High-throughput Evaluation Of Sugarcane Cell Wall Characteristics And Germplasm Resource Mining

The improvement of sugarcane cell wall structure is a promising strategy to enhance the bagasse digestibility to improve its prospects as a bioenergy crop.In this context,cellulose crystallinity(Cr I)and lignin are the key parameters that influence the saccharification efficiency.Therefore,this study was conducted to develop a high-throughput assay for online characterization of these cell wall features in sugarcane.Eventually,large-scale screening of optimal sugarcane varieties and high-throughput screening for bioenergy production in sugarcane will be achieved.A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019.A continuous variation distribution of near-infrared spectroscopy(NIRS)was observed among the sugarcane samples.Due to significant diversity of the cell wall features in the sampled population of the crop,seven high quality calibration models were developed through online NIRS calibration.All of the generated equations displayed coefficient of determination(R~2)values higher than 0.8 and high ratio performance deviation(RPD)values over 2.0 in calibration,internal cross validation,and external validation.Particularly,the equations for Cr I and the total lignin content exhibited the RPD values as high as 2.56 and 2.55,espectively,indicating their excellent prediction capacity.Furthermore,the offline NIRS assay was also performed.A comparable calibration was observed between the offline and online NIRS analyses,suggesting that both of the two strategies would be applicable for estimating cell wall characteristics.Nevertheless,as online NIRS assay offers greater advantages for large-scale screening jobs,it could be implied as a better option for high-throughput cell wall features prediction.On this basis,the best online NIRS model was used to evaluate the cell wall characteristics of 400 sugarcane germplasm resources.In different experimental fields,sugarcane with different genetic backgrounds was collected in different months,and the content of cellulose Cr I and lignin was predicted by the near-infrared model.The analysis of the results showed that the results between the three plots had a significant correlation.Through the analysis of the prediction results,high-quality biomass energy sugarcane varieties are finally screened out,which provides a reference for subsequent energy sugarcane research.This study,as a foremost attempt,explored an online NIRS assay for high-throughput assessment of key sugarcane cell wall attributes in terms of Cr I,lignin content,and its proportion in sugarcane.Consistent and precise calibration results were obtained in NIRS modeling;insinuating this strategy as a reliable approach for large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.