首页 » 文章 » 文章详细信息
BioMed Research International Volume 2018 ,2018-08-12
Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features
Research Article
Le Gao 1 Shulin Chen 1 Dongyuan Zhang 1
Show affiliations
DOI:10.1155/2018/9167508
Received 2018-01-16, accepted for publication 2018-05-31, Published 2018-05-31
PDF
摘要

The classic assay for a large population biomass is time-consuming, labor intensive, and chemically expensive. This paper would find out a rapid assay for predicting biomass digestibility from biomass structural features without hydrolysis. We examined the 62 representative corn stover accessions that displayed a diverse cell-wall composition and varied biomass digestibility. Correlation analysis was firstly to detect effects of cell-wall compositions and wall polymer features on corn stover digestibility. Based on the dependable relationship of structural features and digestibility, a neural networks model has been developed and successfully predicted the corn stover saccharification based on the features without enzymatic hydrolysis. The actual measured and net-simulated predicted corn stover saccharification had good results as mean square error of 1.80E-05, coefficient of determination of 0.942 and average relative deviation of 3.95. The trained networks satisfactorily predicted the saccharification results based on the features of corn stover. Predicting the corn stover saccharification without hydrolysis will reduce capital and operational costs for corn stover purchasing and storage.

授权许可

Copyright © 2018 Le Gao et al. 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

通讯作者

Dongyuan Zhang.Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, cas.cn.zhang_dy@tib.cas.cn

推荐引用方式

Le Gao,Shulin Chen,Dongyuan Zhang. Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features. BioMed Research International ,Vol.2018(2018)

您觉得这篇文章对您有帮助吗?
分享和收藏
0

是否收藏?

