基于近红外光谱的大豆茎秆化学组分含量检测模型构建与应用

doi:10.3864/j.issn.0578-1752.2021.05.002

中国农业科学 ›› 2021, Vol. 54 ›› Issue (5): 887-900.doi: 10.3864/j.issn.0578-1752.2021.05.002

• 作物遗传育种·种质资源·分子遗传学 • 上一篇下一篇

基于近红外光谱的大豆茎秆化学组分含量检测模型构建与应用

李佳佳¹(),洪慧龙²(),万明月¹(),储丽¹(),赵敬会¹,汪明华¹,徐志鹏¹,张阴¹,黄志平³,张文明¹(),王晓波¹(),邱丽娟²()

¹安徽农业大学农学院,合肥 230036
²中国农业科学院作物科学研究所/农业部作物基因资源与遗传改良重大科学工程/农业部作物基因资源与种质创制重点实验室,北京100081
³农作物品质改良安徽省重点实验室,合肥 230001

收稿日期:2020-07-28 接受日期:2020-09-17 出版日期:2021-03-01 发布日期:2021-03-09
通讯作者: 张文明,王晓波,邱丽娟
作者简介:李佳佳,E-mail：lijia6862@163.com|洪慧龙,E-mail：15011290378@163.com|万明月,E-mail：2748001406@qq.com|储丽,E-mail：chuli1206@163.com
基金资助:
国家重点研发计划(2016YFD0100201);安徽省自然科学基金(1908085QC105);安徽省学术和技术带头人及后备人选科研活动经费(2020H236);农作物品质改良安徽省重点实验室开放课题(2020ZW002)

Construction and Application of Detection Model for the Chemical Composition Content of Soybean Stem Based on Near Infrared Spectroscopy

JiaJia LI¹(),HuiLong HONG²(),MingYue WAN¹(),Li CHU¹(),JingHui ZHAO¹,MingHua WANG¹,ZhiPeng XU¹,Yin ZHANG¹,ZhiPing HUANG³,WenMing ZHANG¹(),XiaoBo WANG¹(),LiJuan QIU²()

¹College of Agriculture, Anhui Agricultural University, Hefei 230036
²Institute of Crop Science, Chinese Academy of Agricultural Sciences/The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA), Beijing 100081
³Key Laboratory of Crop Quality Improvement of Anhui Province, Hefei 230001

Received:2020-07-28 Accepted:2020-09-17 Online:2021-03-01 Published:2021-03-09
Contact: WenMing ZHANG,XiaoBo WANG,LiJuan QIU

摘要/Abstract

摘要：

【目的】大豆茎秆化学组分（纤维素、半纤维素、木质素和粗纤维等）与其茎秆抗倒伏能力密切相关,但由于目前大豆茎秆化学组分检测多采用传统的化学分析技术,测定过程操作复杂、耗时耗力、成本昂贵且易造成环境污染,不适合大规模育种应用,因此,通过构建一套低成本、快速、科学、无污染的大豆茎秆化学组分检测方法,为大豆种质资源茎秆组分分布规律及其与大豆生长习性和倒伏性关系的研究提供方法基础。【方法】通过建立一套基于近红外光谱检测技术的大豆茎秆化学组分检测模型,并利用该模型对大豆种质资源茎秆中的中性洗涤纤维（neutral detergent fiber,NDF）、酸性洗涤纤维（acid detergent fiber,ADF）和粗纤维（crude fiber,CF）等化学组分进行检测分析,通过方差分析、多重比较和小提琴图分析,明确大豆茎秆CF含量与其生长习性及抗倒伏性之间的内在关系。【结果】基于构建的大豆茎秆化学组分近红外光谱快速检测模型对茎秆NDF、ADF和CF组分检测数值的校正相关系数（R_C）均在0.90以上。利用16份模型外大豆茎秆样本对模型的有效性进行验证发现,常规化学检测与该模型检测结果之间无显著性差异（P > 0.05）。利用该模型对2017年和2018年种植的393份大豆茎秆CF含量及其生长习性之间的关系进行分析,结果表明,大豆茎秆CF含量符合正态分布规律,在CF含量50.00%以上的材料中,2年数据均表现出直立型（91.67%和86.14%）显著高于蔓生型（8.33%和13.86%）,表明大豆茎秆CF含量与其生长习性呈极显著正相关（P < 0.01）。【结论】构建的近红外光谱模型具有低成本、快速高效、无污染的特点。此外,茎秆中CF含量高的大豆品种植株具有更强的抗弯曲度,可作为大豆抗倒伏育种亲本筛选的重要指标。

