Citation: XU Yong-Hua, WANG Na, LIU Jin-Ming. Research on Rapid Determination of Lignocellulosic Contents in Corn Stover Using Near Infrared Spectroscopy Based on Spectral Intervals Selection[J]. Chinese Journal of Analytical Chemistry, ;2022, 50(10): 1587-1596. doi: 10.19756/j.issn.0253-3820.221239 shu

Research on Rapid Determination of Lignocellulosic Contents in Corn Stover Using Near Infrared Spectroscopy Based on Spectral Intervals Selection

XU Yong-Hua ¹ ,
WANG Na ² ,
LIU Jin-Ming ^2,3,,

1.
School of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
2.
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China;
3.
National Coarse Cereals Engineering Research Center, Daqing 163319, China

Corresponding author: LIU Jin-Ming, jinmingliu2008@126.com
Received Date: 15 May 2022
Revised Date: 2 July 2022

Fund Project: Supported by the National Natural Science Foundation of China (No.52076034), the Heilongjiang Province Science and Technology Plan, Provincial Academy Science and Technology Cooperation Project (No.YS20B01) and the Scientific Research Foundation for Talent of Heilongjiang Bayi Agricultural University (No.XDB202006).

The contents of lignocellulosic components (including cellulose, hemicellulose and lignin) have an important influence on the methane yield of anaerobic digestion (AD) with corn stover (CS) as feedstocks in biogas industry. Aiming at the time-consuming and high-cost issues of traditional chemical analytical techniques, the feasibility of near infrared spectroscopy (NIRS) combined with chemometrics methods to measure the contents of lignocellulose in corn stover was analyzed in this work. To improve the detection accuracy and efficiency of NIRS regressive model, the genetic simulated annealing interval support vector machine (GSA-iSVM) was constructed using genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS) and support vector machine (SVM), which was used for synchronous optimization of the NIRS characteristic intervals and SVM parameters. By comparison with the modeling performance of the characteristic spectral intervals selected by backward interval partial least squares and genetic simulated annealing interval partial least squares (GSA-iPLS), it was found that the calibration model for cellulose and lignin established by GSA-iSVM had the best predicted accuracy, and that of hemicellulose established by GSA-iPLS performed best. For the validation set, the determination coefficients of prediction, root mean squared error of prediction and residual predictive deviation of the best calibration models were 0.910, 0.881% and 3.283 for cellulose; 0.990, 0.707% and 10.235 for hemicellulose; and 0.939, 0.249% and 4.270 for lignin, respectively. The results indicated that NIRS coupled with characteristic intervals intelligent selection of GSA could be used as a reliable alternative strategy to measure contents of lignocellulosic components in the pretreated CS in AD process.
- Corn stover,
- Lignocellulose,
- Near infrared spectroscopy,
- Characteristic intervals,
- Partial least squares,
- Support vector machine
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