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

I tiakina i:
I whakaputaina i:Applied Sciences vol. 15, no. 21 (2025), p. 11325-11340
Kaituhi matua: Wu, Yonghong
Ētahi atu kaituhi: Zhou Yukun, Chen, Xiaojing, Xie Zhonghao, Shujat, Ali, Huang Guangzao, Yuan Leiming, Shi, Wen, Wang, Xin, Zhang Lechao
I whakaputaina:
MDPI AG
Ngā marau:
Variables
Feature selection
Methods
Datasets
Algorithms
Spectrum analysis
Calibration
Urunga tuihono:Citation/Abstract
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Whakarāpopotonga: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
Puna:Publicly Available Content Database