1. Data collection  Triticale samples from 2002 to 2005 (Iowa, USA).  Foss Infratec™ 1241 (transmittance instrument).  Crude protein analysis by AACC Method 46-30 (combustion nitrogen). using a LECO CHN-2000 Analyzer. Calibration methods  Partial Least Squared Regression (PLS-R).  Least Squares Support Vector Machine Regression (LS-SVM R). Preprocessing methods  2 nd derivative (Savitzky-Golay 5-point window, 3 rd order polynomial). Software  MATLAB v. 7.04 (The MathWorks, Inc.).  PLS_Toolbox 3.5.4 (Eigenvector Research, Inc.).  LS-SVMlab Toolbox (Suykens et al., 2002).  JMP v. 6.0.0 (SAS Institute Inc.). Validation methods  Cross-validation (leave-one-out).  Validation set by holding out 25% and 10% of the calibration set.  Validation set from year n+1. Evaluation parameter  Relative Performance Determinant (RPD) using the standard error of prediction (SEP d ) corrected for bias. Does your grain calibration need to be updated? Benoit Igne 1 (igneb@iastate.edu), Glen R. Rippke 1 (rippke@iastate.edu), Lance R. Gibson 2 (lgibson@iastate.edu), Charles R. Hurburgh, Jr 1 (tatry@iastate.edu) 1 Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa. 2 Department of Agronomy, Iowa State University, Ames, Iowa. Introduction Objectives Materials and Methods Conclusions The update of a calibration is a costly process whose outcomes are not easily predictable. If new samples have high variability, the new model can be less accurate than before. Once the model is developed, a calibration transfer process is often required. In commercial applications, users’ model need to be updated as well as their instruments restandardized. The US Department of Agriculture has established a 3-year rule based on cumulative bias which can freeze calibrations for an extended period even if calibration updates are needed.  Evaluate calibration validation scenarios.  Propose objective criteria to determine when an update is needed. Results Validation scenarios  No significant difference between all the validation methods using a part of the calibration set for both regression methods over the four years (α = 0.05).  Significant difference between validation set of next year sample and 25% of the calibration set (α = 0.05) for 2002. No significant difference among the others.  Significant difference in 2003 and 2004 between all validation methods based on same year sample and next year sample (α=0.05).  No significant difference between both regression method (α = 0.05). Comparison of 10% and 25% validation sets  Between similar years, all validation methods gave the same results, no matter the regression method used. While predicting a dissimilar year, the use of a next year sample validation set gave more realistic information on the performance of the calibration.  The use of a small validation set (removed from the calibration set) can lead to wrong conclusions about the calibration performances.  Validation criteria must be adapted to the situation but bias-based decisions are likely not optimal.  The establishment of strict rules is not feasible. The combination of all the parameters presented and discussed here can help to take the best decision. What about bias evolution? Discussion  Based on a bias-evolution, the decision to update the calibration would occur only through a next year validation set samples (update if three-year average bias exceeds ± 0.3%).  The same trend is observable for both regression methods.  Calibrations developed through year n and validated on the same calibration pool can lead to misinterpretations of the performance of the calibration if n+1 year is spectrally different.  Bias-based decision is non-efficient.  RPD-based decision gives a better state of the calibration. Objective criteria with n+1 year validation samples would lead to the following rules: -Better RPD  update can be planned: few risks of poorer accuracy of the calibration after update. -Equivalent RPD  all the sample variability is included in the calibration: no need to change it. -Lower RPD  update necessary. Attention is needed in the choice of the new calibration samples to avoid the addition of noise or yearly outliers. Cross validation Validation set next year 25% calibration set 10% calibration set 2002 0.000.730.06 2003 0.012.020.070.06 2004 0.000.210.060.04 3-year average 0.040.990.06 Cross validation Validation set next year 25% calibration set 10% calibration set 2002 0.010.640.030.06 2003 0.022.290.040.05 2004 0.000.310.050.02 3-year average 0.011.080.04 PLS - Regression LS-SVM Regression  No significant difference between errors given by both regression methods (α=0.05).  No significant difference between the error of both validation methods (α=0.05). Additional data would help clarifying this issue. Various pathways of diffuse reflectance 13 th IDRC Conference