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Benoit Igne 1 , Lance R. Gibson 2 , Glen R. Rippke 1 , Charles R. Hurburgh, Jr 1

Evaluation of preprocessing methods in the development of near-infrared models for triticale protein and moisture. Benoit Igne 1 , Lance R. Gibson 2 , Glen R. Rippke 1 , Charles R. Hurburgh, Jr 1. 1 Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa.

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Benoit Igne 1 , Lance R. Gibson 2 , Glen R. Rippke 1 , Charles R. Hurburgh, Jr 1

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  1. Evaluation of preprocessing methods in the development of near-infrared models for triticale protein and moisture Benoit Igne1, Lance R. Gibson2, Glen R. Rippke1, Charles R. Hurburgh, Jr1 1 Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa. 2 Department of Agronomy, Iowa State University, Ames, Iowa.

  2. Background • Triticale: crossing between wheat and rye. • Greater vigor and yield than their parents. • Need efficient end-use properties measurements. • Evaluate the use of wheat calibration for triticale protein and moisture prediction. • Develop triticale specific calibrations. • Evaluate the use of preprocessing methods.

  3. Material and Methods • Triticale samples over 4-years trials. • 4 transmittance and 2 reflectance instruments. • Wheat calibrations from two instrument vendors. • PLS regression method. • Preprocessing methods: • Accepted by on-board instrument software: Autoscaling and Mean centering • Advanced methods 2nd derivative, SNV, and MSC

  4. Results – Use of wheat models Protein predictions Moisture predictions  Protein calibrations: Efficient the two first years.  Moisture calibrations: Usable for screening the first year, useless after.

  5. Results – Triticale specific models Protein predictions Moisture predictions  Protein calibrations: Advanced pretreatments gave better results (α = 0.05).  Moisture calibrations: No significant difference (α = 0.05).

  6. Conclusions • Use of wheat calibration is not recommended. • Advanced preprocessing methods gave better results with protein but not with moisture. • The effect of preprocessing methods is instrument and parameter dependent. • Additional samples from next years are needed.

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