Raman Spectroscopy of Blood and Blood Components

Atkins, Chad G.; Buckley, Kevin; Blades, Michael W.; Turner, Robin F. B.

doi:10.1177/0003702816686593

Cited by 219 publications

(179 citation statements)

References 241 publications

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“…Figure 8 shows the most informative spectral bands when constructing a logistic regression for distinguishing blood samples with lung cancer from blood samples with other tumors. [3,6,[25][26][27] The detected spectral bands provide information on changes in the blood component composition associated with lung cancer progression.…”

Section: Simca Analysismentioning

confidence: 99%

“…At present, in clinical experimental studies, Raman spectroscopy (RS) is applied to determine the features of the body fluids component composition, specifically, to detect various pathological diseases. RS provides information on the component composition of a tested object at the molecular level and evaluates body homeostasis by analyzing spectral features of body fluids . For instance, RS experimental studies of body fluids have demonstrated a potential to detect Alzheimer's disease, prostate cancer, oral cancer, and diabetes …”

Section: Introductionmentioning

confidence: 99%

“…RS provides information on the component composition of a tested object at the molecular level and evaluates body homeostasis by analyzing spectral features of body fluids. [3] For instance, RS experimental studies of body fluids have demonstrated a potential to detect Alzheimer's disease, [4] prostate cancer, [5] oral cancer, [6,7] and diabetes. [8] In the previous paper, [9] our research team presented the experimental study of quantitative determination of protein fractions in blood of patients with hematological diseases using the Raman setup with a cost-effective probe.…”

Section: Introductionmentioning

confidence: 99%

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Comparative study of multivariative analysis methods of blood Raman spectra classification

Bratchenko

Lykina

et al. 2019

J Raman Spectroscopy

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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.

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Section: Simca Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative study of multivariative analysis methods of blood Raman spectra classification

Bratchenko

Lykina

et al. 2019

J Raman Spectroscopy

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“…Analysis of the plasma and whole blood spectra allows for detection the differences in the proteins component composition. (vibrations of pyrrole), 1120 сm -1 (deformation vibrations of С-N group), 1227 сm -1 (deformation vibrations of СН group), 1550 сm -1 (vibrations of phenylalanine) [13]. Each processed spectrum was a subject to the multivariate analysis for the construction of regression model.…”

Section: Raw Spectra Processingmentioning

confidence: 99%

Raman spectra analysis of human blood protein fractions using the projection on latent structures method

Lykina¹,

Artemyev²,

Bratchenko³

et al. 2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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This work is devoted to the study of human blood protein fractions by Raman spectroscopy. Whole blood and blood plasma were used as the tested samples. For the pure Raman spectra analysis the autofluorescence background was subtracted by using of two mathematical approaches: polynomial approximation and baseline correction with asymmetric least squares. The study allowed for revealing the differences between the spectral features of blood plasma and whole blood plasma, which are changes in the relative Raman intensities of plasma and whole blood and appearance Raman bands 670 cm -1 , 750 cm -1 , 1120 cm -1 and 1550 cm -1 correspond to hemoglobin bonds in whole blood. The spectral features were used for total protein concentration measurement of plasma and whole blood. PLS regression method was utilized for spectral data analysis with different protein concentrations. The VIP-scores make it possible to determine the most informative spectral bands: 1002 cm -1 , 1227 cm -1 , 1400 cm -1 , 1630 cm -1 for proteins analysis.

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“…The large number of review articles that are frequently published in these areas is evidence of this. Recent examples have focused on applications of optical diagnostics to gas phase environments [1][2][3][4][5], liquids [1,4,6,7], and multiphase systems [7][8][9][10]. A key feature of such light-based methods is that they are usually non-intrusive, and hence they do not notably affect the system under investigation.…”

Section: Introductionmentioning

confidence: 99%