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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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, |
doi_str_mv | 10.1016/j.aca.2018.11.018 |
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[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. All rights reserved.</rights><rights>Copyright Elsevier BV Mar 7, 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c424t-afa800d3f928d4baf493cdaf28a21185ff7a5c8d7bacd93dac2a780113d634fd3</citedby><cites>FETCH-LOGICAL-c424t-afa800d3f928d4baf493cdaf28a21185ff7a5c8d7bacd93dac2a780113d634fd3</cites><orcidid>0000-0002-3126-1903</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.aca.2018.11.018$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30661589$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lobanova, E.G.</creatorcontrib><creatorcontrib>Lobanov, S.V.</creatorcontrib><title>Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis</title><title>Analytica chimica acta</title><addtitle>Anal Chim Acta</addtitle><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, 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. ; Lobanov, S.V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-afa800d3f928d4baf493cdaf28a21185ff7a5c8d7bacd93dac2a780113d634fd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Aorta</topic><topic>Arteriosclerosis</topic><topic>Atherosclerosis</topic><topic>Background noise</topic><topic>Baseline correction</topic><topic>Biochemical composition</topic><topic>Biochemical quantification</topic><topic>Biochemistry</topic><topic>Biomedical materials</topic><topic>Calcium</topic><topic>Carotene</topic><topic>Cholesterol</topic><topic>Computer applications</topic><topic>Computing time</topic><topic>Constituents</topic><topic>Crystals</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>Fluid filters</topic><topic>Fluorescence</topic><topic>Hydroxyapatite</topic><topic>Hyperspectral image analysis</topic><topic>Hyperspectral imaging</topic><topic>Lesions</topic><topic>Lipids</topic><topic>Multivariate curve resolution</topic><topic>Noise</topic><topic>Noise intensity</topic><topic>Non-negative matrix factorization</topic><topic>Organic chemistry</topic><topic>Raman spectroscopy</topic><topic>Singular value decomposition</topic><topic>Spectra</topic><topic>Spectroscopic analysis</topic><topic>Spectroscopy</topic><topic>Spectrum analysis</topic><topic>Subtraction</topic><topic>Tissues</topic><topic>β-Carotene</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lobanova, E.G.</creatorcontrib><creatorcontrib>Lobanov, S.V.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Analytica chimica acta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lobanova, E.G.</au><au>Lobanov, S.V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis</atitle><jtitle>Analytica chimica acta</jtitle><addtitle>Anal Chim Acta</addtitle><date>2019-03-07</date><risdate>2019</risdate><volume>1050</volume><spage>32</spage><epage>43</epage><pages>32-43</pages><issn>0003-2670</issn><eissn>1873-4324</eissn><abstract>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, 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.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>30661589</pmid><doi>10.1016/j.aca.2018.11.018</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-3126-1903</orcidid><oa>free_for_read</oa></addata></record> |
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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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