关键词: 大豆, 茎秆化学组分, 近红外光谱检测定标模型, 生长习性, 抗倒伏育种

Abstract:

【Objective】The chemical components (cellulose, hemicellulose, lignin, crude fiber, etc.) in the stem are closely/intently linked with lodging resistance of soybean. However, due to the current detection and analysis of chemical components in the stem, the traditional chemical analysis technology is adopted, and the determination procedure is complex, time-consuming, labor-consuming, expensive, and lead to environmental pollution. Thus, the current study aimed to construct a low-cost, quick, scientific and pollution-free method for detection of chemical components in soybean stems, and provide a methodological basis for the study of the distribution of stem components in soybean germplasm resources and their relationship with soybean growth habits and lodging. 【Method】 In present study, a chemical component detection model of soybean stem based on near-infrared spectroscopy was established, and the model was used to detect neutral detergent fiber (NDF), acid detergent fiber (ADF) and crude fiber (CF) of soybean germplasm resource stem. CF and other chemical components were detected and analyzed. The intrinsic relationship between CF content of soybean stem and its growth habit and lodging resistance was elucidated by analysis of variance, multiple comparisons and violin plot analysis. 【Result】 The results showed that the correction correlation coefficient (R_C) of the NDF, ADF and CF components of the stem based on the rapid detection model constructed in this research was all above 0.90. The validity of the model was verified by using 16 soybean stem samples outside the model, and it was found that there was no significant difference between the results of routine chemical testing and the model testing (P > 0.05). This model was used to analyze the relationship between the CF content and growth habit of 393 soybean stems planted in 2017 and 2018. The findings showed that the CF content of soybean stems conforms to the normal distribution. Among the materials of the CF content is above 50.00%, the two-year data showed that the erect type (91.67% and 86.14%) was significantly higher than the sprawl type (8.33% and 13.86%), indicating that the CF content was significantly correlated with its growth habit of soybean stems (P < 0.01). 【Conclusion】 The Near-infrared Spectroscopy Model constructed in this study has the characteristics of low cost, fast, high efficiency and pollution-free. In addition, the plants of soybean cultivars with high CF content in the stem had stronger bending resistance, which could be used as an important index for screening lodging resistance breeding parents of soybean.

Key words: soybean, stem chemical components, near infrared spectrum detection model, growth habit, lodging resistance breeding

李佳佳,洪慧龙,万明月,储丽,赵敬会,汪明华,徐志鹏,张阴,黄志平,张文明,王晓波,邱丽娟. 基于近红外光谱的大豆茎秆化学组分含量检测模型构建与应用[J]. 中国农业科学, 2021, 54(5): 887-900.

JiaJia LI,HuiLong HONG,MingYue WAN,Li CHU,JingHui ZHAO,MingHua WANG,ZhiPeng XU,Yin ZHANG,ZhiPing HUANG,WenMing ZHANG,XiaoBo WANG,LiJuan QIU. Construction and Application of Detection Model for the Chemical Composition Content of Soybean Stem Based on Near Infrared Spectroscopy[J]. Scientia Agricultura Sinica, 2021, 54(5): 887-900.

