基于近红外光谱的高粱籽粒直链淀粉、支链淀粉含量检测模型的构建与应用

doi:10.3864/j.issn.0578-1752.2022.01.003

Abstract

Abstract:

【Objective】 Sorghum is one of the main raw materials for wine making and feed. The ratio of amylose content to amylopectin content in its grains is closely related to liquor quality and feed quality. Traditional chemical detection methods of sorghum components are no longer suitable for high-throughput testing. Modified PLS is used to perform spectral preprocessing, score processing and result monitoring on the near-infrared spectra of sorghum samples to establish sorghum grain amylose and amylopectin. The prediction model of amylose content aims to obtain a fast, efficient and low-cost detection method, laying the foundation for genetic improvement and quality analysis of sorghum. 【Method】 From 450 sorghum resources, 112 representative varieties were selected as calibration set and verification set. The chemical values of amylose and amylopectin content in 112 sorghum varieties were measured, and near-infrared spectra with wavelengths of 850-1 048 nm were collected, and the spectrum was scanned data matrix and chemical data calculated score (PL1) processing and interpreting the differences between the spectra, and eliminating abnormal species with Global H (GH) greater than 3 to reduce modeling errors. Modified PLS regression technology is used for modeling, and different calibration models are established through different scattering processing and derivative processing methods. Determine the best model according to the cross-validation standard deviation (SECV) and cross-validation correlation coefficient (1-VR), and perform result monitoring and non-parametric testing to evaluate the predictive performance of the model.【Result】 The near-infrared prediction model SECV of amylose is 2.7732, 1-VR is 0.9503, and the correlation coefficient (RSQ) is 0.9688. Bias=0.229<2.7732(SECV)×0.6, that is, the deviation (Bias) is less than 0.6 times of the calibration model SECV; the predicted standard deviation (SEP)=1.266<2.7732(SECV)×1.3=3.60516, that is, the SEP is less than the calibration. The model SECV is 1.3 times, 11.01(SD)-10.81(SD)=0.2<11.02(SD)×0.2=2.204, that is, the difference between the standard deviation (SD) of the chemical data and the near-infrared prediction data is less than 20% of the chemical data SD. The near-infrared prediction model SECV of amylopectin is 1.7516, 1-VR is 0.8818, and RSQ is 0.9127. Bias=-0.014<1.7516(SECV)×0.6 means that Bias is less than 0.6 times of SECV of calibration model, SEP=1.316<1.7516(SECV)×1.3=2.2708 means SEP is less than 1.3 times of SECV of calibration model, 5.30-5.29=0.01<5.30×0.2=1.06, that is, the difference between the chemical data and the near-infrared prediction data SD is less than 20% of the chemical data SD. Using 30 sorghum grains outside the model to conduct a two-pair sample non-parametric test on the validity of the model, the results showed that the difference between the measured and predicted values of amylose content and amylopectin content was not significant (P=0.262>0.05; P=0.992>0.05).【Conclusion】 The established near-infrared model has high accuracy and good stability, can accurately and quickly detect the content of amylose and amylopectin in sorghum, and can be used for the genetic improvement of sorghum and the detection of sorghum quality.

Key words: near infrared spectroscopy, sorghum, amylose, amylopectin, improved least squares method

ZHANG BeiJu,CHEN SongShu,LI KuiYin,LI LuHua,XU RuHong,AN Chang,XIONG FuMin,ZHANG Yan,DONG LiLi,REN MingJian. Construction and Application of Detection Model for Amylose and Amylopectin Content in Sorghum Grains Based on Near Infrared Spectroscopy[J].Scientia Agricultura Sinica, 2022, 55(1): 26-35.

