Open AccessLC‑spectra analysis algorithm for non‑invasive diagnostics by oropharyngeal washout samples
Author(s) -
А. И. Николаев,
I. N. Antonova,
O. S. Donskaya,
L. G. Vladimirova
Publication year - 2020
Publication title -
medicinskij alfavit
Language(s) - English
Resource type - Journals
eISSN - 2949-2807
pISSN - 2078-5631
DOI - 10.33667/2078-5631-2019-4-35(410)-23-27
Subject(s) - principal component analysis , spectroscopy , spectral line , varimax rotation , mathematics , analytical chemistry (journal) , optics , statistics , chemistry , chromatography , physics , cronbach's alpha , quantum mechanics , astronomy , descriptive statistics
The purpose of the study was to offer an alternative algorithm for analysis of LC‑spectra for non‑invasive diagnosis of oropharyngeal samples flushing. Materials and methods. The study included 23 patients with gallstone disease, 22 patients with urolithiasis, 22 patients with salivary stone disease, 4 patients with abundant dental sediments and 13 persons in the control group. Materials for research were serum and oropharyngeal washout. Research conducted by laser correlation spectroscopy in laser spectroscopy correlation computerized LCS‑03‑”INTOX”. The results. In the study of systems with unbalanced anisotropic nanoparticles should be no stray light to the square of the particle and its linear size indicators each channel light scattering, spectrum is divided into the radius of the particles corresponding to this channel and in future all newly obtained spectrum is converted to 1. To reduce the dimensionality of the data analysis was carried out of the whole array of spectra of SC and LC–CSG principal component method (CC) with varimax ro‑ tation (et). In both types of spectrum first 12 GK explained more than 96 % of the variance, the remaining 4 % were accepted as the noise component. Top values on the basis of values of factor loadings CC has compiled a algorithm to convert primary 32‑channel spectra LKS in 12‑band by calculating light scattering totals in each range and their normalization relative to the total on all of the flare 12 fixed ranges, received for the 1 (100 %) with the help of several successive stages of the LST with step by step inclusion of variables applied to spectra LKS SC and CSG patients with biomineralopathy can be fairly effective discrimination against diseases as from healthy individuals, and among themselves. Conclusion. The algorithm processing LC‑spectra of SC and CSG, including rationing by the number of particles of light scattering spectra, the reducer dimension range from 32 to 12 and consistent classification of diseases by methods of linear discriminant analysis. Developed classification rules allow to diagnose diseases on the newly obtained samples of blood serum and/or CSG. Since the LST and GCF serum gives similar results to the classification based on ‘Disease’, for diagnostic purposes you can do only the CSG analysis without blood sampling, however, the LST for the serum and the CSG allows you to increase the power classification on the basis of ‘Disease’ compared to the analysis of these same data separately.