Issue 6, 2011

Improvement of the prediction ability of multivariate calibration by a method based on the combination of data fusion and least squares support vector machines

Abstract

This paper suggests a novel method named DF-LS-SVM, which is based on least squares support vector machines (LS-SVM) regression combined with data fusion (DF) to enhance the ability to extract characteristic information and improve the quality of the regression. Simultaneous multicomponent determination of Fe(III), Co(II) and Cu(II) was conducted for the first time by using the proposed method. Data fusion is a technique that integrates information from disparate sources to produce a single model or decision. The LS-SVM technique allows for learning a high-dimensional feature with fewer training data, and reduces the computational complexity by only requiring the solution of a set of linear equations instead of a quadratic programming problem. Experimental results showed that the DF-LS-SVM method was successful for simultaneous multicomponent determination even when severe overlap of spectra existed. The DF-LS-SVM method is an attractive and promising hybrid approach that combines the best properties of the two techniques. The results obtained from an additional test case, simultaneous differential pulse voltammetric determination of o-nitrophenol, m-nitrophenol and p-nitrophenol, also demonstrated that the DF-LS-SVM method performed somewhat better than LS-SVM and PLS methods.

Graphical abstract: Improvement of the prediction ability of multivariate calibration by a method based on the combination of data fusion and least squares support vector machines

Article information

Article type
Paper
Submitted
23 Jun 2010
Accepted
17 Dec 2010
First published
18 Jan 2011

Analyst, 2011,136, 1252-1261

Improvement of the prediction ability of multivariate calibration by a method based on the combination of data fusion and least squares support vector machines

S. Ren and L. Gao, Analyst, 2011, 136, 1252 DOI: 10.1039/C0AN00433B

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