New data preprocessing trends based on ensemble of multiple preprocessing techniques

Puneet Mishra*, Alessandra Biancolillo, Jean Michel Roger, Federico Marini, Douglas N. Rutledge

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Article number116045
JournalTrAC - Trends in Analytical Chemistry
Volume132
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Chemometrics
  • Ensemble learning
  • Multi-block analysis
  • Multivariate calibration
  • Preprocessing

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