| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1179464 | Chemometrics and Intelligent Laboratory Systems | 2014 | 7 Pages |
•L1-norm based regression for spectra signal multivariate scatter correction•The scatter is modeled directly as the fluctuation of the amplitude of the signal.•The accuracy improves much than MSC and EMSC.
As an efficient method for spectra correction, multivariate scatter correction (MSC) has recently received considerable attention due to the precision improvement of processed data. In general, the spectra approximate mean spectrum S¯ in least square framework. Unfortunately, the existing MSC methods have a limited capability in nonlinear component modeling. In this paper, we propose regularized multivariate scatter correction (RMSC), which has taken nonlinear components into MSC model as well as regularization function for the weight vector w. The weighted sum of mappings of observed spectrum is used to approximate the mean spectrum. By using gradient projection sparse representation, vector w is obtained for RMSC. Results show a substantial decrease in Root Mean Square Error of Prediction of quantitative analysis and improvement in classification precision.
