Preparation of two strains of lignin-degrading bacteria solid inoculum

LI Yalin; LI Suyan; SUN Xiangyang; HAO Dan; CAI Linlin; CHANG Xiaotong

doi:10.11833/j.issn.2095-0756.20210311

Volume 39 Issue 2

Mar. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Zhejiang A&F University > 2022 > 39(2): 364-371

LI Yalin, LI Suyan, SUN Xiangyang, HAO Dan, CAI Linlin, CHANG Xiaotong. Preparation of two strains of lignin-degrading bacteria solid inoculum[J]. Journal of Zhejiang A&F University, 2022, 39(2): 364-371. doi: 10.11833/j.issn.2095-0756.20210311

Citation:

LI Yalin, LI Suyan, SUN Xiangyang, HAO Dan, CAI Linlin, CHANG Xiaotong. Preparation of two strains of lignin-degrading bacteria solid inoculum[J]. Journal of Zhejiang A&F University, 2022, 39(2): 364-371. doi: 10.11833/j.issn.2095-0756.20210311

Preparation of two strains of lignin-degrading bacteria solid inoculum

doi: 10.11833/j.issn.2095-0756.20210311

College of Forestry, Beijing Forestry University, Beijing 100083, China

Received Date: 2021-04-21
Rev Recd Date: 2021-08-16

Available Online: 2022-03-25

Publish Date: 2022-03-25

Abstract

Objective With two different lignin-degrading bacteria, strain A (Aspergillus nidulans) and strain Q (Trametes sp.) used as materials, this study is aimed to research the production of high-efficiency solid fermentation inoculants for degradation or composting of garden greening wastes. Method First, a single factor experiment was employed to determine the types of carbon and nitrogen sources and additional nutrient components of the solid fermentation medium. Then, the orthogonal experiment design was adopted to optimize the amount of carbon and nitrogen sources to be added, based on which, efforts were made to figure out the inoculation amount of additional nutrient components and the best solid medium fermentation conditions of the two lignin-degrading bacteria using a uniform experiment combined with the artificial neural network algorithm. Result The optimized solid inoculum medium matrix (mass ratio) of strain Q was with 30.000 g bran as the base, 3.000 g soybean meal powder and 0.188 g corn flour added, with extra nutrients (based on the mass ratio of the matrix) being MgSO₄ 1.434%, KH₂PO₄ 0.115%, FeSO₄·7H₂O 1.497% whereas the inoculation conditions being 6.000% of inoculation, with the ratio of material to water as 1.000∶0.992, and the protective agent as 1.000%. The optimized solid inoculum medium matrix (mass ratio) of strain A was with 30.000 g bran as the base, 1.500 g soybean meal powder and 1.500 g sodium lignosulfonate were added, with extra nutrients (based on the mass ratio of the matrix) being MgSO₄ 0.123%, KH₂PO₄ 0.213%, FeSO₄·7H₂O 1.280% whereas the inoculation conditions being 21.000% inoculation amount with the ratio of material to water as 1∶1 and the protective agent as 19.000%. Strain Q and strain A were fermented on the optimized medium for 7−9 days to get the largest fermentation biomass with the D(260) value of strain Q being 0.596 and the D(260) value of strain A being 0.478. Conclusion The solid inoculum prepared by two lignin-degrading bacteria under optimized fermentation conditions were featured with high biomass and certain potential for degrading the lignin of landscaping wastes. [Ch, 6 fig. 5 tab. 22 ref.]
- lignin-degrading bacteria,
- optimization,
- solid inoculum,
- fermentation

