Multivariate resolution of rank-deficient near-infrared spectroscopy data from the reaction of curing epoxy resins using the rank augmentation strategy and multivariate curve resolution alternating least squares approach

2004 ◽  
Vol 515 (1) ◽  
pp. 65-73 ◽  
Author(s):  
M. Garrido ◽  
I. Lázaro ◽  
M.S. Larrechi ◽  
F.X. Rius
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Near Infrared Spectroscopy ◽  
Epoxy Resins ◽  
Near Infrared ◽  
Multivariate Curve Resolution ◽  
Spectroscopy Data ◽  
Curve Resolution ◽  
Augmentation Strategy ◽  
Least Squares Approach

scholarly journals A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guang-Hui Fu ◽  
Min-Jie Zong ◽  
Feng-Hua Wang ◽  
Lun-Zhao Yi
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Model Calibration ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Elastic Net ◽  
Spectroscopy Data ◽  
Wavelength Selection

Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.

Sign in / Sign up

Export Citation Format

Share Document