Automation of Local Regression Model Building for Spectroscopic Data

Abstract

The concept of using local calibration for spectroscopic analysis has been discussed since the late 1980s. Since that time, many papers have described modifications to different aspects of the local modeling methodology. In this paper, we briefly discuss some of the modifications and describe an approach for the unattended automation of local model development. Ways to reduce calculation time are discussed. Four example spectroscopic datasets using Raman, FT-NIR, and dispersive NIR are analyzed, and the local model prediction performance is compared to standard PLS prediction performance. Using independent prediction sets, local modeling is shown to improve prediction performance by 17% to 55%.