Relative Article

[1]	LI Junyang, HUO Lizhu, GONG Zhuxiang, XU Hao, WANG Yuxuan, GUO Chaofei, YANG Xuejuan, LUO Xiping. Preparation of sodium lignosulfonate adsorption materials and their adsorption properties for Congo red . Journal of Zhejiang A&F University, 2024, 41(4): 870-878. doi: 10.11833/j.issn.2095-0756.20230585
[2]	WANG Jiejie, YANG Jinyan, CHEN Buzhang, YAO Bo, PEI Jianchuan, LI Mei, ZHONG Ling, FANG Yufei. Optimal addition of aerobic fermentation sawdust in landfill sludge reuse . Journal of Zhejiang A&F University, 2022, 39(3): 662-670. doi: 10.11833/j.issn.2095-0756.20210335
[3]	ZHU Rongrong, ZHANG Zan, SHI Yanke, LIN Hui, ZHANG Xin. Reduction of toxicity of oxytetracycline by highly efficient degrading bacterium Arthrobacter nicotianae ZAF-05 . Journal of Zhejiang A&F University, 2021, 38(6): 1213-1220. doi: 10.11833/j.issn.2095-0756.20200819
[4]	MENG Tongyao, LI Suyan, ZOU Rongsong, YU Kefei, FU Bingyan, JIE Yang. Effect of immobilized lignin-degrading bacteria on green waste composting . Journal of Zhejiang A&F University, 2021, 38(1): 38-46. doi: 10.11833/j.issn.2095-0756.20200219
[5]	LI Yalin, LI Suyan, SUN Xiangyang, HAO Dan, CAI Linlin, CHANG Xiaotong. Screening and identification of a lignin degrading strain and its optimized liquid fermentation conditions . Journal of Zhejiang A&F University, 2021, 38(6): 1297-1304. doi: 10.11833/j.issn.2095-0756.20200814
[6]	WU Wenjuan, YAN Xueqing, ZOU Chunyang, WANG Bowei, HE Xian. A isolation method of lignin from bamboo based on complete dissolution . Journal of Zhejiang A&F University, 2020, 37(2): 335-342. doi: 10.11833/j.issn.2095-0756.2020.02.019
[7]	WANG Dunfei, ZHU Shengnan, XIAO Qingtie, ZHENG Xinyu, LIN Ruiyu. Optimization of culture conditions of Cd-tolerant strain Pseudomonas TCd-1 based on response surface methodology . Journal of Zhejiang A&F University, 2020, 37(5): 914-921. doi: 10.11833/j.issn.2095-0756.20190587
[8]	HE Ziqiang, ZHANG Huiling, YANG Zhengxiong. Extraction of polysaccharides of Abelmoschus esculentus using ultrasonic-assisted [Bmim] Cl-K₂HPO₄ aqueous two phases optimized by response surface method . Journal of Zhejiang A&F University, 2020, 37(3): 547-555. doi: 10.11833/j.issn.2095-0756.20190339
[9]	DING Tingting, LI Qian, JIN Zhenfu. Effects of lignin and brominated lignin on the thermal properties of epoxy resin . Journal of Zhejiang A&F University, 2018, 35(3): 562-566. doi: 10.11833/j.issn.2095-0756.2018.03.023
[10]	SONG Ye, XIANG Junliang, SHEN Yongrui, WANG Jiaqi, LIU Quan, YIN Kuide. Screening and identification of Bacillus strain BU108 and bio-control effects on potato scab . Journal of Zhejiang A&F University, 2018, 35(4): 757-764. doi: 10.11833/j.issn.2095-0756.2018.04.022
[11]	AN Lanzhi, LU Xiang, LIU Zuguang. Aggregation behavior of lignin-amine in a dilute hydrochloric acid aqueous solution . Journal of Zhejiang A&F University, 2017, 34(1): 7-13. doi: 10.11833/j.issn.2095-0756.2017.01.002
[12]	JIANG Yujian, LI Xinxin, SUN Feifei, YU Xuejun. Screening of a cellulose-decomposing strain from bamboo stand soils and optimization of its cellulase production . Journal of Zhejiang A&F University, 2015, 32(6): 821-828. doi: 10.11833/j.issn.2095-0756.2015.06.001
[13]	YE Jingjing, CAO Ningning, LIU Gang, WU Jianmei, YIN Hao, HU Zuozhong, ZHANG Jianfei. Optimization of a liquid fermentation medium for Cordyceps militaris . Journal of Zhejiang A&F University, 2014, 31(3): 465-472. doi: 10.11833/j.issn.2095-0756.2014.03.021
[14]	PENG Zhuqing, FAN Huihua, LIU Bao, LIU Yan, LI Jing. Optimization of SRAP-PCR in Sapindus mukurossi . Journal of Zhejiang A&F University, 2014, 31(2): 322-328. doi: 10.11833/j.issn.2095-0756.2014.02.024
[15]	WU Chong, ZHONG Chonglu, ZHANG Yong, ＪＩＡＮＧ Qingbin, CHEN Yu, CHEN Zhen. Establishment and optimization of an ISSR-PCR system for Chukrasia tabularis . Journal of Zhejiang A&F University, 2013, 30(4): 620-626. doi: 10.11833/j.issn.2095-0756.2013.04.024
[16]	HE Kai, CHEN Ke-ke, GUO Ming. Preparation，characterization，and properties of lignin based，rigid polyurethane foam materials . Journal of Zhejiang A&F University, 2012, 29(2): 203-209. doi: 10.11833/j.issn.2095-0756.2012.02.009
[17]	ZHOU Guo-ying, LI He. Molecular identification of the strain ZNLD-18 selectively decomposing bamboo lignin . Journal of Zhejiang A&F University, 2008, 25(4): 497-501.
[18]	YANG Ping, ZHENG Qian-jian, ZHAO Ming-shui, TANG Ding-qin. Optimization of the reaction system for CAPS marker analysis in Cryptomeria fortunei . Journal of Zhejiang A&F University, 2006, 23(1): 52-55.
[19]	WANG Zheng-jia, HUANG Jian-qin, GUO Chuan-you, YANG Ping, WANG Hua-fang. The optimal reaction system of RAPD in Carya cathayensis . Journal of Zhejiang A&F University, 2003, 20(4): 429-433.
[20]	WANG Sheng-kui. Optimal model of sustainable development of forests . Journal of Zhejiang A&F University, 2001, 18(3): 233-237.

