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.

New data preprocessing trends based on ensemble of multiple preprocessing techniques / Mishra, P.; Biancolillo, A.; Roger, J. M.; Marini, F.; Rutledge, D. N.. - In: TRAC. TRENDS IN ANALYTICAL CHEMISTRY. - ISSN 0165-9936. - 132:(2020), pp. 1-12. [10.1016/j.trac.2020.116045]

New data preprocessing trends based on ensemble of multiple preprocessing techniques

Marini F.;
2020

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.
2020
chemometrics; ensemble learning; multi-block analysis; multivariate calibration; preprocessing
01 Pubblicazione su rivista::01a Articolo in rivista
New data preprocessing trends based on ensemble of multiple preprocessing techniques / Mishra, P.; Biancolillo, A.; Roger, J. M.; Marini, F.; Rutledge, D. N.. - In: TRAC. TRENDS IN ANALYTICAL CHEMISTRY. - ISSN 0165-9936. - 132:(2020), pp. 1-12. [10.1016/j.trac.2020.116045]
File allegati a questo prodotto
File Dimensione Formato  
Mishra_New data_2020.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 1.91 MB
Formato Adobe PDF
1.91 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1499996
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 193
  • ???jsp.display-item.citation.isi??? 165
social impact