End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network-Reference-Cited by-同舟云学术

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End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network

Published:2019-03-19 Issue:5 Volume:33 Page:e3122
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Chen Yuan‐Yuan¹²^ORCID,Wang Zhi‐Bin²³

Affiliation:

1. School of Information and Communication EngineeringNorth University of China Taiyuan China

2. Engineering Technology Research Center of Shanxi Province for Opto‐Electronic Information and Instrument Taiyuan China

3. School of ScienceNorth University of China Taiyuan China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.3122

Reference20 articles.

1. Variables selection methods in near-infrared spectroscopy

2. Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis

3. Feature selection for nonlinear models with extreme learning machines

4. Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy

5. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra

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