References

[1]	WANG Lin, LI Suyan, SUN Xiangyang, et al. Mixing garden wastes and spent mushroom compost of different ratios for vermicomposting [J]. J Zhejiang A&F Univ, 2019, 36(2): 326 − 334.
[2]	CAI Linlin, LI Suyan, GONG Xiaoqiang, et al. Composting-vermicomposting of green waste processing spiked with cow dung [J]. J Zhejiang A&F Univ, 2018, 35(2): 261 − 267.
[3]	LIANG Congying, LIN Lu. Environmental microorganisms driven lignin biodegradation and their roles in lignin utilization [J]. Microbiol China, 2020, 47(10): 3380 − 3392.
[4]	SUN Pu, SUN Wanru, SHI Zhancheng, et al. Application of microbial agents in woody waste composting: a review [J]. Jiangsu Agric Sci, 2020, 48(15): 57 − 63.
[5]	MENG Tongyao, LI Suyan, ZOU Rongsong, et al. Effect of immobilized lignin-degrading bacteria on green waste composting [J]. J Zhejiang A&F Univ, 2021, 38(1): 38 − 46.
[6]	FU Bingyan, SUN Xiangyang, YU Kefei, et al. Solid state fermentation of Bacillus sp. B01 and its effect on green waste composting [J]. Res Environ Sci, 2021, 34(2): 450 − 457.
[7]	LI Xinjun, FENG Xiaoxiao, JIN Huiqing, et al. Optimization of solid-state fermentation media for growth of two endophytic fungal strains [J]. Microbiol China, 2016, 43(3): 550 − 558.
[8]	HU Xinying, LI Hongjun, LI Shaobo, et al. A comparative study of the optimization of processing technology of conditioning pine board meat by response surface methodology and BP neural network-particle swarm optimization [J]. Food Ferment Ind, 2019, 45(24): 179 − 187.
[9]	LI Peng, CHEN Xiuzhen, ZHUANG Wenying. Optimization of solid state fermentation conditions for the high cellulose producing strain Trichoderma koningiopsis 8985 [J]. Mycosystema, 2021, 40(4): 743 − 758.
[10]	LI Jiateng. Screening of Dominant Cellulose-degradation Strains and Condition Optimization of Mixed Strains Fermentation of Pleurotus eryngii Spent Mushroom Substrate[D]. Yangling: Northwest A&F University, 2019.
[11]	LI Xianguo, SUN Yemei, YANG Yanli, et al. symmetrical residual connections for single image super-resolution [J]. ACM Transac Multimedia Comput Commun Appl, 2019, 15(1): 1 − 10.
[12]	PENG Jing, LUO Haoyu, ZHAO Gansen, et al. Survey of medical image segmentation algorithm in deep learning [J]. Comput Eng Appl, 2021, 57(3): 44 − 57.
[13]	LIU Yuliang, JIN Lianwen. Deep matching prior network: toward tighter multi-oriented text detection [C]//IEEE. 2017 IEEE Conference on Computer Vision, 2017: 1962 − 1969. doi: 10.1109/CVPR.2017.368.
[14]	HOANG N D. Automatic impervious surface area detection using image texture analysis and neural computing models with advanced opitimizers[J/OL]. Comput Intell Neurosci, 2021, 2021: 8820116[2021-03-20]. doi: 10.1155/2021/8820116.
[15]	FLORENCIO F, VALENÇT, MORENO E D, et al. Performance analysis of deep learning libraries: TensorFlow and PyTorch [J]. J Comp Sci, 2019, 15(6): 785 − 799.
[16]	LIU Bo, WU Yinglong, ZHANG Xia, et al. Optimization of solid state fermentation medium of Monascus M2 for xylanase production by response surface analysis [J]. Sci Technol Food Ind, 2014(1): 254 − 258.
[17]	ZHANG Jian. Optimization of Mnp Production and Decolorization of Dye from Aspergillus nidulans Transformant Strain TN02A7-Lg-mnp2 [D]. Harbin: Northeast Forestry University, 2017.
[18]	XIONG Yi. Screening and Identification of Lignocellulose Degrading Bacteria and Degradation Products Research[D]. Taiyuan: Shanxi Agricultural University, 2019.
[19]	HU Ruiping, DING Xian, LI Laihao, et al. Optimization of fermentation medium composition by response surface methodology for the spore production of Bacillus subtilis [J]. Chin J Ecol, 2018, 37(2): 605 − 612.
[20]	LIU Yanling, XIAO Anfeng, LI Lijun, et al. The regulatory mechanisms of α-L-rhamnosidase synthesis from Aspergillus aculeatus and improving the medium α-L-rhamnosidase production using solid-state fermentation [J]. J Chin Inst Food Sci Technol, 2015, 15(7): 10 − 17.
[21]	PENG Zhini, GUO Liqiong, ZHANG Xinchao, et al. Compositions and antioxidant activities of soybean substrate during solid-state fermentation by Cordyceps militaris [J]. Mycosystema, 2011, 30(2): 338 − 342.
[22]	LIU Bo. Studies on Fermentation Optimization, Purification and Enzyme Characteristics of Raw-starch-digesting Amylase from Monascus[D]. Ya’an: Sichuan Agricultural University, 2014.

