Comparative study of multivariative analysis methods of blood Raman spectra classification

Abstract

The pathological state of a human body leads to altered biochemical composition of body fluids. Conventional biochemical analysis of body fluids is notable for its low‐informative value in localizing a particular pathology. As an alternative, Raman spectroscopy provides detailed evaluation of blood characteristics at the molecular level. Raman blood spectra are characterized by multicollinearity as well as by the presence of autofluorescence background and noises of different nature. Choice of a proper method for experimental data processing of blood spectra is crucial for obtaining statistically reliable information regarding a pathological process in the body. This study examines different approaches to multidimensional analysis of the various‐size Raman spectral dataset of human blood samples by a cost‐effective Raman setup in a clinical setting. To discriminate blood samples by the pathology type, statistical processing of experimental data is performed by factor analysis, logistic regression, discriminant analysis, classification tree, projection to latent structures discriminant analysis (PLS‐DA), and soft independent modeling of class analogies. Comparative analysis of the discussed multivariate methods demonstrates that the PLS‐DA method (sensitivity 0.75, specificity 0.81, and accuracy 0.76) proved to be the most effective for the classification of blood samples by cancer localization. In terms of classification for the presence of hyperproteinemia, the most efficient are the logistic regression method (sensitivity 0.89, specificity 0.99, and accuracy 0.97) and the discriminant analysis method (sensitivity 0.83, specificity 1.0, and accuracy 0.97). In general, the selected multivariate methods could serve as a reliable tool for analyzing spectral characteristics of body fluids.

Characterization of Polarization Holographic Gratings Obtained on Azopolymer Thin Films by Digital Holographic Microscopy

“…These materials can be useful in biomedical research. Along with the well-known methods of visualization of color, shape, and spectral properties using hyperspectral imaging microscopy [18] or Raman spectroscopy of light reflected from a micropreparation [19], the method of digital holographic microscopy based on phase reconstruction can be extremely useful in morphological studies of microobjects.…”

The Effect of Noise in Raman Spectra on the Reconstruction of the Concentration of Amino Acids in the Mixture by Multivariate Curve Resolution (MCR) Analysis

“…The experimental Raman spectra used in our study are recorded using a portable spectroscopic setup which includes a thermally stabilized LML-785.0RB-04 laser diode module as an excitation source (785 ± 0.1 nm central wavelength, 200 mW laser power) and a QE 65 Pro spectrometer (OceanOptics, Inc., USA) with CCD detector operating at -15 °C [15].…”

“…To simulate different noise levels, we evaluated an additive noise level from a signal-to-noise ratio (SNR) metric proposed in [15]: (3) where is the Raman signal level (intensity of Raman peak in 980-1025 cm -1 band); is the noise standard deviation.…”

Исследование Сыворотки Крови У Крыс С Трансплантированной Холангиокарциномой С Использованием Спектроскопии Комбинационного Рассеяния Света

“…Различные компоненты крови оказывают значительное влияние на спектры КРС, причем их вклад может изменяться при развитии патологических состояний [26,27]. В диапазоне 500−1800 cm −1 находится большое количество линий, индивидуальных для каждого органического соединения [28,29]. Это в основном область валентных и деформационных колебаний.…”

“…Это в основном область валентных и деформационных колебаний. По наличию или отсутствию конкретных линий в спектре в этом диапазоне можно определить как само исследуемое вещество, так и его концентрацию в биологическом образце [28,30].…”