Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy
Identifieur interne : 000E24 ( Pmc/Checkpoint ); précédent : 000E23; suivant : 000E25Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy
Auteurs : Valentina Ulissi ; Francesca Antonucci ; Paolo Benincasa ; Michela Farneselli ; Giacomo Tosti ; Marcello Guiducci ; Francesco Tei ; Corrado Costa ; Federico Pallottino ; Luigi Pari ; Paolo MenesattiSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2011.
Abstract
Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO3 concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO3 concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (
Url:
DOI: 10.3390/s110606411
PubMed: 22163962
PubMed Central: 3231448
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy</title><author><name sortKey="Ulissi, Valentina" sort="Ulissi, Valentina" uniqKey="Ulissi V" first="Valentina" last="Ulissi">Valentina Ulissi</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Antonucci, Francesca" sort="Antonucci, Francesca" uniqKey="Antonucci F" first="Francesca" last="Antonucci">Francesca Antonucci</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Benincasa, Paolo" sort="Benincasa, Paolo" uniqKey="Benincasa P" first="Paolo" last="Benincasa">Paolo Benincasa</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Farneselli, Michela" sort="Farneselli, Michela" uniqKey="Farneselli M" first="Michela" last="Farneselli">Michela Farneselli</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Tosti, Giacomo" sort="Tosti, Giacomo" uniqKey="Tosti G" first="Giacomo" last="Tosti">Giacomo Tosti</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Guiducci, Marcello" sort="Guiducci, Marcello" uniqKey="Guiducci M" first="Marcello" last="Guiducci">Marcello Guiducci</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Tei, Francesco" sort="Tei, Francesco" uniqKey="Tei F" first="Francesco" last="Tei">Francesco Tei</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Costa, Corrado" sort="Costa, Corrado" uniqKey="Costa C" first="Corrado" last="Costa">Corrado Costa</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Pallottino, Federico" sort="Pallottino, Federico" uniqKey="Pallottino F" first="Federico" last="Pallottino">Federico Pallottino</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Pari, Luigi" sort="Pari, Luigi" uniqKey="Pari L" first="Luigi" last="Pari">Luigi Pari</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Menesatti, Paolo" sort="Menesatti, Paolo" uniqKey="Menesatti P" first="Paolo" last="Menesatti">Paolo Menesatti</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22163962</idno><idno type="pmc">3231448</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231448</idno><idno type="RBID">PMC:3231448</idno><idno type="doi">10.3390/s110606411</idno><date when="2011">2011</date><idno type="wicri:Area/Pmc/Corpus">001295</idno><idno type="wicri:Area/Pmc/Curation">001293</idno><idno type="wicri:Area/Pmc/Checkpoint">000E24</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy</title><author><name sortKey="Ulissi, Valentina" sort="Ulissi, Valentina" uniqKey="Ulissi V" first="Valentina" last="Ulissi">Valentina Ulissi</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Antonucci, Francesca" sort="Antonucci, Francesca" uniqKey="Antonucci F" first="Francesca" last="Antonucci">Francesca Antonucci</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Benincasa, Paolo" sort="Benincasa, Paolo" uniqKey="Benincasa P" first="Paolo" last="Benincasa">Paolo Benincasa</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Farneselli, Michela" sort="Farneselli, Michela" uniqKey="Farneselli M" first="Michela" last="Farneselli">Michela Farneselli</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Tosti, Giacomo" sort="Tosti, Giacomo" uniqKey="Tosti G" first="Giacomo" last="Tosti">Giacomo Tosti</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Guiducci, Marcello" sort="Guiducci, Marcello" uniqKey="Guiducci M" first="Marcello" last="Guiducci">Marcello Guiducci</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Tei, Francesco" sort="Tei, Francesco" uniqKey="Tei F" first="Francesco" last="Tei">Francesco Tei</name><affiliation><nlm:aff id="af1-sensors-11-06411"> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</nlm:aff></affiliation></author><author><name sortKey="Costa, Corrado" sort="Costa, Corrado" uniqKey="Costa C" first="Corrado" last="Costa">Corrado Costa</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Pallottino, Federico" sort="Pallottino, Federico" uniqKey="Pallottino F" first="Federico" last="Pallottino">Federico Pallottino</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Pari, Luigi" sort="Pari, Luigi" uniqKey="Pari