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(6) / Tables(5)

Get Citation

PDF

XML

Article views(680) PDF downloads(28) Cited by()

Proportional views

HTML

园林绿化废弃物资源化利用^[1-2]已成为研究热点，其中堆肥化是一种比较理想的处理方式。由于园林绿化废弃物木质素含量较高、起始微生物数量较少等原因，堆肥效率较低，因此如何提高堆肥过程中的微生物数量成为推进园林绿化废弃物堆肥的关键^[3-4]。添加微生物菌剂是提高堆体微生物数量最直接的方式之一。目前，针对木质素降解的菌剂较少，且多集中于液体菌剂。然而液体菌剂对无菌条件要求较高，运输及产品储存有诸多不便^[5]。固体菌剂在生产和储存方面能弥补液体菌剂的不足，同时，固体菌剂生产成本更低、生产工艺也相对简单，对促进园林绿化废弃物堆肥化过程的优势更明显^[6]。生产固体菌剂，除了设计固体发酵的培养基，还要优化其发酵条件，主要流程为试验设计、数学建模和优化设计3个部分^[7]。合理的试验设计能用较少的试验数据进行建模，从而获取各因素范围内的最优解。已有研究对于固态发酵的优化多是使用响应面法^[8-9]。但是有研究发现^[10]：人工神经网络算法优化培养基比响应面法优化培养基验证结果更准确，误差更小。本研究拟采用单因素试验和正交试验确定作为固态发酵培养基碳源、氮源的较优种类和水平，然后根据碳氮源优化结果，通过单因素试验确定外加营养组分种类，最后采用均匀实验结合人工神经网络建模与优化，寻找2株木质素降解菌的外加营养组分接种量和最佳固体培养基发酵条件，以期得到最大发酵生物量，制作高效固体菌剂用于园林绿化废弃物堆肥。

