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      <ref-type name="Journal Article">17</ref-type>
      <work-type>ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES</work-type>
      <contributors>
        <authors>
          <author>
            <style face="normal" font="default" size="100%">Zgouz, A.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Heran, D.</style>
          </author>
          <author>
            <style face="bold" font="default" size="100%">Barthès, Bernard</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Bastianelli, D.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Bonnal, L.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Baeten, V.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Lurol, S.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Bonin, M.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Roger, J. M.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Bendoula, R.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Chaix, G.</style>
          </author>
        </authors>
      </contributors>
      <titles>
        <title>Dataset of visible-near infrared handheld and micro-spectrometers-comparison of the prediction accuracy of sugarcane properties [Data paper]</title>
        <secondary-title>Data in Brief</secondary-title>
      </titles>
      <pages>106013 [6 ]</pages>
      <keywords>
        <keyword>Micro-spectrometers</keyword>
        <keyword>Handheld devices</keyword>
        <keyword>Visible-near infrared</keyword>
        <keyword>spectroscopy</keyword>
        <keyword>chemometrics</keyword>
        <keyword>sugarcane</keyword>
      </keywords>
      <dates>
        <year>2020</year>
      </dates>
      <call-num>fdi:010079738</call-num>
      <language>ENG</language>
      <periodical>
        <full-title>Data in Brief</full-title>
      </periodical>
      <isbn>2352-3409</isbn>
      <accession-num>ISI:000569214200040</accession-num>
      <electronic-resource-num>10.1016/j.dib.2020.106013</electronic-resource-num>
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          <url>https://www.documentation.ird.fr/hor/fdi:010079738</url>
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          <url>https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-10/010079738.pdf</url>
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      <volume>31</volume>
      <remote-database-provider>Horizon (IRD)</remote-database-provider>
      <abstract>In the dataset presented in this article, sixty sugarcane samples were analyzed by eight visible / near infrared spectrometers including seven micro-spectrometers. There is one file per spectrometer with sample name, wavelength, absorbance data [calculated as log(10) (1/Reflectance)], and another file for reference data, in order to assess the potential of the micro spectrometers to predict chemical properties of sugarcane samples and to compare their performance with a LabSpec spectrometer. The Partial Least Square Regression (PLS-R) algorithm was used to build calibration models. This open ac cess dataset could also be used to test new chemometric methods, for training, etc.</abstract>
      <custom6>076 ; 020</custom6>
      <custom1>UR210</custom1>
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