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成分 Component	平均值±标准差 Mean±SD (%)	95%置信区间 95% Confidence interval (%)		最小值 Min (%)	最大值 Max (%)
成分 Component	平均值±标准差 Mean±SD (%)	下限 Lower Limit	上限 Upper Limit	最小值 Min (%)	最大值 Max (%)
酸性洗涤纤维ADF	40.59±3.72	39.96	41.23	30.27	51.15
中性洗涤纤维NDF	63.12±3.84	62.46	63.77	51.91	73.08
粗纤维CF	47.08±3.86	46.42	47.74	36.98	58.31

序号 No.	测定值（常规方法） Value (Conventional) (%)			预测值 Predicted value (%)			常规测定值与预测值差异 Difference of value
序号 No.	ADF	NDF	CF	ADF	NDF	CF	ADF	NDF	CF
1	35.27	62.22	43.42	36.99	59.35	43.91	-1.72	2.87	-0.50
2	34.45	59.28	42.00	36.08	59.32	43.33	-1.63	-0.04	-1.34
3	39.75	61.47	47.68	40.20	62.25	48.03	-0.45	-0.78	-0.35
4	42.97	63.45	49.03	42.66	65.21	49.73	0.31	-1.76	-0.70
5	41.56	64.47	49.47	43.07	64.96	50.14	-1.51	-0.49	-0.67
6	41.10	59.06	45.97	42.80	60.32	47.47	-1.70	-1.26	-1.51
7	42.51	61.30	45.92	40.94	61.68	44.90	1.58	-0.38	1.02
8	30.27	51.91	36.98	31.80	51.00	37.12	-1.54	0.91	-0.14
9	47.70	71.70	54.58	45.87	71.45	53.81	1.83	0.24	0.77
10	40.82	66.99	48.17	41.85	66.06	48.85	-1.03	0.94	-0.68
11	39.80	67.31	50.49	40.70	64.82	50.10	-0.90	2.50	0.38
12	35.99	62.31	44.44	37.89	62.42	46.08	-1.90	-0.11	-1.64
13	43.47	64.96	48.86	42.14	64.55	48.02	1.32	0.41	0.84
14	37.53	62.68	44.71	37.84	61.61	44.37	-0.31	1.07	0.34
15	42.25	68.34	48.15	42.62	68.30	49.84	-0.37	0.04	-1.69
16	41.19	69.23	49.84	41.81	68.95	49.79	-0.62	0.29	0.05

指标 Index	相关系数 Correlation coefficient (r)	T测验 T-test（P）
酸性洗涤纤维 Acid detergent fiber (ADF)	0.969	0.098
中性洗涤纤维 Neutral detergent fiber (NDF)	0.967	0.374
粗纤维Crude fiber (CF)	0.976	0.124

项目 Items		年份 Year
项目 Items		2017	2018
大豆品种数量（份） Number of soybean varieties		1664	1335
打磨秸秆数（份） Number of grinding stem		745	639
2年共有秸秆数（份） Total number of stems in two years		393	393
粗纤维含量 Crude fiber contents (%)	最大值Max	57.39	58.63
	最小值Min	27.64	37.39
	均值±标准差Mean±Sd	47.60±3.53	50.30±3.60
	变异系数CV (%)	7.42	7.16

年份Year	类型 Types	数量 Number	CF含量均值±标准差 Crude fiber contents Mean±Sd (%)	95%置信区间 95% Confidence interval		极小值 Min	极大值 Max	F值 F value	P值 P value
2017	直立型 Erect type	335 (85.24%)	47.96±3.35A	47.60	48.32	37.66	57.39	25.77	0.000
	蔓生型 Sprawl type	58 (14.76%)	45.49±3.85B	44.48	46.50	27.64	53.02
	总数Total	393	47.60±3.53	47.25	47.95	27.64	57.39
2018	直立型 Erect type	312 (79.39%)	50.65±3.56A	50.25	51.05	37.39	58.63	14.64	0.000
	蔓生型 Sprawl type	81 (20.61%)	48.96±3.45B	48.20	49.72	39.96	58.35
	总数Total	393	50.30±3.60	49.94	50.66	37.39	58.63

基于近红外光谱的大豆茎秆化学组分含量检测模型构建与应用

Construction and Application of Detection Model for the Chemical Composition Content of Soybean Stem Based on Near Infrared Spectroscopy

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