectra of their mixtures are studied. As a decomposition method, Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) analysis is used. It is shown that one can evaluate the concentration of amino acids in the mixtures using the Raman spectra of the mixtures and the spectra of pure amino acids. The results can be used in further research on lung cancer.
In the paper, we introduce an additive simulation approach of Raman light scattering by skin cancer using the Monte Carlo method. Raman light scattering from normal skin and malignant melanoma is investigated. A two-stage algorithm for simulating Raman light scattering from skin based on the known photon transport algorithm has been developed. A method for additive modeling of skin pathologies is proposed. The main idea of this method is a hypothesis that an experimental Raman spectrum of normal skin, obtained by averaging in vivo Raman spectra of normal skin, may be served as a “substrate” for the feature simulated Raman spectrum. Thus, the pathology, for their part, may be “grown” by adding on this “substrate” Raman specific components set related to a tumor type. Additive simulation of malignant melanoma has been carried out. The possibility of using the developed algorithm to determine the component composition of the skin by the in vivo Raman spectrum of skin is discussed. An attempt to evaluate the change in the concentration of skin components during the development of cancer has been made.
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