The characteristic spectral selection method based on forward and backward interval partial least squares
Access Status
Authors
Date
2016Type
Metadata
Show full item recordCitation
Source Title
ISSN
School
Collection
Abstract
In the near-infrared spectroscopy, the Forward Interval Partial Least Squares (FiPLS) and Backward Interval Partial Least Squares (BiPLS) are commonly used modeling methods, which are based on the wavelength variable selection. These methods are usually of high prediction accuracy, but are strongly characteristic of greedy search, which causes that the intervals selected are not good enough to indicate the analyte information. To solve the problem, a spectral characteristic intervals selection strategy (FB-iPLS) based on the combination of FiPLS and BiPLS is proposed. On the basis of spectral segmentation, both FiPLSs are used to select useful intervals, and BiPLS is used to delete useless intervals, so as to perform the selection and deletion of the characteristic variables alternatively, which conducts a two-way choice of the target characteristic variables, and is used to improve the robustness of the model. The experiments on determining the ethanol concentration in pure water are conducted by modeling with FiPLS, BiPLS and the proposed method. Since different size of intervals will affect the result of the model, the experiments here will also examine the model results with different intervals of these three models. When the spectrum is divided into 60 segments, the FB-iPLS method obtains the best prediction performance. The correlation coefficients (r) of the calibration set and validation set are 0.967 7 and 0.967 0 respectively, and the cross-validation root mean square errors (RMSECV) are 0.088 8 and 0.057 1, respectively. Compared with FiPLS and BiPLS, the overall prediction performance of the proposed model is better. The experiments show that the proposed method can further improve the predictive performance of the model by resolving the greedy search feature against BiPLS and FiPLS, which is more efficient for and representative of the selection of characteristic intervals.
Related items
Showing items related by title, author, creator and subject.
-
Xu, C.; Yin, YanYan; Liu, F. (2016)Based on Gaussian Process (GP), a wavelength selection algorithm named Synergy Interval Gaussian Process (siGP) model is proposed in this paper by using near infrared spectroscopy technology. Full spectrum is divided into ...
-
Ren, D.; Qu, F.; Lv, K.; Zhang, Z.; Xu, Honglei; Wang, X. (2015)When the technique of boosting regression is applied to near-infrared spectroscopy, the full spectrum of samples are generally used to perform partial least squares (PLS) modeling. However, there is a large amount of ...
-
Lo, Johnny Su Hau (2011)The determination of the zenith wet delay (ZWD) component can be a difficult task due to the dynamic nature of atmospheric water vapour. However, precise estimation of the ZWD is essential for high-precision Global ...