Multivariate Curve Resolution of Wavelet and Fourier Compressed Spectra
The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra. The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompresse...
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| Veröffentlicht in: | Analytical chemistry (Washington) 2001-07, Vol.73 (14), p.3247-3256 |
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| creator | Harrington, Peter de B Rauch, Paul J Cai, Chunsheng |
| description | The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra. The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPLISMA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium γ-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and concentration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean-square errors of reconstruction |
| doi_str_mv | 10.1021/ac000956s |
| format | Article |
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The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPLISMA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium γ-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and concentration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean-square errors of reconstruction <3%. The daublet wavelet filters were selected, because they worked well when compared to coiflet and symmlet. The effects of the daublet filter width and compression ratio were evaluated with respect to reconstruction errors of the data sets and SIMPLISMA spectra. For these experiments, the daublet 14 filter performed well for the two data sets.</description><identifier>ISSN: 0003-2700</identifier><identifier>EISSN: 1520-6882</identifier><identifier>DOI: 10.1021/ac000956s</identifier><identifier>PMID: 11476222</identifier><identifier>CODEN: ANCHAM</identifier><language>eng</language><publisher>Washington, DC: American Chemical Society</publisher><subject>Analytical chemistry ; Chemistry ; Exact sciences and technology ; Ions ; Sensors ; Spectrometric and optical methods ; Spectrum analysis</subject><ispartof>Analytical chemistry (Washington), 2001-07, Vol.73 (14), p.3247-3256</ispartof><rights>Copyright © 2001 American Chemical Society</rights><rights>2001 INIST-CNRS</rights><rights>Copyright American Chemical Society Jul 15, 2001</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a405t-fceceef343c381debc10ca47c82be1d01597572cccbd7426466a96c5715380ff3</citedby><cites>FETCH-LOGICAL-a405t-fceceef343c381debc10ca47c82be1d01597572cccbd7426466a96c5715380ff3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/ac000956s$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/ac000956s$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,780,784,2765,27076,27924,27925,56738,56788</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1065501$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11476222$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Harrington, Peter de B</creatorcontrib><creatorcontrib>Rauch, Paul J</creatorcontrib><creatorcontrib>Cai, Chunsheng</creatorcontrib><title>Multivariate Curve Resolution of Wavelet and Fourier Compressed Spectra</title><title>Analytical chemistry (Washington)</title><addtitle>Anal. Chem</addtitle><description>The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra. The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPLISMA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium γ-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and concentration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean-square errors of reconstruction <3%. The daublet wavelet filters were selected, because they worked well when compared to coiflet and symmlet. The effects of the daublet filter width and compression ratio were evaluated with respect to reconstruction errors of the data sets and SIMPLISMA spectra. For these experiments, the daublet 14 filter performed well for the two data sets.</description><subject>Analytical chemistry</subject><subject>Chemistry</subject><subject>Exact sciences and technology</subject><subject>Ions</subject><subject>Sensors</subject><subject>Spectrometric and optical methods</subject><subject>Spectrum analysis</subject><issn>0003-2700</issn><issn>1520-6882</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNpl0FFv0zAUBWALMbEyeOAPoAgBEg-Be53YTh9RxTakTgxW4NG6vbmRMtKks5MK_j1GrbaJPVmyPx0dH6VeILxH0PiBGADmxsZHaoZGQ26rSj9Ws3Rb5NoBHKunMV4DIALaJ-oYsXRWaz1TZxdTN7Y7Ci2Nki2msJPsm8Shm8Z26LOhyX7STjoZM-rr7HSYQishWwybbZAYpc6utsJjoGfqqKEuyvPDeaK-n35aLc7z5Zezz4uPy5xKMGPesLBIU5QFFxXWsmYEptJxpdeCNaCZO-M0M69rV2pbWktzy8ahKSpomuJEvd3nbsNwM0kc_aaNLF1HvQxT9A6hNFCZBF_9B69T-T518xpdZbVFm9C7PeIwxBik8dvQbij88Qj-37T-dtpkXx4Cp_VG6jt52DKB1wdAkalrAvXcxnuJ1hjAxPI9a-Mov2-fKfzy1hXO-NXllT9fua_u8gf6ZfJv9p443v3hYb-_j3yatQ</recordid><startdate>20010715</startdate><enddate>20010715</enddate><creator>Harrington, Peter de B</creator><creator>Rauch, Paul J</creator><creator>Cai, Chunsheng</creator><general>American Chemical Society</general><scope>BSCLL</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U7</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</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></search><sort><creationdate>20010715</creationdate><title>Multivariate Curve Resolution of Wavelet and Fourier Compressed Spectra</title><author>Harrington, Peter de B ; 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Chem</addtitle><date>2001-07-15</date><risdate>2001</risdate><volume>73</volume><issue>14</issue><spage>3247</spage><epage>3256</epage><pages>3247-3256</pages><issn>0003-2700</issn><eissn>1520-6882</eissn><coden>ANCHAM</coden><abstract>The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra. The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPLISMA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium γ-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and concentration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean-square errors of reconstruction <3%. The daublet wavelet filters were selected, because they worked well when compared to coiflet and symmlet. The effects of the daublet filter width and compression ratio were evaluated with respect to reconstruction errors of the data sets and SIMPLISMA spectra. 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| subjects | Analytical chemistry Chemistry Exact sciences and technology Ions Sensors Spectrometric and optical methods Spectrum analysis |
| title | Multivariate Curve Resolution of Wavelet and Fourier Compressed Spectra |
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