Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm
Identifieur interne : 001707 ( Istex/Corpus ); précédent : 001706; suivant : 001708Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm
Auteurs : Michael Vohland ; Kerstin Michel ; Bernard LudwigSource :
- Journal of Plant Nutrition and Soil Science [ 1436-8730 ] ; 2011-10.
English descriptors
- KwdEn :
Abstract
Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.
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DOI: 10.1002/jpln.201000226
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ISTEX:A57092B3D2E142EFAF62E095A8AC0B259D7DCA44Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm</title><author><name sortKey="Vohland, Michael" sort="Vohland, Michael" uniqKey="Vohland M" first="Michael" last="Vohland">Michael Vohland</name><affiliation><mods:affiliation>Remote Sensing Department, University of Trier, Campus II, 54286 Trier, Germany</mods:affiliation></affiliation></author><author><name sortKey="Michel, Kerstin" sort="Michel, Kerstin" uniqKey="Michel K" first="Kerstin" last="Michel">Kerstin Michel</name><affiliation><mods:affiliation>Unit of Soil Biology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Seckendorff‐Gudent‐Weg 8, 1131 Wien, Austria</mods:affiliation></affiliation></author><author><name sortKey="Ludwig, Bernard" sort="Ludwig, Bernard" uniqKey="Ludwig B" first="Bernard" last="Ludwig">Bernard Ludwig</name><affiliation><mods:affiliation>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:A57092B3D2E142EFAF62E095A8AC0B259D7DCA44</idno><date when="2011" year="2011">2011</date><idno type="doi">10.1002/jpln.201000226</idno><idno type="url">https://api.istex.fr/document/A57092B3D2E142EFAF62E095A8AC0B259D7DCA44/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001707</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001707</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm</title><author><name sortKey="Vohland, Michael" sort="Vohland, Michael" uniqKey="Vohland M" first="Michael" last="Vohland">Michael Vohland</name><affiliation><mods:affiliation>Remote Sensing Department, University of Trier, Campus II, 54286 Trier, Germany</mods:affiliation></affiliation></author><author><name sortKey="Michel, Kerstin" sort="Michel, Kerstin" uniqKey="Michel K" first="Kerstin" last="Michel">Kerstin Michel</name><affiliation><mods:affiliation>Unit of Soil Biology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Seckendorff‐Gudent‐Weg 8, 1131 Wien, Austria</mods:affiliation></affiliation></author><author><name sortKey="Ludwig, Bernard" sort="Ludwig, Bernard" uniqKey="Ludwig B" first="Bernard" last="Ludwig">Bernard Ludwig</name><affiliation><mods:affiliation>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Plant Nutrition and Soil Science</title><title level="j" type="abbrev">Z. Pflanzenernähr. Bodenk.</title><idno type="ISSN">1436-8730</idno><idno type="eISSN">1522-2624</idno><imprint><publisher>WILEY‐VCH Verlag</publisher><pubPlace>Weinheim</pubPlace><date type="published" when="2011-10">2011-10</date><biblScope unit="volume">174</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="695">695</biblScope><biblScope unit="page" to="701">701</biblScope></imprint><idno type="ISSN">1436-8730</idno></series><idno type="istex">A57092B3D2E142EFAF62E095A8AC0B259D7DCA44</idno><idno type="DOI">10.1002/jpln.201000226</idno><idno type="ArticleID">JPLN201000226</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1436-8730</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>NIR spectroscopy</term><term>char</term><term>genetic algorithm</term><term>partial least squares regression</term><term>soil organic carbon</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Michael Vohland</name><affiliations><json:string>Remote Sensing Department, University of Trier, Campus II, 54286 Trier, Germany</json:string></affiliations></json:item><json:item><name>Kerstin Michel</name><affiliations><json:string>Unit of Soil Biology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Seckendorff‐Gudent‐Weg 8, 1131 Wien, Austria</json:string></affiliations></json:item><json:item><name>Bernard Ludwig</name><affiliations><json:string>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</json:string><json:string>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>char</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>genetic algorithm</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>NIR spectroscopy</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>partial least squares regression</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>soil organic carbon</value></json:item></subject><articleId><json:string>JPLN201000226</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. 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Varmuza</name></json:item></author><host><volume>446</volume><pages><last>494</last><first>485</first></pages><author></author><title>Analytica Chimica Acta</title></host><title>Feature selection by genetic algorithms for mass spectral classifiers.