Data preprocessing for multiblock modelling – A systematization with new methods-Reference-Cited by-同舟云学术

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Data preprocessing for multiblock modelling – A systematization with new methods

Published:2020-04 Issue: Volume:199 Page:103959
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Campos Maria P.,Reis Marco S.

Funder

Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáticas

European Union’s FEDER

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference83 articles.

1. Network-induced supervised learning: Network-induced classification (NI-C) and network-induced regression (NI-R)

2. Process analysis, monitoring and diagnosis, using multivariate projection methods

3. Advanced predictive methods for wine age prediction: Part II – A comparison study of multiblock regression approaches

4. Review on data-driven modeling and monitoring for plant-wide industrial processes

5. Two-level multiblock statistical monitoring for plant-wide processes

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2. Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products;Analytica Chimica Acta;2024-08

3. Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts;Analytica Chimica Acta;2024-06

4. Improving profitability of continuous processes facing raw material variability through data-driven SMB-PLS model-based adaptive control;Computers & Chemical Engineering;2024-05

5. Combination of feature selection and geographical stratification increases the soil total nitrogen estimation accuracy based on vis-NIR and pXRF spectral fusion;Computers and Electronics in Agriculture;2024-03

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