OPTIMIZED SCALING - A NOVEL-APPROACH TO LINEAR CALIBRATION WITH CLOSED DATA SETS

Optimized scaling is a new calibration method for closed data sets which corrects for multiplicative effects or errors in the calibration step. The predictive ability is compared to the methods of partial least squares (PLS) regression and principal component regression. On data sets where multiplicative effects are present, optimized scaling gave the lowest prediction errors. On data sets where multiplicative corrections are not needed, optimized scaling gave comparable results to PLS regression.