L" first="Luigi" last="Pari">Luigi Pari</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author><author><name sortKey="Menesatti, Paolo" sort="Menesatti, Paolo" uniqKey="Menesatti P" first="Paolo" last="Menesatti">Paolo Menesatti</name><affiliation><nlm:aff id="af2-sensors-11-06411"> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</nlm:aff></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO<sub>3</sub> concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO<sub>3</sub> concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (<italic>r</italic> = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Agostini, F" uniqKey="Agostini F">F Agostini</name></author><author><name sortKey="Tei, F" uniqKey="Tei F">F Tei</name></author><author><name sortKey="Silgram, M" uniqKey="Silgram M">M Silgram</name></author><author><name sortKey="Farneselli, M" uniqKey="Farneselli M">M Farneselli</name></author><author><name sortKey="Benincasa, P" uniqKey="Benincasa P">P Benincasa</name></author><author><name sortKey="Aller, Mf" uniqKey="Aller M">MF Aller</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Farneselli, M" uniqKey="Farneselli M">M Farneselli</name></author><author><name sortKey="Benincasa, P" uniqKey="Benincasa P">P Benincasa</name></author><author><name sortKey="Tei, F" uniqKey="Tei F">F 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id><journal-title-group><journal-title>Sensors (Basel, Switzerland)</journal-title></journal-title-group><issn pub-type="epub">1424-8220</issn><publisher><publisher-name>Molecular Diversity Preservation International (MDPI)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22163962</article-id><article-id pub-id-type="pmc">3231448</article-id><article-id pub-id-type="doi">10.3390/s110606411</article-id><article-id pub-id-type="publisher-id">sensors-11-06411</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Ulissi</surname><given-names>Valentina</given-names></name><xref ref-type="aff" rid="af1-sensors-11-06411"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Antonucci</surname><given-names>Francesca</given-names></name><xref ref-type="aff" rid="af2-sensors-11-06411"><sup>2</sup></xref><xref ref-type="corresp" rid="c1-sensors-11-06411"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Benincasa</surname><given-names>Paolo</given-names></name><xref ref-type="aff" rid="af1-sensors-11-06411"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Farneselli</surname><given-names>Michela</given-names></name><xref ref-type="aff" rid="af1-sensors-11-06411"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Tosti</surname><given-names>Giacomo</given-names></name><xref ref-type="aff" rid="af1-sensors-11-06411"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Guiducci</surname><given-names>Marcello</given-names></name><xref ref-type="aff" rid="af1-sensors-11-06411"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Tei</surname><given-names>Francesco</given-names></name><xref ref-type="aff" rid="af1-sensors-11-06411"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Costa</surname><given-names>Corrado</given-names></name><xref ref-type="aff" rid="af2-sensors-11-06411"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Pallottino</surname><given-names>Federico</given-names></name><xref ref-type="aff" rid="af2-sensors-11-06411"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Pari</surname><given-names>Luigi</given-names></name><xref ref-type="aff" rid="af2-sensors-11-06411"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Menesatti</surname><given-names>Paolo</given-names></name><xref ref-type="aff" rid="af2-sensors-11-06411"><sup>2</sup></xref></contrib></contrib-group><aff id="af1-sensors-11-06411"><label>1</label> Department of Agricultural and Environmental Sciences, University of Perugia, Borgo XX Giugno 74, 06121, Perugia, Italy; E-Mails:<email>valentina.ulissi@yahoo.it</email> (V.U.);<email>paoloben@unipg.it</email> (P.B.);<email>michela.farneselli@unipg.it</email> (M.F.);<email>giacomo.tosti@gmail.com</email> (G.T.);<email>mguid@unipg.it</email> (M.G.);<email>f.tei@unipg.it</email> (F.T.)</aff><aff id="af2-sensors-11-06411"><label>2</label> Agricultural Engineering Research Unit of the Agricultural Research Council (CRA-ING), Via della Pascolare 16, 00015, Monterotondo scalo (Rome), Italy; E-Mails:<email>corrado.costa@entecra.it</email> (C.C.);<email>fedepall@yahoo.it</email> (F.P.);<email>luigi.pari@entecra.it</email> (L.P.);<email>paolo.menesatti@entecra.it</email> (P.M.)</aff><author-notes><corresp id="c1-sensors-11-06411"><label>*</label>Author to whom correspondence should be addressed; E-Mail: <email>francescaantonucci@hotmail.it</email>; Tel.: +39-06-906-75-213; Fax: +39-06-906-25-591.</corresp></author-notes><pub-date pub-type="collection"><year>2011</year></pub-date><pub-date pub-type="epub"><day>16</day><month>6</month><year>2011</year></pub-date><volume>11</volume><issue>6</issue><fpage>6411</fpage><lpage>6424</lpage><history><date date-type="received"><day>7</day><month>4</month><year>2011</year></date><date date-type="rev-recd"><day>7</day><month>6</month><year>2011</year></date><date date-type="accepted"><day>15</day><month>6</month><year>2011</year></date></history><permissions><copyright-statement>© 2011 by the authors; licensee MDPI, Basel, Switzerland.</copyright-statement><copyright-year>2011</copyright-year><license><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link>).</license-p></license></permissions><abstract><p>Nitrogen concentration in plants is normally determined by expensive and time consuming chemical analyses. As an alternative, chlorophyll meter readings and N-NO<sub>3</sub> concentration determination in petiole sap were proposed, but these assays are not always satisfactory. Spectral reflectance values of tomato leaves obtained by visible-near infrared spectrophotometry are reported to be a powerful tool for the diagnosis of plant nutritional status. The aim of the study was to evaluate the possibility and the accuracy of the estimation of tomato leaf nitrogen concentration performed through a rapid, portable and non-destructive system, in comparison with chemical standard analyses, chlorophyll meter readings and N-NO<sub>3</sub> concentration in petiole sap. Mean reflectance leaf values were compared to each reference chemical value by partial least squares chemometric multivariate methods. The correlation between predicted values from spectral reflectance analysis and the observed chemical values showed in the independent test highly significant correlation coefficient (<italic>r</italic> = 0.94). The utilization of the proposed system, increasing efficiency, allows better knowledge of nutritional status of tomato plants, with more detailed and sharp information and on wider areas. More detailed information both in space and time is an essential tool to increase and stabilize crop quality levels and to optimize the nutrient use efficiency.</p></abstract><kwd-group><kwd>spectrophotometry</kwd><kwd>VIS-NIR</kwd><kwd>tomato leaf</kwd><kwd>non-destructive</kwd><kwd>SPAD chlorophyll meter readings</kwd><kwd>SAP test</kwd><kwd>leaf analysis</kwd><kwd>nutritional status</kwd><kwd>chemometry</kwd></kwd-group></article-meta></front><floats-group><fig id="f1-sensors-11-06411" position="float"><label>Figure 1.</label><caption><p>Spectral data chemometric analysis procedure.</p></caption><graphic xlink:href="sensors-11-06411f1"></graphic></fig><fig id="f2-sensors-11-06411" position="float"><label>Figure 2.</label><caption><p>Cumulated nitrogen concentration frequency chemically measured in relation to the three different sampling periods (s.p. 1st, 2nd and 3rd) and the three thresholds (vertical lines) extracted by the critical-N curve proposed by Tei <italic>et al.</italic> [<xref ref-type="bibr" rid="b9-sensors-11-06411">9</xref>].</p></caption><graphic xlink:href="sensors-11-06411f2"></graphic></fig><fig id="f3-sensors-11-06411" position="float"><label>Figure 3.</label><caption><p>The correlation between measured and predicted values of N of SPAD (chlorophyll meter readings) and SAP (measurements of N-NO<sub>3</sub> concentration in petiole) analysis in the test represented by the 15% of the whole sample dataset extracted by the SPXY (sample set partitioning based on joint X- and Y-blocks) method.</p></caption><graphic xlink:href="sensors-11-06411f3"></graphic></fig><table-wrap id="t1-sensors-11-06411" position="float"><label>Table 1.</label><caption><p>List of the different X and Y pre-processing techniques applied in the analysis.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="middle" rowspan="1" colspan="1"><bold>Label</bold></th><th align="left" valign="middle" rowspan="1" colspan="1"><bold>Description</bold></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">None</td><td align="left" valign="top" rowspan="1" colspan="1">No pre-processing</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Log 1/R</td><td align="left" valign="top" rowspan="1" colspan="1">Transformation of reflectance in absorbance following log(1/R) formula</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Diff1</td><td align="left" valign="top" rowspan="1" colspan="1">differences between adjacent variables (approximate derivatives)</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Log10</td><td align="left" valign="top" rowspan="1" colspan="1">Log 