参考文献
[1] M. E. Himmel, S. Ding, D. K. Johnson, W. S. Adney. et al.(2007). Biomass recalcitrance: engineering plants and enzymes for biofuels production. Science.315(5813):804-807. DOI: 10.1038/nature07190.
[2] Z. Wu, M. Zhang, L. Wang, Y. Tu. et al.(2013). Biomass digestibility is predominantly affected by three factors of wall polymer features distinctive in wheat accessions and rice mutants. Biotechnology for Biofuels.6(1):183. DOI: 10.1038/nature07190.
[3] R. Gaur, R. Agrawal, R. Kumar, E. Ramu. et al.(2015). Evaluation of recalcitrant features impacting enzymatic saccharification of diverse agricultural residues treated by steam explosion and dilute acid. RSC Advances.5(75):60754-60762. DOI: 10.1038/nature07190.
[4] G. Papa, P. Varanasi, L. Sun, G. Cheng. et al.(2012). Exploring the effect of different plant lignin content and composition on ionic liquid pretreatment efficiency and enzymatic saccharification of Eucalyptus globulus L. mutants. Bioresource Technology.117:352-359. DOI: 10.1038/nature07190.
[5] T. Arioli, L. C. Peng, A. S. Betzner, J. Burn. et al.(1998). Molecular analysis of cellulose biosynthesis in. Science.279(5351):717-720. DOI: 10.1038/nature07190.
[6] M.-Q. Fan, F. Xu, L.-X. Sun. (2007). Studies on hydrogen generation characteristics of hydrolysis of the ball milling Al-based materials in pure water. International Journal of Hydrogen Energy.32(14):2809-2815. DOI: 10.1038/nature07190.
[7] J. Huang, T. Xia, A. Li, B. Yu. et al.(2012). A rapid and consistent near infrared spectroscopic assay for biomass enzymatic digestibility upon various physical and chemical pretreatments in Miscanthus. Bioresource Technology.121:274-281. DOI: 10.1038/nature07190.
[8] J. P. O'Dwyer, L. Zhu, C. B. Granda, V. S. Chang. et al.(2008). Neural network prediction of biomass digestibility based on structural features. Biotechnology Progress.24(2):283-292. DOI: 10.1038/nature07190.
[9] M. Baucher, M. A. Bernard-Vailhé, B. Chabbert, J.-M. Besle. et al.(1999). Down-regulation of cinnamyl alcohol dehydrogenase in transgenic alfalfa ( L.) and the effect on lignin composition and digestibility. Plant Molecular Biology.39(3):437-447. DOI: 10.1038/nature07190.
[10] O. Giustolisi. (2004). Sparse solution in training artificial neural networks. Neurocomputing.56(1-4):285-304. DOI: 10.1038/nature07190.
[11] S. Kim, M. T. Holtzapple. (2006). Effect of structural features on enzyme digestibility of corn stover. Bioresource Technology.97(4):583-591. DOI: 10.1038/nature07190.
[12] N. Xu, W. Zhang, S. Ren, F. Liu. et al.(2012). Hemicelluloses negatively affect lignocellulose crystallinity for high biomass digestibility under NaOH and H2SO4 pretreatments in Miscanthus. Biotechnology for Biofuels.5(1):58. DOI: 10.1038/nature07190.
[13] J. Ralph, K. Lundquist, G. Brunow, F. Lu. et al.(2004). Lignins: Natural polymers from oxidative coupling of 4-hydroxyphenyl- propanoids. Phytochemistry Reviews.3(1-2):29-60. DOI: 10.1038/nature07190.
[14] K. E. Achyuthan, A. M. Achyuthan, P. D. Adams, S. M. Dirk. et al.(2010). Supramolecular self-assembled chaos: Polyphenolic lignin's barrier to cost-effective lignocellulosic biofuels. Molecules.15(12):8641-8688. DOI: 10.1038/nature07190.
[15] R. Gama, J. S. Van Dyk, M. H. Burton, B. I. Pletschke. et al.(2017). Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace. 3 Biotech.7(2). DOI: 10.1038/nature07190.
[16] H. E. Grethlein. (1985). The effect of pore size distribution on the rate of enzymatic hydrolysis of cellulosic substrates. Bio/Technology.3(2):155-160. DOI: 10.1038/nature07190.
[17] K. Grohmann, D. J. Mitchell, M. E. Himmel, B. E. Dale. et al.(1989). The role of ester groups in resistance of plant cell wall polysaccharides to enzymatic hydrolysis. Applied Biochemistry and Biotechnology.20-21(1):45-61. DOI: 10.1038/nature07190.
[18] B. H. A. Sluiter, R. Ruiz, C. Scarlata. (2008). Determination of structural carbohydrates and lignin in biomass. NREL/TP-510-42618:510-42618. DOI: 10.1038/nature07190.
[19] E. M. Rubin. (2008). Genomics of cellulosic biofuels. Nature.454(7206):841-845. DOI: 10.1038/nature07190.
[20] L. Gao, D. Li, F. Gao, Z. Liu. et al.(2015). Hydroxyl radical-aided thermal pretreatment of algal biomass for enhanced biodegradability. Biotechnology for Biofuels.8(1). DOI: 10.1038/nature07190.
[21] Y.-H. P. Zhang, L. R. Lynd. (2004). Toward an aggregated understanding of enzymatic hydrolysis of cellulose: noncomplexed cellulase systems. Biotechnology and Bioengineering.88(7):797-824. DOI: 10.1038/nature07190.
[22] W. Zhang, Z. Yi, J. Huang, F. Li. et al.(2013). Three lignocellulose features that distinctively affect biomass enzymatic digestibility under NaOH and H2SO4 pretreatments in Miscanthus. Bioresource Technology.130:30-37. DOI: 10.1038/nature07190.
[23] Y. Pei, Y. Li, Y. Zhang, C. Yu. et al.(2016). G-lignin and hemicellulosic monosaccharides distinctively affect biomass digestibility in rapeseed. Bioresource Technology.203:325-333. DOI: 10.1038/nature07190.
[24] H. V. Scheller, P. Ulvskov. (2010). Hemicelluloses. Annual Review of Plant Biology.61(1):263-289. DOI: 10.1038/nature07190.
[25] P. Bansal, M. Hall, M. J. Realff, J. H. Lee. et al.(2010). Multivariate statistical analysis of X-ray data from cellulose: A new method to determine degree of crystallinity and predict hydrolysis rates. Bioresource Technology.101(12):4461-4471. DOI: 10.1038/nature07190.
[26] L. Sun, P. Varanasi, F. Yang, D. Loqué. et al.(2012). Rapid determination of syringyl: Guaiacyl ratios using FT-Raman spectroscopy. Biotechnology and Bioengineering.109(3):647-656. DOI: 10.1038/nature07190.
[27] J.-Y. Park, M. Kang, J. S. Kim, J.-P. Lee. et al.(2012). Enhancement of enzymatic digestibility of Eucalyptus grandis pretreated by NaOH catalyzed steam explosion. Bioresource Technology.123:707-712. DOI: 10.1038/nature07190.
文献评价指标
浏览 26次
下载全文 1次
评分次数 0次
用户评分 0.0分
分享 0次