Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis

Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each havi...

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Veröffentlicht in:Analytica chimica acta 2019-03, Vol.1050, p.32-43
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description Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues,
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However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and β-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively. [Display omitted] •Three new chemometric methods for automatic hyperspectral image processing are proposed.•The methods define a framework for quantitative analysis of Raman data in three steps.•These steps include denoising, background removal and hyperspectral image unmixing.•The Q-HIU method requires only two parameters for the analysis.•Q-HIU allows to quantify unknown biochemical composition of analytes from Raman data.</description><identifier>ISSN: 0003-2670</identifier><identifier>EISSN: 1873-4324</identifier><identifier>DOI: 10.1016/j.aca.2018.11.018</identifier><identifier>PMID: 30661589</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Algorithms ; Aorta ; Arteriosclerosis ; Atherosclerosis ; Background noise ; Baseline correction ; Biochemical composition ; Biochemical quantification ; Biochemistry ; Biomedical materials ; Calcium ; Carotene ; Cholesterol ; Computer applications ; Computing time ; Constituents ; Crystals ; Data analysis ; Data processing ; Fluid filters ; Fluorescence ; Hydroxyapatite ; Hyperspectral image analysis ; Hyperspectral imaging ; Lesions ; Lipids ; Multivariate curve resolution ; Noise ; Noise intensity ; Non-negative matrix factorization ; Organic chemistry ; Raman spectroscopy ; Singular value decomposition ; Spectra ; Spectroscopic analysis ; Spectroscopy ; Spectrum analysis ; Subtraction ; Tissues ; β-Carotene</subject><ispartof>Analytica chimica acta, 2019-03, Vol.1050, p.32-43</ispartof><rights>2018 Elsevier B.V.</rights><rights>Copyright © 2018 Elsevier B.V. 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However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and β-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively. [Display omitted] •Three new chemometric methods for automatic hyperspectral image processing are proposed.•The methods define a framework for quantitative analysis of Raman data in three steps.•These steps include denoising, background removal and hyperspectral image unmixing.•The Q-HIU method requires only two parameters for the analysis.•Q-HIU allows to quantify unknown biochemical composition of analytes from Raman data.</description><subject>Algorithms</subject><subject>Aorta</subject><subject>Arteriosclerosis</subject><subject>Atherosclerosis</subject><subject>Background noise</subject><subject>Baseline correction</subject><subject>Biochemical composition</subject><subject>Biochemical quantification</subject><subject>Biochemistry</subject><subject>Biomedical materials</subject><subject>Calcium</subject><subject>Carotene</subject><subject>Cholesterol</subject><subject>Computer applications</subject><subject>Computing time</subject><subject>Constituents</subject><subject>Crystals</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Fluid filters</subject><subject>Fluorescence</subject><subject>Hydroxyapatite</subject><subject>Hyperspectral image analysis</subject><subject>Hyperspectral imaging</subject><subject>Lesions</subject><subject>Lipids</subject><subject>Multivariate curve resolution</subject><subject>Noise</subject><subject>Noise intensity</subject><subject>Non-negative matrix factorization</subject><subject>Organic chemistry</subject><subject>Raman spectroscopy</subject><subject>Singular value decomposition</subject><subject>Spectra</subject><subject>Spectroscopic analysis</subject><subject>Spectroscopy</subject><subject>Spectrum analysis</subject><subject>Subtraction</subject><subject>Tissues</subject><subject>β-Carotene</subject><issn>0003-2670</issn><issn>1873-4324</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kUuLFDEUhYMoTjv6A9xIwI2bKnOTdFUKVzKMDxgQRNfhdh49aaoq1UlqsP-9aXp04cLV4cJ3Dsk5hLwG1gKD7v2hRYMtZ6BagLbKE7IB1YtGCi6fkg1jTDS869kVeZHzoZ4cmHxOrgTrOtiqYUOOt94HE9xc6HHFuYSCJTw4en9aXMqLMyXhSMOEe0fXeQq_wrynkyv30VIfEx0x7V2TDY6OfscJZzoFk2JzscZs4nKiFgtSnHE85ZBfkmcex-xePeo1-fnp9sfNl-bu2-evNx_vGiO5LA16VIxZ4QeurNyhl4MwFj1XyAHU1vset0bZfofGDsKi4dgrBiBsJ6S34pq8u-QuKR5Xl4ueQjZuHHF2cc2aQz9I1omhr-jbf9BDXFN975kaBGw5l7JScKHq_3JOzusl1WLSSQPT5z30Qdc99HkPDaCrVM-bx-R1Nzn71_FngAp8uACuVvEQXNL5vIZxNqTaoLYx_Cf-N0InnWg</recordid><startdate>20190307</startdate><enddate>20190307</enddate><creator>Lobanova, E.G.</creator><creator>Lobanov, S.V.</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7TM</scope><scope>7U5</scope><scope>7U7</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-3126-1903</orcidid></search><sort><creationdate>20190307</creationdate><title>Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis</title><author>Lobanova, E.G. ; 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However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. 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source ScienceDirect Journals (5 years ago - present)
subjects Algorithms
Aorta
Arteriosclerosis
Atherosclerosis
Background noise
Baseline correction
Biochemical composition
Biochemical quantification
Biochemistry
Biomedical materials
Calcium
Carotene
Cholesterol
Computer applications
Computing time
Constituents
Crystals
Data analysis
Data processing
Fluid filters
Fluorescence
Hydroxyapatite
Hyperspectral image analysis
Hyperspectral imaging
Lesions
Lipids
Multivariate curve resolution
Noise
Noise intensity
Non-negative matrix factorization
Organic chemistry
Raman spectroscopy
Singular value decomposition
Spectra
Spectroscopic analysis
Spectroscopy
Spectrum analysis
Subtraction
Tissues
β-Carotene
title Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis
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