10</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">logdecay</td><td align="left" valign="top" rowspan="1" colspan="1">Log Decay Scaling</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Baseline</td><td align="left" valign="top" rowspan="1" colspan="1">Baseline (Weighted Least Squares)</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Abs</td><td align="left" valign="top" rowspan="1" colspan="1">Takes the absolute value of the data</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Autoscale</td><td align="left" valign="top" rowspan="1" colspan="1">Centres columns to zero mean and scales to unit variance</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Detrend</td><td align="left" valign="top" rowspan="1" colspan="1">Remove a linear trend</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">GLS Weighting</td><td align="left" valign="top" rowspan="1" colspan="1">Generalized Least Squares Weighting</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Groupscale</td><td align="left" valign="top" rowspan="1" colspan="1">Group/block scaling</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">mean centre</td><td align="left" valign="top" rowspan="1" colspan="1">Centre columns to have zero mean</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">msc (mean)</td><td align="left" valign="top" rowspan="1" colspan="1">Multiplicative scatter correction with offset, the mean is the reference spectrum</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">median centre</td><td align="left" valign="top" rowspan="1" colspan="1">Centre columns to have zero median</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Normalize</td><td align="left" valign="top" rowspan="1" colspan="1">Normalization of the rows</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Osc</td><td align="left" valign="top" rowspan="1" colspan="1">Orthogonal Signal Correction</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Sg</td><td align="left" valign="top" rowspan="1" colspan="1">Savitsky-Golay smoothing and derivatives</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Snv</td><td align="left" valign="top" rowspan="1" colspan="1">Standard Normal Deviate</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Centering</td><td align="left" valign="top" rowspan="1" colspan="1">Multiway Center</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Scaling</td><td align="left" valign="top" rowspan="1" colspan="1">Multiway Scale</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">sqmnsc</td><td align="left" valign="top" rowspan="1" colspan="1">Scale each variable by the square root of its mean</td></tr></tbody></table></table-wrap><table-wrap id="t2-sensors-11-06411" position="float"><label>Table 2.</label><caption><p>Results of linear regression prediction of N concentration in tomato leaves from SPAD (chlorophyll meter readings) and SAP (measurements of N-NO<sub>3</sub> concentration in petiole) analysis. Efficiency parameters reported: correlation coefficient (<italic>r</italic>), Standard Error of Prediction (SEP), Root Mean Square Error (RMSE), Squared bias (SB), nonunity (NU) and lack of correlation (LC).</p></caption><table frame="box" rules="cols"><thead><tr><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Parameter</bold></th><th align="center" valign="middle" rowspan="1" colspan="1"><bold>SPAD</bold></th><th align="center" valign="middle" rowspan="1" colspan="1"><bold>SAP</bold></th></tr><tr><th colspan="3" align="left" valign="middle" rowspan="1"><hr></hr></th></tr></thead><tbody><tr><td colspan="3" align="center" valign="top" rowspan="1"><bold>MODEL (85%)</bold></td></tr><tr><td colspan="3" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">n° samples</td><td align="center" valign="top" rowspan="1" colspan="1">100</td><td align="center" valign="top" rowspan="1" colspan="1">100</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><italic>r</italic> (measured <italic>vs.</italic> predicted)</td><td align="center" valign="top" rowspan="1" colspan="1">0.5383</td><td align="center" valign="top" rowspan="1" colspan="1">0.5638</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">SEP</td><td align="center" valign="top" rowspan="1" colspan="1">0.6260</td><td align="center" valign="top" rowspan="1" colspan="1">0.6135</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">RMSE</td><td align="center" valign="top" rowspan="1" colspan="1">0.3918</td><td align="center" valign="top" rowspan="1" colspan="1">0.3763</td></tr><tr><td colspan="3" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td colspan="3" align="center" valign="top" rowspan="1"><bold>TEST (15%)</bold></td></tr><tr><td colspan="3" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">n° samples</td><td align="center" valign="top" rowspan="1" colspan="1">17</td><td align="center" valign="top" rowspan="1" colspan="1">17</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><italic>r</italic> (measured <italic>vs.