Figures/Tables 8

Table 1

Fig. 1

Fig.2

Table 2

Main evaluation parameters of amylose and amylopectin content in sorghum with different treatment methods"

处理方法 Approach	淀粉类型 Starch type	样品数量 Number of samples	定标标准偏差 SEC	相关系数 RSQ	交叉验证标准偏差 SECV	交叉验证相关系数 1-VR
标准正常化+散射处理+一阶导数SNV+detrend+first derivative	直链淀粉 Amylose	76	2.5045	0.9602	3.2933	0.9316
标准正常化+散射处理+二阶导数SNV+detrend+second derivative	直链淀粉 Amylose	79	2.7621	0.9516	3.3402	0.9298
无散射处理+一阶导数None+first derivative	直链淀粉 Amylose	81	3.3227	0.9288	3.8291	0.9053
无散射处理+二阶导数None+second derivative	直链淀粉 Amylose	81	3.0363	0.9406	3.7766	0.9079
标准正常化处理+一阶导数SNV+first derivative	直链淀粉 Amylose	75	2.7078	0.9508	3.3899	0.9229
标准正常化处理+二阶导数SNV+second derivative	直链淀粉 Amylose	76	2.1958	0.9688	2.7732	0.9503
去散射处理+一阶导数Detrend only+first derivative	直链淀粉 Amylose	80	3.1756	0.9332	3.7943	0.9045
去散射处理+二阶导数Detrend only+second derivative	直链淀粉 Amylose	81	3.2852	0.9304	3.6507	0.9139
多元离散校正+一阶导数MSC+first derivative	直链淀粉 Amylose	73	2.6378	0.9540	3.3120	0.9273
多元离散校正+二阶导数MSC+second derivative	直链淀粉 Amylose	79	2.4211	0.9624	2.9392	0.9447
反向多元离散校正+一阶导数Inverse-MSC+first derivative	直链淀粉 Amylose	76	3.0152	0.9409	3.4970	0.9208
反向多元离散校正+二阶导数Inverse-MSC+second derivative	直链淀粉 Amylose	77	3.3412	0.9245	3.5641	0.9138
加权散射校正+一阶导数Weighted MSC++first derivative	直链淀粉 Amylose	79	2.7588	0.9513	3.8374	0.9065
加权散射校正+二阶导数Weighted MSC+second derivative	直链淀粉 Amylose	80	2.7625	0.9507	3.3601	0.9273
标准正常化+散射处理+一阶导数SNV+detrend+first derivative	支链淀粉 Amylopectin	43	1.4723	0.9173	1.9041	0.8602
标准正常化+散射处理+二阶导数SNV+detrend+second derivative	支链淀粉Amylopectin	44	1.3861	0.9209	1.9073	0.8486
无散射处理+一阶导数None+first derivative	支链淀粉Amylopectin	44	1.9477	0.8434	2.3981	0.7607
无散射处理+二阶导数None+second derivative	支链淀粉Amylopectin	46	2.2004	0.8217	2.6703	0.7353
标准正常化处理+一阶导数SNV+first derivative	支链淀粉Amylopectin	44	1.5098	0.9127	1.7516	0.8818
标准正常化处理+二阶导数SNV+second derivative	支链淀粉Amylopectin	45	2.1107	0.8368	2.2006	0.8209
去散射处理+一阶导数Detrend only+first derivative	支链淀粉Amylopectin	46	2.2710	0.8100	2.5787	0.7531
去散射处理+二阶导数Detrend only+second derivative	支链淀粉Amylopectin	46	2.3122	0.8031	2.6936	0.7306
多元离散校正+一阶导数MSC+first derivative	支链淀粉Amylopectin	41	1.4930	0.9119	1.7521	0.8794
多元离散校正+二阶导数MSC+second derivative	支链淀粉Amylopectin	45	2.0743	0.8424	2.1851	0.8234
反向多元离散校正+一阶导数Inverse-MSC+first derivative	支链淀粉Amylopectin	44	1.5827	0.9029	1.7719	0.8790
反向多元离散校正+二阶导数Inverse-MSC+second derivative	支链淀粉Amylopectin	45	1.3854	0.9305	1.8142	0.8795
加权散射校正+一阶导数Weighted MSC+first derivative	支链淀粉Amylopectin	42	1.6364	0.8718	2.0300	0.8010
加权散射校正+二阶导数Weighted MSC+second derivative	支链淀粉Amylopectin	42	1.6410	0.8918	2.0391	0.8339

