Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.
Identifieur interne : 001189 ( PubMed/Checkpoint ); précédent : 001188; suivant : 001190Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.
Auteurs : A G Quentin [Australie] ; T. Rodemann [Australie] ; M-F Doutreleau [France] ; M. Moreau [France] ; N W Davies [Australie] ; Peter MillardSource :
- Tree physiology [ 1758-4469 ] ; 2017.
Abstract
Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.
DOI: 10.1093/treephys/tpw083
PubMed: 28173560
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The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">28173560</PMID><DateCreated><Year>2017</Year><Month>02</Month><Day>07</Day></DateCreated><DateRevised><Year>2017</Year><Month>10</Month><Day>16</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1758-4469</ISSN><JournalIssue CitedMedium="Internet"><Volume>37</Volume><Issue>1</Issue><PubDate><Year>2017</Year><Month>01</Month><Day>31</Day></PubDate></JournalIssue><Title>Tree physiology</Title><ISOAbbreviation>Tree Physiol.</ISOAbbreviation></Journal><ArticleTitle>Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.</ArticleTitle><Pagination><MedlinePgn>131-141</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/treephys/tpw083</ELocationID><Abstract><AbstractText>Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Quentin</LastName><ForeName>A G</ForeName><Initials>AG</Initials><AffiliationInfo><Affiliation>CSIRO Land and Water, Private Bag 12, Hobart, Tasmania, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rodemann</LastName><ForeName>T</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Central Science Laboratory, University of Tasmania, Private Bag 74, Hobart, Tasmania , Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Doutreleau</LastName><ForeName>M-F</ForeName><Initials>MF</Initials><AffiliationInfo><Affiliation>Agri'Terr Unit, Esitpa, 3 rue du Tronquet, Mont-Saint-Aignan, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Moreau</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Agri'Terr Unit, Esitpa, 3 rue du Tronquet, Mont-Saint-Aignan, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Davies</LastName><ForeName>N W</ForeName><Initials>NW</Initials><AffiliationInfo><Affiliation>Central Science Laboratory, University of Tasmania, Private Bag 74, Hobart, Tasmania , Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Millard</LastName><ForeName>Peter</ForeName><Initials>P</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>Canada</Country><MedlineTA>Tree Physiol</MedlineTA><NlmUniqueID>100955338</NlmUniqueID><ISSNLinking>0829-318X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">calibration model performance</Keyword><Keyword MajorTopicYN="Y">high-throughput phenotyping tool</Keyword><Keyword MajorTopicYN="Y">partial least squares regression</Keyword><Keyword MajorTopicYN="Y">rapid and cost-effective</Keyword><Keyword MajorTopicYN="Y">soluble sugars</Keyword><Keyword MajorTopicYN="Y">starch</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>11</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2016</Year><Month>05</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>07</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>2</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>2</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>2</Month><Day>9</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28173560</ArticleId><ArticleId IdType="pii">2527786</ArticleId><ArticleId IdType="doi">10.1093/treephys/tpw083</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>France</li></country></list><tree><noCountry><name sortKey="Millard, Peter" sort="Millard, Peter" uniqKey="Millard P" first="Peter" last="Millard">Peter Millard</name></noCountry><country name="Australie"><noRegion><name sortKey="Quentin, A G" sort="Quentin, A G" uniqKey="Quentin A" first="A G" last="Quentin">A G Quentin</name></noRegion><name sortKey="Davies, N W" sort="Davies, N W" uniqKey="Davies N" first="N W" last="Davies">N W Davies</name><name sortKey="Rodemann, T" sort="Rodemann, T" uniqKey="Rodemann T" first="T" last="Rodemann">T. Rodemann</name></country><country name="France"><noRegion><name sortKey="Doutreleau, M F" sort="Doutreleau, M F" uniqKey="Doutreleau M" first="M-F" last="Doutreleau">M-F Doutreleau</name></noRegion><name sortKey="Moreau, M" sort="Moreau, M" uniqKey="Moreau M" first="M" last="Moreau">M. Moreau</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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