Additive partial least squares for efficient modelling of independent variance sources demonstrated on practical case studies

Autoren Pekka Luoma
Thomas Natschläger
Birgit Malli
Marcin Pawliczek
Markus Brandstetter
Editoren
Titel Additive partial least squares for efficient modelling of independent variance sources demonstrated on practical case studies
Typ Artikel
Journal Analytica Chimica Acta
Nummer 5
Band 1077
DOI 10.1016/j.aca.2017.12.027
Monat January
Jahr 2018
Seiten 10-15
SCCH ID# 17082
Abstract

A model recalibration method based on additive Partial Least Squares (PLS) regression is generalized for multi-adjustment scenarios of independent variance sources (referred to as additive PLS - aPLS). aPLS allows for effortless model readjustment under changing measurement conditions and the combination of independent variance sources with the initial model by means of additive modelling. We demonstrate these distinguishing features on two NIR spectroscopic case-studies. In case study 1 aPLS was used as a readjustment method for an emerging offset. The achieved RMS error of prediction (1.91 a.u.) was of similar level as before the offset occurred (2.11 a.u.). In case-study 2 a calibration combining different variance sources was conducted. The achieved performance was of sufficient level with an absolute error being better than 0.8% of the mean concentration, therefore being able to compensate negative effects of two independent variance sources. The presented results show the applicability of the aPLS approach. The main advantages of the method are that the original model stays unadjusted and that the modelling is conducted on concrete changes in the spectra thus supporting efficient (in most cases straightforward) modelling. Additionally, the method is put into context of existing machine learning algorithms.