3. 结论

本研究结果表明：人工神经网络算法优化后菌株Q的预测值为0.6020，实测值为0.5960，误差为1.000%；菌株A的预测值为0.485，实测值为0.478，误差为1.500%。基于人工神经网络构建的模型拟合度较好。

根据单因素试验和人工神经网络算法结果，确定了2株木质素降解菌的最优固体发酵条件。菌株Q：固体菌剂培养基基质为麸皮30.000 g作为基底，添加豆饼粉10.000%和玉米粉0.625%，外加营养组分为MgSO₄ 1.434%、KH₂PO₄ 0.115%和FeSO_4·7H₂O 1.497%；接种条件为接菌量6.000%、料水比1.000∶0.992、保护剂1.000%。菌株A：固体菌剂培养基基质为麸皮30.000g作为基底，添加豆饼粉5.000%和木质素磺酸钠为5.000%，外加营养组分为MgSO₄ 0.123 %、KH₂PO₄ 0.213 %、FeSO₄·7H₂O 1.280%；接种条件为接菌量21.000%、料水比1∶1、保护剂19.000%。

Reference (22)

[1]	王琳, 李素艳, 孙向阳, 等. 不同配比园林绿化废弃物和蘑菇渣混合蚯蚓堆肥的效果[J]. 浙江农林大学学报, 2019, 36(2): 326 − 334.	WANG Lin, LI Suyan, SUN Xiangyang, et al. Mixing garden wastes and spent mushroom compost of different ratios for vermicomposting [J]. J Zhejiang A&F Univ, 2019, 36(2): 326 − 334.
[2]	蔡琳琳, 李素艳, 龚小强, 等. 好氧堆肥-蚯蚓堆肥结合法处理绿化废弃物与牛粪[J]. 浙江农林大学学报, 2018, 35(2): 261 − 267.	CAI Linlin, LI Suyan, GONG Xiaoqiang, et al. Composting-vermicomposting of green waste processing spiked with cow dung [J]. J Zhejiang A&F Univ, 2018, 35(2): 261 − 267.
[3]	梁丛颖, 林璐. 环境微生物介导的木质素代谢及其资源化利用研究进展[J]. 微生物学通报, 2020, 47(10): 3380 − 3392.	LIANG Congying, LIN Lu. Environmental microorganisms driven lignin biodegradation and their roles in lignin utilization [J]. Microbiol China, 2020, 47(10): 3380 − 3392.
[4]	孙谱, 孙婉薷, 石占成, 等. 微生物菌剂在木本废弃物堆肥中的应用综述[J]. 江苏农业科学, 2020, 48(15): 57 − 63.	SUN Pu, SUN Wanru, SHI Zhancheng, et al. Application of microbial agents in woody waste composting: a review [J]. Jiangsu Agric Sci, 2020, 48(15): 57 − 63.
[5]	孟童瑶, 李素艳, 邹荣松, 等. 固定化木质素降解菌对园林废弃物堆肥的影响[J]. 浙江农林大学学报, 2021, 38(1): 38 − 46.	MENG Tongyao, LI Suyan, ZOU Rongsong, et al. Effect of immobilized lignin-degrading bacteria on green waste composting [J]. J Zhejiang A&F Univ, 2021, 38(1): 38 − 46.
[6]	付冰妍, 孙向阳, 余克非, 等. 芽孢杆菌B01固态发酵及其对园林废弃物堆肥的影响[J]. 环境科学研究, 2021, 34(2): 450 − 457.	FU Bingyan, SUN Xiangyang, YU Kefei, et al. Solid state fermentation of Bacillus sp. B01 and its effect on green waste composting [J]. Res Environ Sci, 2021, 34(2): 450 − 457.
[7]	李信军, 冯晓晓, 金慧清, 等. 2株内生真菌菌株固态发酵培养基优化[J]. 微生物学通报, 2016, 43(3): 550 − 558.	LI Xinjun, FENG Xiaoxiao, JIN Huiqing, et al. Optimization of solid-state fermentation media for growth of two endophytic fungal strains [J]. Microbiol China, 2016, 43(3): 550 − 558.
[8]	胡欣颖, 李洪军, 李少博, 等. 对比研究响应面法和BP神经网络-粒子群算法优化调理松板肉加工工艺[J]. 食品与发酵工业, 2019, 45(24): 179 − 187.	HU Xinying, LI Hongjun, LI Shaobo, et al. A comparative study of the optimization of processing technology of conditioning pine board meat by response surface methodology and BP neural network-particle swarm optimization [J]. Food Ferment Ind, 2019, 45(24): 179 − 187.
[9]	李鹏, 陈秀珍, 庄文颖. 高产纤维素酶的拟康宁木霉菌株8985固态发酵条件优化[J]. 菌物学报, 2021, 40(4): 743 − 758.	LI Peng, CHEN Xiuzhen, ZHUANG Wenying. Optimization of solid state fermentation conditions for the high cellulose producing strain Trichoderma koningiopsis 8985 [J]. Mycosystema, 2021, 40(4): 743 − 758.
[10]	李佳腾. 纤维素降解优势菌株的筛选与杏鲍菇菌糠混菌发酵条件的优化[D]. 杨凌: 西北农林科技大学, 2019.	LI Jiateng. Screening of Dominant Cellulose-degradation Strains and Condition Optimization of Mixed Strains Fermentation of Pleurotus eryngii Spent Mushroom Substrate[D]. Yangling: Northwest A&F University, 2019.