</italic> predicted)</td><td align="center" valign="top" rowspan="1" colspan="1">0.5589</td><td align="center" valign="top" rowspan="1" colspan="1">0.5594</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">SEP</td><td align="center" valign="top" rowspan="1" colspan="1">0.7169</td><td align="center" valign="top" rowspan="1" colspan="1">0.7537</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">RMSE</td><td align="center" valign="top" rowspan="1" colspan="1">0.6420</td><td align="center" valign="top" rowspan="1" colspan="1">0.6978</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">SB</td><td align="center" valign="top" rowspan="1" colspan="1">0.12</td><td align="center" valign="top" rowspan="1" colspan="1">0.12</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">NU</td><td align="center" valign="top" rowspan="1" colspan="1">0.0003</td><td align="center" valign="top" rowspan="1" colspan="1">0.0517</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">LC</td><td align="center" valign="top" rowspan="1" colspan="1">0.4834</td><td align="center" valign="top" rowspan="1" colspan="1">0.483</td></tr></tbody></table></table-wrap><table-wrap id="t3-sensors-11-06411" position="float"><label>Table 3.</label><caption><p>Results of Partial Least Squares (PLS) multivariate analysis on the four different datasets (W = whole dataset, 125 vars; R1 = restricted dataset, first 92 vars, 400–694 nm; R2 = restricted dataset, 46 vars, 400–694 nm, represented by the mean values of each single consecutive pair of steps; R3 = restricted dataset, 62 vars, 496–694 nm) predicting the N concentration in tomato leaves from spectral reflectance analysis. In the table are reported: number of Latent Vectors (LV), Root mean square error in calibration (RMSEC) and validation (RMSECV), correlation coefficient (<italic>r</italic>), Standard Error of Prediction (SEP) and Root Mean Squares Error (RMSE).</p></caption><table frame="box" rules="cols"><thead><tr><th align="left" valign="middle" rowspan="1" colspan="1"></th><th align="center" valign="middle" rowspan="1" colspan="1"><bold>W</bold></th><th align="center" valign="middle" rowspan="1" colspan="1"><bold>R1</bold></th><th align="center" valign="middle" rowspan="1" colspan="1"><bold>R2</bold></th><th align="center" valign="middle" rowspan="1" colspan="1"><bold>R3</bold></th></tr><tr><th colspan="5" align="center" valign="top" rowspan="1"><hr></hr></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">n° total samples</td><td align="center" valign="top" rowspan="1" colspan="1">117</td><td align="center" valign="top" rowspan="1" colspan="1">117</td><td align="center" valign="top" rowspan="1" colspan="1">117</td><td align="center" valign="top" rowspan="1" colspan="1">117</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">n° LV</td><td align="center" valign="top" rowspan="1" colspan="1">4</td><td align="center" valign="top" rowspan="1" colspan="1">8</td><td align="center" valign="top" rowspan="1" colspan="1">11</td><td align="center" valign="top" rowspan="1" colspan="1">11</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">First pre-processing X-block</td><td align="center" valign="top" rowspan="1" colspan="1">Log1/R</td><td align="center" valign="top" rowspan="1" colspan="1">None</td><td align="center" valign="top" rowspan="1" colspan="1">Log1/R</td><td align="center" valign="top" rowspan="1" colspan="1">Log1/R</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Second pre-processing X-block</td><td align="center" valign="top" rowspan="1" colspan="1">sg</td><td align="center" valign="top" rowspan="1" colspan="1">snv</td><td align="center" valign="top" rowspan="1" colspan="1">sg</td><td align="center" valign="top" rowspan="1" colspan="1">snv</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Pre-processing Y-block</td><td align="center" valign="top" rowspan="1" colspan="1">none</td><td align="center" valign="top" rowspan="1" colspan="1">autoscale</td><td align="center" valign="top" rowspan="1" colspan="1">autoscale</td><td align="center" valign="top" rowspan="1" colspan="1">none</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">RMSEC</td><td align="center" valign="top" rowspan="1" colspan="1">0.4942</td><td align="center" valign="top" rowspan="1" colspan="1">0.5744</td><td align="center" valign="top" rowspan="1" colspan="1">0.6079</td><td align="center" valign="top" rowspan="1" colspan="1">0.4294</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">RMSECV</td><td