TrAC Trends in Analytical Chemistry· 2020Q1
New data preprocessing trends based on ensemble of multiple preprocessing techniques
- 479citations
- Q1SCImago
- 2020year
Short summary
Ensemble preprocessing, combining multiple techniques, improves analytical model performance by removing residual artefacts missed by single methods.
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Key points
- Analytical data requires preprocessing to remove unwanted variation from measurement factors.
- Single preprocessing techniques may leave residual artefacts.
- Ensemble preprocessing combines multiple techniques to achieve better artefact removal.
- This approach improves analytical model performance.
- Ensemble methods are applicable to various data types beyond spectral data.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. TrAC Trends in Analytical Chemistry, 2020 · DOI ↗
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Field: Analytical Chemistry
Analytical ChemistryChemistry