Citation: XIE Huan, CHEN Zheng-Guang. Application of Genetic Simulated Annealing Algorithm in Detection of Corn Straw Cellulose[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(12): 1987-1994. doi: 10.19756/j.issn.0253-3820.191131 shu

Application of Genetic Simulated Annealing Algorithm in Detection of Corn Straw Cellulose

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

Received Date: 11 March 2019
Revised Date: 5 November 2019

Fund Project: This work was supported by the National Key R&D Program of China (No.2016YFD0701300) and Heilongjiang Bayi Agricultural University Innovative Research Team Foundation (No. TDJH201807).

The near-infrared spectroscopy was used to predict the content of corn straw cellulose. According to the characteristics of high-dimensional and high correlation of near-infrared spectroscopy, the selection of characteristic wavelengths was discussed when using partial least squares (PLS) prediction model for corn straw cellulose. Firstly, the influence of interval partition number of synergy interval partial least squares (SIPLS) and backward interval partial least squares (BIPLS) on the algorithm effect was discussed. On the basis of SIPLS and BIPLS, genetic simulated annealing algorithm (GSAA) was used to filter the second characteristic wavelength, which could further improve the prediction accuracy and modeling efficiency of the model. The results showed that the three algorithms could improve the prediction accuracy of the model, but the effect of SIPLS and BIPLS was greatly affected by the partition number. Although the root mean square error of prediction (RMSEP) of BIPLS model was the smallest, the number of variables selected was up to 485, which affected the modeling efficiency of the model. On the basis of SIPLS and BIPLS, GSAA was used to filter the second characteristic wavelength. Compared with BIPLS, the RMSEP of BIPLS-GSAA model was slightly increased, but its input variables were reduced to 134, and the main component fraction of the model was also reduced from 11 to 10. Compared with SIPLS, the input variables of SIPLS-GSAA model were only 34, and the prediction accuracy was improved. Its RMSEP was next to that of BIPLS model. The experimental results showed that SIPLS-GSAA model had the best prediction effect. Therefore, the second screening of spectral data based on GSAA on SIPLS and BIPLS could not only simplify the input of the model, but also effectively improve the prediction ability of the model.
- Near-infrared Spectroscopy,
- Corn Straw,
- Cellulose,
- Synergy interval partial least square,
- Genetic simulated annealing algorithm
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