Pre-Processing Ensemble Modeling Based on Faster Covariate Selection Calibration for Near-Infrared Spectroscopy

Guardat en:

Dades bibliogràfiques
Publicat a:	Applied Sciences vol. 15, no. 21 (2025), p. 11325-11340
Autor principal:	Wu, Yonghong
Altres autors:	Zhou Yukun, Chen, Xiaojing, Xie Zhonghao, Shujat, Ali, Huang Guangzao, Yuan Leiming, Shi, Wen, Wang, Xin, Zhang Lechao
Publicat:	MDPI AG
Matèries:	Variables Feature selection Methods Datasets Algorithms Spectrum analysis Calibration
Accés en línia:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Descripció
Resum:	Ensemble techniques are crucial for preprocessing near-infrared (NIR) data, yet effectively integrating information from multiple preprocessing methods remains challenging. While multi-block approaches have been introduced to optimize preprocessing selection, they face issues such as block order dependency, slow optimization, and limited interpretability. This study proposes PFCOVSC—a fast, order-independent, and interpretable ensemble preprocessing strategy integrating multi-block fusion and variable selection. The method combines diverse preprocessed data into a unified matrix and employs the efficient fCovsel technique to select informative variables and construct an ensemble model. Evaluated against SPORT and PROSAC on three public datasets, PFCOVSC substantially reduced prediction root mean squared error (RMSE) on wheat and meat datasets by 17%, 13% and 49%, 20%, respectively, while performing comparably on tablet data. The method also demonstrated advantages in computational speed and model interpretability, offering a promising new direction for preprocessing ensemble strategies.
ISSN:	2076-3417
DOI:	10.3390/app152111325
Font:	Publicly Available Content Database