NDLI: Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver

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Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver

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Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver

Content Provider	Royal Society of Chemistry (RSC)
Author	Chen, Xiaojing Guan, Xiaochun Zhu, Xiangou Wu, Guichu Wu, Di
Copyright Year	2011
Abstract	The potential of using partial least square based uninformative variable elimination algorithm (UVEPLS) on successive projections algorithm (SPA) for spectral multivariable selection was evaluated. A case study was done on the visible and shortwave-near infrared (Vis-SNIR) spectroscopy for the rapid and non-destructive determination of protein content in dried laver. Three calibration algorithms, namely multiple linear regression (MLR), partial least square regression (PLS) and least-square support vector machine (LS-SVM), were used for the model establishment based on the selected variables of SPA, UVEPLS and UVEPLS-SPA, respectively. A total of 175 samples were prepared for the calibration (n = 117) and prediction (n = 58) sets. The performances of different pretreatments were compared. Both linear calibration algorithms of MLR and PLS and non-linear calibration algorithms of LS-SVM with linear kernel and RBF kernel obtained similar results based on certain variable selection strategies of SPA, UVEPLS and UVEPLS-SPA. The average improvement percentage of RPD values of four calibration algorithms was 38.66% by calculating SPA on UVEPLS processed variables. Therefore there was much improvement of using UVEPLS on SPA spectral multivariable selection with both linear and nonlinear calibration algorithms in this case. Moreover, the RPD values of both linear and non-linear models based on the thirteen selected variables of UVEPLS-SPA show that coarse quantitative predictions of the protein determination in dried laver is possible based on Vis-SNIR spectra. We hope that the results obtained in this study will help both further chemometric (multivariate selection and calibration analysis) investigations and investigations in the sphere of applied vibrational (Near infrared, Mid-infrared and Raman) spectroscopy of sophisticated multicomponent systems.
Starting Page	1790
Ending Page	1796
Page Count	7
File Format	HTM / HTML PDF
ISSN	17599660
Volume Number	3
Issue Number	8
Journal	Analytical Methods
DOI	10.1039/c1ay05075c
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Infrared spectroscopy Spectroscopy Variable elimination Reproducing kernel Hilbert space Feature selection Regression analysis Support vector machine RPD machine gun Algorithm Protein Kernel method RBF Raman
Content Type	Text
Resource Type	Article
Subject	Analytical Chemistry Engineering Chemical Engineering

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