To understand the structural changes of lignin after soda-AQ and kraft pretreatment, milled straw lignin, black liquor lignin and residual lignin extracted from wheat straw were characterized by FT-IR, UV, GPC and NMR. The results showed that the main lignin linkages were β-aryl ether substructures (β-O-4'), followed by phenylcoumaran (β-5') and resinol (β-β') substructures, while minor content of spirodienone (β-1'), dibenzodioxocin (5-5') and α,β-diaryl ether linkages were detected as well. After pretreatment, most lignin inter-units and lignin-carbohydrate complex (LCC) linkages were degraded and dissolved in black liquor, with minor amount left in residual pretreated biomass. In addition, through quantitative (13)C and 2D-HSQC NMR spectral analysis, lignin and LCC were found to be more degraded after kraft pretreatment than soda-AQ pretreatment. Furthermore, the subsequent enzymatic hydrolysis results showed that more cellulose in wheat straw was converted to glucose after kraft pretreatment, indicating that LCC linkages were important in the enzymatic hydrolysis process.
Background
To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture.
Methods
The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.
Results
The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.
Conclusions
The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
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