Precise high-throughput online near-infrared spectroscopy assay to determine key cell wall features associated with sugarcane bagasse digestibility

Abstract

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.

Accuracy of single and mix calibration as method to determine ruminant feedstuffs nutrient utilization using Near-Infrared Reflectance Spectroscopy

“…Looking at the results obtained in this study, it is evident that the accuracy between the reference value and the predicted data value shows high precision. This is in line with [6] which explains that the higher the value of R 2 is close to one, the higher the accuracy of a prediction. In addition to the R 2 value, the RPD also determines the effectiveness of the calibration model.…”

Pilot-Scale Pelleting Tests on High-Moisture Pine, Switchgrass, and Their Blends: Impact on Pellet Physical Properties, Chemical Composition, and Heating Values

“…In recent years, near infrared (NIR)-based high-throughput spectroscopic techniques have been developed to monitor the output of pelleting and other downstream processing of lignocellulosic biomass (Edmunds et al, 2018;Feng et al, 2018;Li et al, 2018;Li et al, 2021). NIR spectroscopy works based on the absorption, emission, reflection, and diffusereflection of light in the region of 800-2,500 nm (e.g., 12,500-4,000 cm −1 ) (Ozaki et al, 2017).…”

“…Multivariate prediction models could be built on the basis of on-, at-, or off-line NIR spectroscopic analysis and used for rapid, inexpensive characterization of changes in lignocellulosic feedstocks during thermochemical or physico-chemical conversion processes (Li et al, 2018;Hwang et al, 2021). Principal component analysis of online NIR spectra has been recently used to screen the seasonal variability of cellulose crystallinity and lignin content in sugarcane bagasse (Li et al, 2021). Similarly, off-line NIR spectroscopy was used to monitor regional differences in the composition of cellulose, hemicellulose, lignin, and ash of Jerusalem artichokes (Li et al, 2018).…”

Fungal deterioration of the bagasse storage from the harvested sugarcane

“…Six clean harvested stalks without leaf sheath were surface-sterilized and randomly crushed into bagasse using DM540-CPS. This system rapidly smashed sugarcane with a high-speed rotating blade without pressing or producing cane juice, which the stalks were immediately shredded using DM540 (IRBI Machines & Equipment Ltd, Brazil), blended, and transmitted by CPS (Cane presentation system, Bruker Optik GmbH, Germany) [ 42 ]. The cane bagasse of five groups was packed in 50 zip lock bags (500 g each), 40 randomly separated as two groups stored at 30 °C and 40 °C for 4 days.…”