Table 2

Fig. 3

Table 3

Table 4

Table 5

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性状 Trait	校正集Calibration set				验证集Validation set
性状 Trait	样品数 Sample size	范围 Range	均值 Mean	标准差 SD	样品数 Sample size	范围 Range	均值 Mean	标准差 SD
直链淀粉Amylose	82	1.08—40.08	18.23	12.49	30	6.81—40.08	20.59	11.01
支链淀粉Amylopectin	82	26.74—67.95	45.04	9.45	30	29.02—53.96	45.63	5.30

序号 No.	测定值 Measured value		预测值 Predicted value		测定值与预测值差异 Difference of value
序号 No.	直链淀粉Amylose	支链淀粉Amylopectin	直链淀粉Amylose	支链淀粉Amylopectin	直链淀粉Amylose	支链淀粉Amylopectin
1	12.030	49.610	12.094	50.893	-0.064	-1.238
2	9.730	48.780	8.933	48.967	0.797	-0.187
3	38.020	29.020	39.256	28.190	-1.236	0.830
4	30.260	38.710	31.733	38.206	-1.472	0.504
5	9.740	48.500	10.817	48.644	-1.077	-0.144
6	38.440	37.160	36.908	37.452	1.532	-0.292
7	40.080	47.100	39.215	45.670	0.865	1.430
8	31.760	41.400	31.298	42.629	0.462	-1.229
9	28.420	43.520	27.056	44.158	1.364	-0.638
10	12.370	47.390	12.00	48.180	0.370	-0.790
11	27.580	46.110	26.088	47.377	1.492	-1.267
12	13.720	52.230	13.368	49.854	0.352	2.376
13	15.550	45.840	14.400	48.761	1.150	-2.921
14	8.890	47.510	9.847	48.098	-0.957	-0.588
15	12.500	47.260	11.744	49.650	0.756	-2.390
16	14.420	46.300	16.154	46.405	-1.734	-0.105
17	13.420	48.460	12.164	47.136	1.256	1.324
18	7.650	47.730	8.144	49.223	-0.494	-1.493
19	6.810	51.890	9.679	53.231	-2.869	-1.341
20	21.380	50.440	19.716	47.841	1.664	2.599
21	9.730	49.870	8.744	48.124	0.986	1.746
22	24.820	47.410	24.288	47.009	0.532	0.401
23	39.620	35.310	38.362	34.939	1.258	0.371
24	24.430	47.140	23.864	45.625	0.566	1.515
25	31.040	42.130	32.280	42.999	-1.240	-0.869
26	34.280	41.390	32.245	42.238	2.035	-0.848
27	15.840	46.650	14.222	46.316	1.618	0.334
28	10.260	53.960	10.258	52.351	0.002	1.609
29	25.500	45.370	27.481	44.757	-1.981	0.613
30	9.490	44.690	8.549	44.383	0.941	0.307

指标 Index		直链淀粉 Amylose	支链淀粉 Amylopectin
个案数No. of cases		30	30
正态参数^a,bNormal parameter	平均值Mean	20.59	45.63
	标准差SD	11.01	5.30
最极端差值 The most extreme difference	绝对Absolute	0.200	0.183
	正Positive	0.200	0.082
	负Negative	-0.105	-0.183
检验统计Test statistics		0.200	0.183
渐进显著性（双尾）^c Progressive significance (two-tailed)		0.003^c	0.012^c

指标 Index	直链淀粉测定值-直链淀粉预测值 Measured value of amylose-predicted value of amylose	支链淀粉测定值-支链淀粉预测值 Measured value of amylopectin-predicted value of amylopectin
Z统计量Z statistics	-1.121^a	-0.010^a
渐进显著性（双尾） Progressive significance (two-tailed)	0.262	0.992

Construction and Application of Detection Model for Amylose and Amylopectin Content in Sorghum Grains Based on Near Infrared Spectroscopy

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