MOESM2 of High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using dual-optimized partial least squares calibration models

Abstract

Additional file 2: Figure A1. Plots of predicted versus measured values of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on CARS-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A2. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on SR-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A3. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on VIP-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A4. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on MC-UVE-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets. Figure A5. Plots of predicted versus measured value of parameters. Soluble sugar (a), cellulose (b), hemicellulose (c), lignin (d), ash (e), and theoretical ethanol yield (f) for the external validation subsets based on UVE-SPXY dual-optimized PLS models. The $${\text{R}}_{\text{V}}^{2}$$ R V 2 represents the square of the correlation coefficients of the external validation subsets