Journal ArticleDOI
Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy
Lars Nørgaard,A. Saudland,J. Wagner,Jesper Pram Nielsen,Lars Kristian Munck,Søren Balling Engelsen +5 more
TLDR
In this article, a graphically oriented local modeling procedure called interval partial least squares (i PLS) is presented for use on spectral data, which is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR).Abstract:
A new graphically oriented local modeling procedure called interval partial least-squares (i PLS) is presented for use on spectral data. The i PLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of i PLS (r=0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by i PLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and i PLS is still able to utilize the first-order advantage. Index Headings: Interval PLS; Variable selection; NIR, Principal variables; Forward stepwise selection; Recursively weighted regression; Beer; Extract.read more
Citations
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Journal ArticleDOI
A review of variable selection methods in Partial Least Squares Regression
TL;DR: A review of available methods for variable selection within one of the many modeling approaches for high-throughput data, Partial Least Squares Regression, to get an understanding of the characteristics of the methods and to get a basis for selecting an appropriate method for own use.
Journal ArticleDOI
Variables selection methods in near-infrared spectroscopy.
TL;DR: This review focuses on the variable selection methods in NIR spectroscopy with some classical approaches and sophisticated methods such as successive projections algorithm (SPA), uninformative variable elimination (UVE) and elaborate search-based strategies.
Journal ArticleDOI
icoshift: A versatile tool for the rapid alignment of 1D NMR spectra
TL;DR: The icoshift program presented here is an open source and highly efficient program designed for solving signal alignment problems in metabonomic NMR data analysis and is demonstrated to be faster than similar methods found in the literature making full-resolution alignment of large datasets feasible and thus avoiding down-sampling steps such as binning.
Journal ArticleDOI
Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.
TL;DR: Last decade's advances and modern aspects of near infrared spectroscopy are critically examined and reviewed in order to understand why the technique has found intensive application in the most diverse and modern areas of analytical importance during the last ten years.
Journal ArticleDOI
Variable selection in regression—a tutorial
C. M. Andersen,Rasmus Bro +1 more
TL;DR: The emphasis in this paper is on how to use variable selection in practice and avoid the most common pitfalls.
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