Locally-weighted-RoBoost-PLS: a multivariate calibration approach to simultaneously cope with non-linearities and outliers
This paper describes a novel multivariate calibration approach named LW-RoBoost-PLS, which provides a solution for predictive modelling in scenarios where outliers and non-linearities co-exist. LW-RoBoost-PLS simultaneously handles non-linearities and outliers by combining local and robust modelling strategies, leading to improved prediction accuracy and reduced bias.
Authors
- Daniele Tanzilli
- Lorenzo Strani
- Marina Cocchi (ORCID: https://orcid.org/0000-0001-8764-4981)
- Matthieu Lesnoff
- Jean Michel Roger
- Raffaele Vitale (ORCID: https://orcid.org/0000-0002-7497-1673)
- Maxime Metz (ORCID: https://orcid.org/0000-0002-0715-7997)
- Cyril Ruckebusch (ORCID: https://orcid.org/0000-0001-8120-4133)
Institutions
- University of Modena and Reggio Emilia (IT)
- Centre National de la Recherche Scientifique (FR)
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR)
- Université de Montpellier (FR)
- Université de Lille (FR)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Institut Agro Montpelier (FR)
- Services déconcentrés d'appui à la recherche Occitanie-Montpellier (FR)
- Encana (Canada) (CA)
Publication Details
- Journal
- Analytica Chimica Acta
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1016/j.aca.2025.344167
- Citations
- 4
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 3.01