New data preprocessing trends based on ensemble of multiple preprocessing techniques

TLDR

Analytical instruments generate data with unwanted variation from measurement mode, sample state, and environmental factors, requiring preprocessing to predict properties accurately, yet individual correction methods may leave residual artefacts that complementary multiple preprocessing can jointly remove. The article reviews recent developments in data preprocessing strategies, focusing on emerging ensemble approaches to preprocessing fusion in chemometrics. The authors present a demonstration case illustrating the ensemble preprocessing approach. Ensemble preprocessing enables selecting complementary techniques that improve models and is applicable beyond spectral data to any context where a single best preprocessing option is unclear.

Abstract

Data generated by analytical instruments, such as spectrometers, may contain unwanted variation due to measurement mode, sample state and other external physical, chemical and environmental factors. Preprocessing is required so that the property of interest can be predicted correctly. Different correction methods may remove specific types of artefacts while still leaving some effects behind. Using multiple preprocessing in a complementary way can remove the artefacts that would be left behind by using only one technique. This article summarizes the recent developments in new data preprocessing strategies and specifically reviews the emerging ensemble approaches to preprocessing fusion in chemometrics. A demonstration case is also presented. In summary, ensemble preprocessing allows the selection of several techniques and their combinations that, in a complementary way, lead to improved models. Ensemble approaches are not limited to spectral data but can be used in all cases where preprocessing is needed and identification of a single best option is not easily done.