Predicting pectin performance strength using near‐infrared spectroscopic data: A comparative evaluation of 1‐D convolutional neural network, partial least squares, and ridge regression modeling

2021 ◽  
Author(s):  
Kasper A. Einarson ◽  
Andreas Baum ◽  
Terkel B. Olsen ◽  
Jan Larsen ◽  
Ibrahim Armagan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Comparative Evaluation ◽  
Spectroscopic Data ◽  
Near Infrared ◽  
Regression Modeling ◽  
Infrared Spectroscopic

A weighted ensemble method based on wavelength selection for near-infrared spectroscopic calibration

2019 ◽  
Vol 11 (36) ◽  
pp. 4593-4599
Author(s):  
Shaohui Yu ◽  
Jing Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Ensemble Method ◽  
Wavelength Selection ◽  
Weighted Clustering ◽  
Infrared Spectroscopic ◽  
Selection For ◽  
Pls Method

A weighted clustering and pruning of wavelength variables-partial least squares (WCPV-PLS) method was proposed.

Boosting partial least-squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

2012 ◽  
Vol 26 (1-2) ◽  
pp. 34-39 ◽  
Author(s):  
Shi-Miao Tan ◽  
Rui-Min Luo ◽  
Yan-Ping Zhou ◽  
Hui Xu ◽  
Dan-Dan Song ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Infrared Spectroscopic
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