</title></json:item></refBibs><genre><json:string>article</json:string></genre><host><volume>174</volume><publisherId><json:string>JPLN</json:string></publisherId><pages><total>7</total><last>701</last><first>695</first></pages><issn><json:string>1436-8730</json:string></issn><issue>5</issue><subject><json:item><value>Regular Article</value></json:item></subject><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1522-2624</json:string></eissn><title>Journal of Plant Nutrition and Soil Science</title><doi><json:string>10.1002/(ISSN)1522-2624</json:string></doi></host><categories><wos><json:string>science</json:string><json:string>soil science</json:string><json:string>plant sciences</json:string><json:string>agronomy</json:string></wos><scienceMetrix><json:string>applied sciences</json:string><json:string>agriculture, fisheries & forestry</json:string><json:string>agronomy & agriculture</json:string></scienceMetrix></categories><publicationDate>2011</publicationDate><copyrightDate>2011</copyrightDate><doi><json:string>10.1002/jpln.201000226</json:string></doi><id>A57092B3D2E142EFAF62E095A8AC0B259D7DCA44</id><score>0.7910118</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/A57092B3D2E142EFAF62E095A8AC0B259D7DCA44/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/A57092B3D2E142EFAF62E095A8AC0B259D7DCA44/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/A57092B3D2E142EFAF62E095A8AC0B259D7DCA44/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>WILEY‐VCH Verlag</publisher><pubPlace>Weinheim</pubPlace><availability><p>Copyright © 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim</p></availability><date>2011</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Use of near‐infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest‐floor material in soils: usefulness of a genetic algorithm</title><author xml:id="author-1"><persName><forename type="first">Michael</forename><surname>Vohland</surname></persName><affiliation>Remote Sensing Department, University of Trier, Campus II, 54286 Trier, Germany</affiliation></author><author xml:id="author-2"><persName><forename type="first">Kerstin</forename><surname>Michel</surname></persName><affiliation>Unit of Soil Biology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Seckendorff‐Gudent‐Weg 8, 1131 Wien, Austria</affiliation></author><author xml:id="author-3"><persName><forename type="first">Bernard</forename><surname>Ludwig</surname></persName><affiliation>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</affiliation><affiliation>Department of Environmental Chemistry, University of Kassel, Nordbahnhofstraße 1a, 37213 Witzenhausen, Germany</affiliation></author></analytic><monogr><title level="j">Journal of Plant Nutrition and Soil Science</title><title level="j" type="abbrev">Z. Pflanzenernähr. Bodenk.</title><idno type="pISSN">1436-8730</idno><idno type="eISSN">1522-2624</idno><idno type="DOI">10.1002/(ISSN)1522-2624</idno><imprint><publisher>WILEY‐VCH Verlag</publisher><pubPlace>Weinheim</pubPlace><date type="published" when="2011-10"></date><biblScope unit="volume">174</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="695">695</biblScope><biblScope unit="page" to="701">701</biblScope></imprint></monogr><idno type="istex">A57092B3D2E142EFAF62E095A8AC0B259D7DCA44</idno><idno type="DOI">10.1002/jpln.201000226</idno><idno type="ArticleID">JPLN201000226</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Several algorithms exist for the calibration procedures of near‐infrared spectra in soil‐scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. 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Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. 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Objectives were (1) to test the usefulness of near‐infrared spectroscopy (NIRS) for a prediction of C and N from char and forest‐floor Oa material in soils using either a partial least squares (PLS) method or a GA‐PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near‐IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C‐free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest‐floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA‐PLS approach was superior over the full‐spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: “approximate quantitative” instead of a “distinction between high and low” for C derived from mixed coal and “good” instead of “approximate quantitative” for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.</abstract><subject lang="en"><genre>keywords</genre><topic>char</topic><topic>genetic algorithm</topic><topic>NIR spectroscopy</topic><topic>partial least squares regression</topic><topic>soil organic carbon</topic></subject><relatedItem type="host"><titleInfo><title>Journal of Plant Nutrition and Soil Science</title></titleInfo><titleInfo type="abbreviated"><title>Z. Pflanzenernähr. Bodenk.</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Regular Article</topic></subject><identifier type="ISSN">1436-8730</identifier><identifier type="eISSN">1522-2624</identifier><identifier type="DOI">10.1002/(ISSN)1522-2624</identifier><identifier type="PublisherID">JPLN</identifier><part><date>2011</date><detail type="volume"><caption>vol.</caption><number>174</number></detail><detail type="issue"><caption>no.</caption><number>5</number></detail><extent unit="pages"><start>695</start><end>701</end><total>7</total></extent></part></relatedItem><identifier type="istex">A57092B3D2E142EFAF62E095A8AC0B259D7DCA44</identifier><identifier type="DOI">10.1002/jpln.201000226</identifier><identifier type="ArticleID">JPLN201000226</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2011 WILEY‐VCH Verlag GmbH & Co. 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