1. Use of spectral preprocessing to obtain a common basis for robust regression 5 spectral preprocessing combinations gave significantly higher RPDs (α = 5%): Second derivative (25-point window) + Normalization. SNV + Second derivative (25-point window) + Normalization. MSC + Second derivative (25-point window) + Normalization. Second derivative (25-point window) + Normalization + OSC + Autoscaling. Second derivative (25-point window) + GLSW + Normalization + Mean-Center. The five combinations were averaged and compared to other standardization techniques. Comparison among standardization techniques Figures 1 and 2 show results obtained when predicting validation sets where Foss Infratec 1241 and Dickey-john OmegAnalyzer G 6118 were respective network masters. PDS and DS gave significantly lower RPDs. Other techniques were not significantly different. Network master’ RPDs were significantly higher. Among common standardization techniques, post- regression correction gave as good or better results than individual models (developed on their own calibration set). Robust techniques also gave as good or better results than other techniques in 6 of 8 cases. Calibration transfer from Foss Infratec to Dickey-john OmegAnalyzer G gave more precise validation results than the reverse case. Data Preprocessing for common basis Baseline and Offset Correction: Smoothing / Derivative. Detrending. Weighted Least Squares Baseline. Sample Normalization or Light Scattering Correction: Normalization. Standard Normal Variate (SNV). Multiplicative Scatter Correction (MSC). Interference Removal or Multivariate filtering: Orthogonal Signal Correction (OSC). Generalized Least Squares Weighting (GLSW). Variable Scaling: Mean-Center. Autoscaling. All reasonable and meaningful spectral pretreatment combinations were evaluated (n = 75). Common Standardization techniques Optical Techniques: Direct Standardization (DS) and Piecewise Direct Standardization (PDS). Post-Regression Correction: Slope and Offset; and Bias Only. The transfer of calibration models without any standardization procedure was also considered. All models using common standardization techniques and when each instrument was developed on their own calibration set used 2 nd derivative (25 point window) + Normalization + Autoscale as spectral pretreatment. Model comparison RPD: Ratio of standard error of prediction to standard deviation of reference values. RPD allows rapid comparison between models validated on the same set. Software MATLAB v. 7.04 (The MathWorks,Inc., Natick, MA). PLS_Toolbox 3.5.4 (Eigenvector Research, Inc., Wenatchee, WA). JMP v. 6.0.0 (SAS Institute Inc., Cary, NC). Robust Regression for Inter-Brand Standardization Benoit Igne (igneb@iastate.edu), Glen R. Rippke (rippke@iastate.edu), Charles R. Hurburgh, Jr. (tatry@iastate.edu). Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa. Introduction Objectives Materials and Methods Conclusions Procedure The transfer of calibrations from instrument to instrument is an important research area. Many methods have been developed to transfer a prediction model from a master unit to a secondary unit: Optical techniques (Piecewise Direct Standardization, Direct Standardization) Post-regression correction techniques (slope and bias or bias only correction) Model adaptation techniques (robust regression) The new challenge is to transfer calibration across brands. Robust models are attractive because they allow the use of historical databases and the use of the same samples scanned on different instruments. Robust models often increase the prediction error because they add additional noise. 1.Evaluate the use of preprocessing techniques in the creation of robust models for inter-brand standardization. 2.Compare results with standardization performance of known standardization techniques. Data collection Soybean Samples (whole): Calibration set: 638 samples from 2002 to 2006 crop years. Two validation sets: Set 1: 20 samples representative of the variability of the calibration set. Set 2: 40 very diverse samples from the 2006 crop year. Spectral data: Four transmittance units, spectral range: 850 – 1048 nm with 2 nm increment. 2 Foss Infratec™ (Foss North America, Eden Prairie, MN): Foss Infratec™ 1229 (S/N: 553075) and Foss Infratec™ 1241 (S/N: 12410350). 2 Dickey-john/Bruins OmegAnalyzer G (Dickey-john ® Corporation, Auburn, IL): S/N 106110 and 106118. Reference analysis: Protein content by combustion (AOAC 990.03), Eurofins, Des Moines. Oil content by ether extract (AOCS Ac 3-44), Eurofins, Des Moines. Calibration method Partial Least Squares Regression (PLS). Robust Regression Two types of robust models were created: Combine historical databases of each brand master. Use historical database of one brand master. Models were developed on a common spectral basis obtained using spatial spectral pretreatments. Results 1.Establish baseline calibration performance when each instrument is calibrated on its own calibration set. 2.Apply common standardization techniques to inter-brand standardization (Infratec 1241 and OmegAnalyzer G 106118 were brand masters). 3.Compare inter-brand standardization results developed from the best spectral preprocessing combinations with common standardization results. The calculation of intermediate standardization parameters for optical techniques (DS, PDS) increased the error. Results from other standardization techniques were similar across standardization sets and instruments. The transformation of spectral data to a common basis by preprocessing techniques (before robust regression) gave the best results in 75% of the cases. The transfer of historical databases from one instrument brand to another was proven possible, with or without spectral data from the secondary brand, using robust regressions developed on a common basis obtained by spatial spectral pretreatment. Figure 1: Foss Infratec 1241 as Overall Master for Common Methods.Figure 2: Dickey-john OmegAnalyzer G 106118 as Overall Master for Common Methods. Legend: