Stacked PLS for calibration transfer without standards

摘要

We report the successful application of stacked partial least-squares (SPLS) regression for direct application of multivariate calibration models to data from a secondary spectrometer, without use of any calibration transfer. Unlike a conventional calibration that requires transfer methods which need measurement of a set of transfer samples to make useful predictions from data obtained on a secondary instrument, SPLS regression can be used to generate regression models with good predictive power on both primary and secondary instruments. Results of direct application of SPLS to lake sediment data without use of any transfer standards show predictive results comparable to those obtained from conventional PLS calibration followed by a model updating (MUP) step. We also demonstrate the use of calibration MUP applied to stacked PLS regression, which further minimizes local differences between two instruments.