An NIRS-based assay of chemical composition and biomass digestibility for rapid selection of Jerusalem artichoke clones

Abstract

BackgroundHigh-throughput evaluation of lignocellulosic biomass feedstock quality is the key to the successful commercialization of bioethanol production. Currently, wet chemical methods for the determination of chemical composition and biomass digestibility are expensive and time-consuming, thus hindering comprehensive feedstock quality assessments based on these biomass specifications. To find the ideal bioethanol feedstock, we perform a near-infrared spectroscopic (NIRS) assay to rapidly and comprehensively analyze the chemical composition and biomass digestibility of 59 Jerusalem artichoke (Helianthus tuberosus L., abbreviated JA) clones collected from 24 provinces in six regions of China.ResultsThe distinct geographical distribution of JA accessions generated varied chemical composition as well as related biomass digestibility (after soluble sugars extraction and mild alkali pretreatment). Notably, the soluble sugars, cellulose, hemicellulose, lignin, ash, and released hexoses, pentoses, and total carbohydrates were rapidly and perfectly predicted by partial least squares regression coupled with model population analyses (MPA), which exhibited significantly higher predictive performance than controls. Subsequently, grey relational grade analysis was employed to correlate chemical composition and biomass digestibility with feedstock quality score (FQS), resulting in the assignment of tested JA clones to five feedstock quality grades (FQGs). Ultimately, the FQGs of JA clones were successfully classified using partial least squares-discriminant analysis model coupled with MPA, attaining a significantly higher correct rate of 97.8% in the calibration subset and 91.1% in the validation subset.ConclusionsBased on the diversity of JA clones, the present study has not only rapidly and precisely examined the biomass composition and digestibility with MPA-optimized NIRS models but has also selected the ideal JA clones according to FQS. This method provides a new insight into the selection of ideal bioethanol feedstock for high-efficiency bioethanol production.Electronic supplementary materialThe online version of this article (10.1186/s13068-018-1335-1) contains supplementary material, which is available to authorized users.

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

“…Multivariate predictive models constructed based on at-line or inline NIR spectroscopic analysis could be a valuable resource for high-throughput and inexpensive characterization of chemical and physical changes in lignocellulosic feedstocks during thermochemical or physico-chemical conversion processes (Li et al, 2018;Hwang et al, 2021). Previous research has shown that NIR analysis in the 900-1700 nm region could be coupled with chemometric approaches to predict the cellulose (R 2 0.92), hemicellulose (R 2 0.84), and lignin (R 2 0.71) contents of various types of lignocellulosic pellets like corn stover, rice straw, pine, mahogany, and rubber wood (Feng et al, 2018).…”

“…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).…”

“…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).…”

“…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). The studies on high-moisture pelleting of southern yellow pine (SYP), switchgrass (SG), and their blends using a pilot-scale pellet mill and data on understanding the quality changes during pelleting using on-, at-, or off-line NIR spectroscopy are not available in the literature.…”

Microbial biotechnological approaches: renewable bioprocessing for the future energy systems

“…isoprenoid alcohol (IPA) pathway was used that focuses on the synthesis and subsequent IPAs phosphorylation. 0.6 g/L [ 48 ] Gaseous products Hydrogen Thermoanaerobacterium aotearoense Rice straw Deletion of Fd:NADP + oxidoreductase (nfnAB) 0.381–0.419 g/L [ 49 ] Hydrogen Escherichia coli Xylose and Glucose Deletion in gene ptsG (phosphotransferase system) for as well as ldhA and frdD 0.284 g/L [ 50 ] Hydrogen Chlamydomonas reinhardtii CO 2 Repression of the psbA (PSII D1 protein gene) 60% enhancement in hydrogen production [ 51 ] …”