Citation: CHENG Jie-Hong, CHEN Zheng-Guang. Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm[J]. Chinese Journal of Analytical Chemistry, ;2021, 49(8): 1402-1409. doi: 10.19756/j.issn.0253-3820.201307 shu

Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm

CHENG Jie-Hong ,
CHEN Zheng-Guang ^,

College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China

Corresponding author: CHEN Zheng-Guang, ruzee@sina.com
Received Date: 25 May 2020
Revised Date: 18 January 2021

Fund Project: Supported by the National Key R&D Program of China (No.2016YFD0701300), the key Scientific Research Program of Heilongjiang Agricultural Reclamation Bureau (No.HKKYZD190804), and the Scientific Research Projects of Basic Scientific Research Funds in Heilongjiang Provincial Colleges and Universities (No.ZRCPY201913).

As a wavelength selection algorithm, successive projection algorithm (SPA) is used in the quantitative analysis of near infrared spectroscopy to simplify the model complexity and improve the prediction accuracy. According to the principle of SPA algorithm, SPA method can only ensure that there is low redundancy between the two wavelengths selected by two adjacent projections, but it does not guarantee that the selected variables must be effective variables, that is to say, the subset of variables screened by SPA may contain some uninformation variables or even interfering variables. We extract effective variables (i.e. strong and weak information variables) by iteratively retains informative variables (IRIV), and then select wavelength by SPA based on effective variables, so as to solve the problem that the selected variables by SPA may contain uninformation and interfering variables, and improve the prediction accuracy of the model. The improved IRIV-SPA is applied to processing of two groups of public NIR spectral data. After the wavelengths are selected, the MLR model is established and compared with the FULL-PLSR model and some other commonly used high-performance wavelength selection methods (SPA, IRIV, RF) to prove the effectiveness of the improved IRIV-SPA algorithm. The results show that the improved IRIV-SPA-MLR model has the best prediction accuracy under two opening test datasets, which greatly improves the prediction accuracy and simplifies the complexity of the model compared with other algorithms. The improved IRIV-SPA can achieve efficient dimensionality reduction and is an effective wavelength selection algorithm.
- Near-infrared spectroscopy,
- Wavelength selection,
- Multivariate calibration,
- Successive projection algorithm,
- Iteratively retains informative variables
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