Molecular spectroscopic wavelength selection using combined interval partial least squares and correlation coefficient optimization

2019 ◽  
Vol 11 (24) ◽  
pp. 3108-3116 ◽  
Author(s):  
Weiwei Jiang ◽  
Changhua Lu ◽  
Yujun Zhang ◽  
Wei Ju ◽  
Jizhou Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Vital Role ◽  
Wavelength Selection ◽  
Coefficient Optimization

Wavelength selection plays a vital role in employing near-infrared spectroscopy for analyzing samples.

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.

scholarly journals Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cicer Arietinum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Neutral Detergent Fiber ◽  
Acid Detergent Fiber ◽  
Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

scholarly journals Hemodialysis monitoring using mid- and near-infrared spectroscopy with partial least squares regression

2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression
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