Variable selection in random calibration of near-infrared instruments: ridge regression and partial least squares regression settings

2003 ◽  
Vol 17 (3) ◽  
pp. 174-185 ◽  
Author(s):  
Arief Gusnanto ◽  
Yudi Pawitan ◽  
Jian Huang ◽  
Bill Lane
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Infrared Instruments

Variable Selection in near Infrared Spectroscopy Based on Significance Testing in Partial Least Squares Regression

2000 ◽  
Vol 8 (2) ◽  
pp. 117-124 ◽  
Author(s):  
F. Westad ◽  
H. Martens
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Model Parameters ◽  
Least Squares Regression ◽  
Data Set ◽  
Number Of Segments ◽  
Better Than

A jack-knife based method for variable selection in partial least squares regression is presented. The method is based on significance tests of model parameters, in this paper applied to regression coefficients. The method is tested on a near infrared (NIR) spectral data set recorded on beer samples, correlated to extract concentration and compared to other methods with known merit. The results show that the jack-knife based variable selection performs as well or better than other variable selection methods do. Furthermore, results show that the method is robust towards various cross-validation schemes (the number of segments and how they are chosen).

scholarly journals Wood species identification from Atlantic forest by near infrared spectroscopy

2019 ◽  
Vol 28 (3) ◽  
pp. e015
Author(s):  
José-Henrique Camargo Pace ◽  
João-Vicente De Figueiredo Latorraca ◽  
Paulo-Ricardo Gherardi Hein ◽  
Alexandre Monteiro de Carvalho ◽  
Jonnys Paz Castro ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Atlantic Forest ◽  
Wood Species ◽  
Native Species ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Planted Forests ◽  
Good Ability

Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.

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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