[11]	LI Xianguo, SUN Yemei, YANG Yanli, et al. symmetrical residual connections for single image super-resolution [J]. ACM Transac Multimedia Comput Commun Appl, 2019, 15(1): 1 − 10.
[12]	彭璟, 罗浩宇, 赵淦森, 等. 深度学习下的医学影像分割算法综述[J]. 计算机工程与应用, 2021, 57(3): 44 − 57.	PENG Jing, LUO Haoyu, ZHAO Gansen, et al. Survey of medical image segmentation algorithm in deep learning [J]. Comput Eng Appl, 2021, 57(3): 44 − 57.
[13]	LIU Yuliang, JIN Lianwen. Deep matching prior network: toward tighter multi-oriented text detection [C]//IEEE. 2017 IEEE Conference on Computer Vision, 2017: 1962 − 1969. doi: 10.1109/CVPR.2017.368.
[14]	HOANG N D. Automatic impervious surface area detection using image texture analysis and neural computing models with advanced opitimizers[J/OL]. Comput Intell Neurosci, 2021, 2021: 8820116[2021-03-20]. doi: 10.1155/2021/8820116.
[15]	FLORENCIO F, VALENÇT, MORENO E D, et al. Performance analysis of deep learning libraries: TensorFlow and PyTorch [J]. J Comp Sci, 2019, 15(6): 785 − 799.
[16]	刘波, 邬应龙, 张霞, 等. 红曲霉固态发酵产木聚糖酶培养基的响应面优化[J]. 食品工业科技, 2014(1): 254 − 258.	LIU Bo, WU Yinglong, ZHANG Xia, et al. Optimization of solid state fermentation medium of Monascus M2 for xylanase production by response surface analysis [J]. Sci Technol Food Ind, 2014(1): 254 − 258.
[17]	张健. 构巢曲霉转化子菌株TN02A7-Lg-mnp2产MnP的优化诱导及染料脱色研究[D]. 哈尔滨: 东北林业大学, 2017.	ZHANG Jian. Optimization of Mnp Production and Decolorization of Dye from Aspergillus nidulans Transformant Strain TN02A7-Lg-mnp2 [D]. Harbin: Northeast Forestry University, 2017.
[18]	熊乙. 木质纤维素降解菌的筛选鉴定及降解产物研究[D]. 太原: 山西农业大学, 2019.	XIONG Yi. Screening and Identification of Lignocellulose Degrading Bacteria and Degradation Products Research[D]. Taiyuan: Shanxi Agricultural University, 2019.
[19]	胡瑞萍, 丁贤, 李来好, 等. 响应面法优化枯草芽孢杆菌NHS1产芽孢发酵培养[J]. 生态学杂志, 2018, 37(2): 605 − 612.	HU Ruiping, DING Xian, LI Laihao, et al. Optimization of fermentation medium composition by response surface methodology for the spore production of Bacillus subtilis [J]. Chin J Ecol, 2018, 37(2): 605 − 612.
[20]	刘艳苓, 肖安风, 李利君, 等. 棘孢曲霉固态发酵α-L-鼠李糖苷酶调控机制及培养基优化[J]. 中国食品学报, 2015, 15(7): 10 − 17.	LIU Yanling, XIAO Anfeng, LI Lijun, et al. The regulatory mechanisms of α-L-rhamnosidase synthesis from Aspergillus aculeatus and improving the medium α-L-rhamnosidase production using solid-state fermentation [J]. J Chin Inst Food Sci Technol, 2015, 15(7): 10 − 17.
[21]	彭志妮, 郭丽琼, 张新超, 等. 蛹虫草固体发酵大豆基质的成分及抗氧化活性变化研究[J]. 菌物学报, 2011, 30(2): 338 − 342.	PENG Zhini, GUO Liqiong, ZHANG Xinchao, et al. Compositions and antioxidant activities of soybean substrate during solid-state fermentation by Cordyceps militaris [J]. Mycosystema, 2011, 30(2): 338 − 342.
[22]	刘波. 红曲霉生淀粉酶发酵优化、分离纯化及其酶学性质研究[D]. 雅安: 四川农业大学, 2014.	LIU Bo. Studies on Fermentation Optimization, Purification and Enzyme Characteristics of Raw-starch-digesting Amylase from Monascus[D]. Ya’an: Sichuan Agricultural University, 2014.

