Local vs global methods applied to large near infrared databases covering high variability

  • O. Minet
  • V. Baeten
  • B. Lecler
  • P. Dardenne
  • J. A. Fernández Pierna
Walloon Agricultural Research Centre (CRA-W), Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Henseval Building, 24 Chaussée de Namur, 5030 Gembloux, Belgium

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Walloon Agricultural Research Centre (CRA-W), Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Henseval Building, 24 Chaussée de Namur, 5030 Gembloux, Belgium

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Walloon Agricultural Research Centre (CRA-W), Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Henseval Building, 24 Chaussée de Namur, 5030 Gembloux, Belgium

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Walloon Agricultural Research Centre (CRA-W), Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Henseval Building, 24 Chaussée de Namur, 5030 Gembloux, Belgium

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 Corresponding Author
Walloon Agricultural Research Centre (CRA-W), Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Henseval Building, 24 Chaussée de Namur, 5030 Gembloux, Belgium
[email protected]
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The purpose of this study was to evaluate two different locally based regression methods (LOCAL and Local Calibration by Customized Radii Selection) and compare their performance to the classical global PLS for large NIR data. The data used in this study came from two inter-laboratory studies for wheat grain analysis organized in 2016 in the framework of the REQUASUD network. The results showed that improved predictions in terms of prediction errors can be obtained using local approaches compared to the classical global PLS. Moreover, the study highlighted clear differences between inter-laboratory studies and participating laboratories, which were even more evident when working with local procedures.


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