Background Sugarcane is one of the most crucial energy crops that produces high yields of sugar and lignocellulose. The cellulose crystallinity index (CrI) and lignin are the two kinds of key cell wall features that account for lignocellulose saccharification. Therefore, high-throughput screening of sugarcane germplasm with excellent cell wall features is considered a promising strategy to enhance bagasse digestibility. Recently, there has been research to explore near-infrared spectroscopy (NIRS) assays for the characterization of the corresponding wall features. However, due to the technical barriers of the offline strategy, it is difficult to apply for high-throughput real-time analyses. This study was therefore initiated to develop a high-throughput online NIRS assay to rapidly detect cellulose crystallinity, lignin content, and their related proportions in sugarcane, aiming to provide an efficient and feasible method for sugarcane cell wall feature evaluation. Results A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of the near-infrared spectrum was observed among these collections. Due to the very large diversity of CrI and lignin contents detected in the collected sugarcane samples, seven high-quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination (R2) values greater than 0.8 and high ratio performance deviation (RPD) values of over 2.0 in calibration, internal cross-validation, and external validation. Remarkably, the equations for CrI and total lignin content exhibited RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. An offline NIRS assay was also performed. Comparable calibration was observed between the offline and online NIRS analyses, suggesting that both strategies would be applicable to estimate cell wall characteristics. Nevertheless, as online NIRS assays offer tremendous advantages for large-scale real-time screening applications, it could be implied that they are a better option for high-throughput cell wall feature prediction. Conclusions This study, as an initial attempt, explored an online NIRS assay for the high-throughput assessment of key cell wall features in terms of CrI, lignin content, and their proportion in sugarcane. Consistent and precise calibration results were obtained with NIRS modeling, insinuating this strategy as a reliable approach for the large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.

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Background: Sugarcane (Saccharum officinarum L.) is the core crop for sugar and bioethanol production over the world. A major problem in sugarcane production is stalk lodging due to weak mechanical strength. Since there are no efficient methods for determining stalk mechanical strength in sugarcane, genetic approaches for improving stalk lodging resistance are largely limited. This study was designed to use near-infrared spectroscopy (NIRS) calibration assay to accurately assess mechanical strength on a high-throughput basis for the first time. Results: Hundreds of sugarcane germplasms were harvested at the mature stage in the year of 2019 and 2020. In terms of determining rind penetrometer resistance (RPR) and breaking force, large variations of mechanical strength were found in the sugarcane stalk internodes, based on well-established laboratory measurements. Through partial least square regression analysis, two online NIRS models were established with a high coefficient of determination (R2) and the ratio of prediction to deviation (RPD) values during calibration, internal cross-validation, and external validation. Remarkably, the equation for RPR exhibited R2 and RPD values as high as 1.00 and 17.7, as well as showing relatively low root mean square error values at 0.44 N mm-2 during global modeling, demonstrating excellent predictive performance. Conclusions: This study delivered a successful attempt for rapid and precise prediction of mechanical strength in sugarcane stalk by NIRS assay. By using these established models, genetic improvements could be made to phenotyping jobs for large-scale sugarcane germplasm.

Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond.

Background: 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 (CrI) 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. Results: 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 (R2) 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 CrI and the total lignin content exhibited the RPD values as high as 2.56 and 2.55, respectively, 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. Conclusions: This study, as a foremost attempt, explored an online NIRS assay for high-throughput assessment of key sugarcane cell wall attributes in terms of CrI, 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.