High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using...

Due to its chemical composition and abundance, lignocellulosic biomass is an attractive feedstock source for global bioenergy production. However, chemical composition variations interfere with the success of any single methodology for efficient bioenergy extraction from diverse lignocellulosic biomass sources. Although chemical component distributions could guide process design, they are difficult to obtain and vary widely among lignocellulosic biomass types. Therefore, expensive and laborious "one-size-fits-all" processes are still widely used. Here, a non-destructive and rapid analytical technology, near-infrared spectroscopy (NIRS) coupled with multivariate calibration, shows promise for addressing these challenges. Recent advances in molecular spectroscopy analysis have led to methodologies for dual-optimized NIRS using sample subset partitioning and variable selection, which could significantly enhance the robustness and accuracy of partial least squares (PLS) calibration models. Using this methodology, chemical components and theoretical ethanol yield (TEY) values were determined for 70 sweet and 77 biomass sorghum samples from six sweet and six biomass sorghum varieties grown in 2013 and 2014 at two study sites in northern China.

Chemical components and TEY of the 147 bioenergy sorghum samples were initially analyzed and compared using wet chemistry methods. Based on linear discriminant analysis, a correct classification assignment rate (either sweet or biomass type) of 99.3% was obtained using 20 principal components. Next, detailed statistical analysis demonstrated that partial optimization using sample set partitioning based on joint X-Y distances (SPXY) for sample subset partitioning enhanced the robustness and accuracy of PLS calibration models. Finally, comparisons between five dual-optimized strategies indicated that competitive adaptive reweighted sampling coupled with the SPXY (CARS-SPXY) was the most efficient and effective method for improving predictive performance of PLS multivariate calibrations.

As a dual-optimized methodology, sample subset partitioning combined with variable selection is an efficient and straightforward strategy to enhance the accuracy and robustness of NIRS models. This knowledge should facilitate generation of improved lignocellulosic biomass feedstocks for bioethanol production. Moreover, methods described here should have wider applicability for use with feedstocks incorporating multispecies biomass resource streams.