处理	豆饼粉/%	木质素磺酸钠/%	D(260)
T₁	1.250	2.500	0.228±0.009
T₂	1.250	5.000	0.241±0.001
T₃	1.250	10.000	0.207±0.018
T₄	2.500	2.500	0.216±0.007
T₅	2.500	5.000	0.213±0.008
T₆	2.500	10.000	0.245±0.010
T₇	5.000	2.500	0.237±0.010
T₈	5.000	5.000	0.249±0.003
T₉	5.000	10.000	0.241±0.007

处理	豆饼粉/%	玉米粉/%	D(260)
T₁	1.250	0.625	0.101±0.003
T₂	1.250	2.500	0.224±0.006
T₃	1.250	5.000	0.214±0.005
T₄	5.000	0.625	0.101±0.003
T₅	5.000	2.500	0.162±0.008
T₆	5.000	5.000	0.232±0.020
T₇	10.000	0.625	0.261±0.003
T₈	10.000	2.500	0.191±0.002
T₉	10.000	5.000	0.102±0.003

试验号	因素水平(实际用量/%)						D(260)
试验号	MgSO₄	KH₂PO₄	FeSO₄·7H₂O	接菌量	料水比	海藻糖	D(260)
实测值1	1(0.600)	2(0.800)	3(1.000)	5(25.000)	7(1.00∶0.50)	10(16.000)	0.4110
仿真值1	1(0.600)	2(0.800)	3(1.000)	5(25.000)	7(1.00∶0.50)	10(16.000)	0.4109
实测值2	2(0.800)	4(1.200)	6(0.600)	10(25.000)	3(1.00∶0.65)	9(12.000)	0.4500
仿真值2	2(0.800)	4(1.200)	6(0.600)	10(25.000)	3(1.00∶0.65)	9(12.000)	0.4499
实测值3	3(1.000)	6(0.600)	9(1.200)	4(20.000)	10(1.00∶0.95)	8(8.000)	0.4800
仿真值3	3(1.000)	6(0.600)	9(1.200)	4(20.000)	10(1.00∶0.95)	8(8.000)	0.4801
实测值4	4(1.200)	8(1.000)	1(0.600)	9(20.000)	6(1.00∶0.30)	7(4.000)	0.4430
仿真值4	4(1.200)	8(1.000)	1(0.600)	9(20.000)	6(1.00∶0.30)	7(4.000)	0.4431
实测值5	5(1.400)	10(1.400)	4(1.200)	3(15.000)	2(1.00∶0.50)	6(0.000)	0.3860
仿真值5	5(1.400)	10(1.400)	4(1.200)	3(15.000)	2(1.00∶0.50)	6(0.000)	0.3859
实测值6	6(0.600)	1(0.600)	7(0.800)	8(15.000)	9(1.00∶0.80)	5(16.000)	0.4620
仿真值6	6(0.600)	1(0.600)	7(0.800)	8(15.000)	9(1.00∶0.80)	5(16.000)	0.4619
实测值7	7(0.800)	3(1.000)	10(1.400)	2(10.000)	5(1.00∶0.95)	4(12.000)	0.4630
仿真值7	7(0.800)	3(1.000)	10(1.400)	2(10.000)	5(1.00∶0.95)	4(12.000)	0.4635
实测值8	8(1.000)	5(1.400)	2(0.800)	7(10.000)	1(1.00∶0.30)	3(8.000)	0.2930
仿真值8	8(1.000)	5(1.400)	2(0.800)	7(10.000)	1(1.00∶0.30)	3(8.000)	0.2929
实测值9	9(1.200)	7(0.800)	5(1.400)	1(5.000)	8(1.00∶0.65)	2(4.000)	0.4450
仿真值9	9(1.200)	7(0.800)	5(1.400)	1(5.000)	8(1.00∶0.65)	2(4.000)	0.4453
实测值10	10(1.400)	9(1.200)	8(1.000)	6(5.000)	4(1.00∶0.80)	1(0.000)	0.4230
仿真值10	10(1.400)	9(1.200)	8(1.000)	6(5.000)	4(1.00∶0.80)	1(0.000)	0.4231

