Finally, the mesh-screened samples as dry matter were stored in a dry container until use. The main topic of this research was to build upon the laboratory-scale alkali-based conversion process developed by Li et al. A standard curve was plotted using d -glucose as the standard Xilong Scientific Co. Structural carbohydrates i. Calculations of cellulose, hemicellulose, and lignin content were performed according to Li et al. Cellulose was calculated from glucose content, while hemicellulose was calculated from the sum of xylose and arabinose content values.
Lignin was calculated from the sum of acid-soluble lignin and acid-insoluble lignin content values. All experiments were carried out in triplicate. Biomass digestibility was defined by accounting for the hexoses, pentoses, and total carbohydrates released from the soluble sugars extracted biomass feedstock after alkali-based pretreatment followed by enzymatic hydrolysis [ 11 ]. During enzymatic hydrolysis, each pretreated sample was mixed with 0.
Glucose and xylose released after alkali-based pretreatment followed by enzymatic hydrolysis were determined by HPLC as described above. Spectrometer control and data collection were conducted using TQ Analyst software ver.
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In order to correct spectra scatter, all spectra were first adjusted using multiplicative scatter correction. Next, the Savitzky—Golay smoothing filter and the first derivative were employed to reduce random noise and to resolve spectral peak overlap and eliminate linear baseline drift [ 23 ]. The purpose of the aforementioned corrections was to remove multiplicative and additive effects stemming from instrument settings or variations caused by the sample and environmental conditions [ 19 ].
After pretreatments, six principal component analysis models were developed using TQ Analyst software ver.
For a fair multivariate prediction, one of every five samples was sorted into validation sets using KS algorithms based on full spectra and two types of characteristic spectra; the remaining samples were used for generating calibration sets. Calibration and validation sets were compared in terms of both their chemical components and biomass digestibility.
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In addition, the NIRS plots distribution of calibration and validation sets was also compared using eighteen principal component analysis models in the present study. Based on the MPA-optimized spectra, sixteen PLSR multivariate calibrations were developed to predict soluble sugars, cellulose, hemicellulose, lignin, ash, and biomass digestibility hexoses, pentoses, and total carbohydrates using ChemDataSolution software ver.
In this study, JA stem feedstock quality specifications could be divided into two categories: chemical components soluble sugars, cellulose, hemicellulose, lignin, and ash and biomass digestibility hexoses, pentoses, and total carbohydrates. These quality specifications were used to calculate and compare the FQS values of JA samples collected from different geographic regions.
To address this challenge, a high-efficiency evaluation and selection model was developed based on grey relational grade analysis theory, which is an essential component of the grey system theory formulated by Julong Deng [ 16 ]. Both chemical components and biomass digestibility total carbohydrates released from pretreatment and subsequent enzymatic hydrolysis were assigned the same weight of 0.
One of every five samples was sorted into validation sets using KS algorithms based on full spectra and two characteristic spectra and the remaining samples were used for the calibration sets. In addition, the multiple coefficients of determination R 2 , the explained variation in the test set Q 2 , and the correct rate were employed to ascertain MPA enhancement of classification performance [ 21 ].
GHX organized this research as the laboratory chief. ZL collected the JA germplasm resources. ML and JW conducted chemical composition and biomass digestibility determination. ML and SH designed the lignocellulosic biomass feedstocks screening system and built all the discussed models. ML performed the statistical analysis and drafted figures and tables. All authors read and approved the final manuscript.
Ltd and Henan Tianguan Group Co. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Meng Li, Email: nc. Siyang He, Email: moc. Jun Wang, Email: moc. Zuxin Liu, Email: moc. National Center for Biotechnology Information , U. Journal List Biotechnol Biofuels v. Biotechnol Biofuels. Published online Dec Author information Article notes Copyright and License information Disclaimer.
Corresponding author. Received Oct 19; Accepted Dec The solid lines and dashed lines overlaid upon each histogram represent normal distributions and were used to embody the discrepancy between each histogram and normality. R 2 V represents the square of the correlation coefficients of the external validation subsets. Table S1. Feedstock quality grades of 59 Jerusalem artichoke accessions. Table S2.follow site
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A summary of NIR application in different biomass feedstocks for chemical components. Table S3. A summary of NIR application in different biomass feedstocks for biomass digestibility. Additional file 2. Raw NIRS data. Additional file 3. The procedure code of grey relational grade analysis.
Abstract Background High-throughput evaluation of lignocellulosic biomass feedstock quality is the key to the successful commercialization of bioethanol production. Results The distinct geographical distribution of JA accessions generated varied chemical composition as well as related biomass digestibility after soluble sugars extraction and mild alkali pretreatment. Electronic supplementary material The online version of this article Keywords: Jerusalem artichoke, Chemical composition, Chemical pretreatment, Biomass digestibility, Near-infrared spectroscopy, Grey relational grade analysis.
Background In recent years, fossil fuels consumption and greenhouse gas emissions have increased dramatically in step with rapid global industrialization, especially in China. Open in a separate window. Optimization of spectral variable selection and samples sets partitioning Judicious selection of spectral information is a crucial step for successful NIRS modeling, which not only permits the collection of strong informative variables but also removes interference due to uninformative variables [ 23 , 26 ]. Comprehensive assessment of feedstock quality score Based on chemical composition and biomass digestibility total carbohydrates released after pretreatment and subsequent enzymatic hydrolysis , the feedstock quality of tested JA accessions was comprehensively evaluated using the GRA model.
Discussion Evaluation and selection of ideal feedstock among bioenergy crops are necessary to enhance lignocellulosic biofuel production [ 37 , 38 ]. Conclusions In this study, 59 JA clone stems originating from six regions of China exhibited diverse chemical compositions, biomass digestibility, and variable NIRS results, which were applicable for statistical analysis and NIRS modeling.
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Methods Sample collection and preparation A total of 59 JA natural clones were collected nationwide from to Biomass components and digestibility analysis The main topic of this research was to build upon the laboratory-scale alkali-based conversion process developed by Li et al. Grey relational grade analysis In this study, JA stem feedstock quality specifications could be divided into two categories: chemical components soluble sugars, cellulose, hemicellulose, lignin, and ash and biomass digestibility hexoses, pentoses, and total carbohydrates.
Additional files Additional file 1: Fig. Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Contributor Information Meng Li, Email: nc.
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