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

Abstract

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

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

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

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