试验号	因素水平(实际用量/%)						D(260)
试验号	CaSO₄	MgSO₄	KH₂PO₄	接菌	料水比	海藻糖	D(260)
实测值1	1(0.600)	2(0.800)	3(1.000)	5(25.000)	7(1.00∶0.50)	10(16.000)	0.3930
仿真值1	1(0.600)	2(0.800)	3(1.000)	5(25.000)	7(1.00∶0.50)	10(16.000)	0.3930
实测值2	2(0.800)	4(1.200)	6(0.600)	10(25.000)	3(1.00∶0.65)	9(12.000)	0.4020
仿真值2	2(0.800)	4(1.200)	6(0.600)	10(25.000)	3(1.00∶0.65)	9(12.000)	0.4020
实测值3	3(1.000)	6(0.600)	9(1.200)	4(20.000)	10(1.00∶0.95)	8(8.000)	0.4260
仿真值3	3(1.000)	6(0.600)	9(1.200)	4(20.000)	10(1.00∶0.95)	8(8.000)	0.4260
实测值4	4(1.200)	8(1.000)	1(0.600)	9(20.000)	6(1.00∶0.30)	7(4.000)	0.3930
仿真值4	4(1.200)	8(1.000)	1(0.600)	9(20.000)	6(1.00∶0.30)	7(4.000)	0.3930
实测值5	5(1.400)	10(1.400)	4(1.200)	3(15.000)	2(1.00∶0.50)	6(0.000)	0.3900
仿真值5	5(1.400)	10(1.400)	4(1.200)	3(15.000)	2(1.00∶0.50)	6(0.000)	0.3900
实测值6	6(0.600)	1(0.600)	7(0.800)	8(15.000)	9(1.00∶0.80)	5(16.000)	0.4320
仿真值6	6(0.600)	1(0.600)	7(0.800)	8(15.000)	9(1.00∶0.80)	5(16.000)	0.4320
实测值7	7(0.800)	3(1.000)	10(1.400)	2(10.000)	5(1.00∶0.95)	4(12.000)	0.4110
仿真值7	7(0.800)	3(1.000)	10(1.400)	2(10.000)	5(1.00∶0.95)	4(12.000)	0.4110
实测值8	8(1.000)	5(1.400)	2(0.800)	7(10.000)	1(1.00∶0.30)	3(8.000)	0.3650
仿真值8	8(1.000)	5(1.400)	2(0.800)	7(10.000)	1(1.00∶0.30)	3(8.000)	0.3650
实测值9	9(1.200)	7(0.800)	5(1.400)	1(5.000)	8(1.00∶0.65)	2(4.000)	0.3770
仿真值9	9(1.200)	7(0.800)	5(1.400)	1(5.000)	8(1.00∶0.65)	2(4.000)	0.3770
实测值10	10(1.400)	9(1.200)	8(1.000)	6(5.000)	4(1.00∶0.80)	1(0.000)	0.4210
仿真值10	10(1.400)	9(1.200)	8(1.000)	6(5.000)	4(1.00∶0.80)	1(0.000)	0.4210

Preparation of two strains of lignin-degrading bacteria solid inoculum

doi: 10.11833/j.issn.2095-0756.20210311

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

Proportional views