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

P. Luoma, B. Malli, M. Pawliczek, M. Brandstetter. Additive partial least squares for efficient modelling of independent variance sources demonstrated on practical case studies. Analytica Chimica Acta, volume 1077, number 5, pages 10-15, DOI 10.1016/j.aca.2017.12.027, 1, 2018.

Autoren
  • Pekka Luoma
  • Birgit Malli
  • Marcin Pawliczek
  • Markus Brandstetter
TypArtikel
JournalAnalytica Chimica Acta
Nummer5
Band1077
DOI10.1016/j.aca.2017.12.027
Monat1
Jahr2018
Seiten10-15
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.