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Rapid Determination of Cellulose and Hemicellulose Contents in Corn Stover Using Near-Infrared Spectroscopy Combined with Wavelength Selection
| Content Provider | MDPI |
|---|---|
| Author | Wang, Na Feng, Jinrui Li, Longwei Liu, Jinming Sun, Yong |
| Copyright Year | 2022 |
| Description | The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment. |
| Starting Page | 3373 |
| e-ISSN | 14203049 |
| DOI | 10.3390/molecules27113373 |
| Journal | Molecules |
| Issue Number | 11 |
| Volume Number | 27 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-24 |
| Access Restriction | Open |
| Subject Keyword | Molecules Near-infrared Spectroscopy Cellulose and Hemicellulose Contents Backward Partial Least Squares Competitive Adaptive Reweighted Sampling Genetic Simulated Annealing Algorithm |
| Content Type | Text |
| Resource Type | Article |