align="center" valign="top" rowspan="1" colspan="1">0.5120</td><td align="center" valign="top" rowspan="1" colspan="1">0.7990</td><td align="center" valign="top" rowspan="1" colspan="1">0.7924</td><td align="center" valign="top" rowspan="1" colspan="1">0.5700</td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><bold>MODEL (85%)</bold></td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">n° samples</td><td align="center" valign="top" rowspan="1" colspan="1">100</td><td align="center" valign="top" rowspan="1" colspan="1">100</td><td align="center" valign="top" rowspan="1" colspan="1">100</td><td align="center" valign="top" rowspan="1" colspan="1">100</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><italic>r</italic> (measured <italic>vs.</italic> predicted)</td><td align="center" valign="top" rowspan="1" colspan="1">0.7436</td><td align="center" valign="top" rowspan="1" colspan="1">0.8165</td><td align="center" valign="top" rowspan="1" colspan="1">0.7917</td><td align="center" valign="top" rowspan="1" colspan="1">0.8134</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">SEP</td><td align="center" valign="top" rowspan="1" colspan="1">0.4967</td><td align="center" valign="top" rowspan="1" colspan="1">0.4308</td><td align="center" valign="top" rowspan="1" colspan="1">0.4533</td><td align="center" valign="top" rowspan="1" colspan="1">0.4316</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">RMSE</td><td align="center" valign="top" rowspan="1" colspan="1">0.4942</td><td align="center" valign="top" rowspan="1" colspan="1">0.4286</td><td align="center" valign="top" rowspan="1" colspan="1">0.4510</td><td align="center" valign="top" rowspan="1" colspan="1">0.4294</td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><bold>TEST (15%)</bold></td></tr><tr><td colspan="5" align="center" valign="top" rowspan="1"><hr></hr></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">n° samples</td><td align="center" valign="top" rowspan="1" colspan="1">17</td><td align="center" valign="top" rowspan="1" colspan="1">17</td><td align="center" valign="top" rowspan="1" colspan="1">17</td><td align="center" valign="top" rowspan="1" colspan="1">17</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1"><italic>r</italic> (measured <italic>vs.</italic> predicted)</td><td align="center" valign="top" rowspan="1" colspan="1">0.8856</td><td align="center" valign="top" rowspan="1" colspan="1">0.8921</td><td align="center" valign="top" rowspan="1" colspan="1">0.9244</td><td align="center" valign="top" rowspan="1" colspan="1">0.9414</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">SEP</td><td align="center" valign="top" rowspan="1" colspan="1">0.4186</td><td align="center" valign="top" rowspan="1" colspan="1">0.4255</td><td align="center" valign="top" rowspan="1" colspan="1">0.3597</td><td align="center" valign="top" rowspan="1" colspan="1">0.3466</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">RMSE</td><td align="center" valign="top" rowspan="1" colspan="1">0.4271</td><td align="center" valign="top" rowspan="1" colspan="1">0.6460</td><td align="center" valign="top" rowspan="1" colspan="1">0.5997</td><td align="center" valign="top" rowspan="1" colspan="1">0.4054</td></tr></tbody></table></table-wrap></floats-group></pmc><affiliations><list></list><tree><noCountry><name sortKey="Antonucci, Francesca" sort="Antonucci, Francesca" uniqKey="Antonucci F" first="Francesca" last="Antonucci">Francesca Antonucci</name><name sortKey="Benincasa, Paolo" sort="Benincasa, Paolo" uniqKey="Benincasa P" first="Paolo" last="Benincasa">Paolo Benincasa</name><name sortKey="Costa, Corrado" sort="Costa, Corrado" uniqKey="Costa C" first="Corrado" last="Costa">Corrado Costa</name><name sortKey="Farneselli, Michela" sort="Farneselli, Michela" uniqKey="Farneselli M" first="Michela" last="Farneselli">Michela Farneselli</name><name sortKey="Guiducci, Marcello" sort="Guiducci, Marcello" uniqKey="Guiducci M" first="Marcello" last="Guiducci">Marcello Guiducci</name><name sortKey="Menesatti, Paolo" sort="Menesatti, Paolo" uniqKey="Menesatti P" first="Paolo" last="Menesatti">Paolo Menesatti</name><name sortKey="Pallottino, Federico" sort="Pallottino, Federico" uniqKey="Pallottino F" first="Federico" last="Pallottino">Federico Pallottino</name><name sortKey="Pari, Luigi" sort="Pari, Luigi" uniqKey="Pari L" first="Luigi" last="Pari">Luigi Pari</name><name sortKey="Tei, Francesco" sort="Tei, Francesco" uniqKey="Tei F" first="Francesco" last="Tei">Francesco Tei</name><name sortKey="Tosti, Giacomo" sort="Tosti, Giacomo" uniqKey="Tosti G" first="Giacomo" last="Tosti">Giacomo Tosti</name><name sortKey="Ulissi, 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