Title
Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration.
Abstract
One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20-200 mu g/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity.
Year
DOI
Venue
2016
10.3390/s16060827
SENSORS
Keywords
Field
DocType
partial least squares regression,wavelength selection,multivariate calibration,ultraviolet-visible absorbance spectra,local algorithm
Analytical chemistry,Partial least squares regression,Local regression,Multivariate calibration,Local algorithm,Engineering,Calibration,Wavelength
Journal
Volume
Issue
ISSN
16
6.0
1424-8220
Citations 
PageRank 
References 
0
0.34
3
Authors
6
Name
Order
Citations
PageRank
Haitao Chang100.34
Lianqing Zhu235.13
Xiaoping Lou302.03
Xiaochen Meng400.68
Yangkuan Guo500.34
Zhongyu Wang600.34