红色和黑色花生籽仁黄酮含量近红外模型的构建

doi:10.3864/j.issn.0578-1752.2025.07.003

摘要/Abstract

摘要：

【目的】黄酮含量是花生籽仁的重要品质指标之一，近红外光谱分析技术是快速检测花生籽仁黄酮含量的有效方法，然而，种皮颜色差异会影响检测结果的准确性。针对红色、黑色花生分别构建近红外预测模型，为特色花生籽仁黄酮含量的高效快速检测提供保障。【方法】选用232份不同种皮颜色花生种质为材料，其中，红色花生108份、黑色花生124份。以芦丁为标准品（RT：rutin），使用氯化铝显色法测定其总黄酮含量。使用瑞典波通DA7250近红外分析仪（DA7250 Diode Array Analyzer）进行光谱采集，扫描光谱范围为950—1 650 nm。基于全波段的偏最小二乘法（PLS），使用建模软件The Unscrambler X10.4建模，通过不同的导数和散射等光谱预处理方法进行单一处理和复合处理，建立不同定标模型。比较不同模型的相关系数和误差，选择最佳处理方法构建红色、黑色花生籽仁黄酮含量预测模型。以四粒红和冀农黑3号为亲本衍生的重组自交系群体为材料，进行外部交叉验证。【结果】红色花生籽仁黄酮含量为60.33—122.49 mg RT/100 g，平均值为94.34 mg RT/100 g。黑色花生籽仁黄酮含量为64.98—121.55 mg RT/100 g，平均值为95.59 mg RT/100 g。红色花生预测模型最佳光谱预处理方法为“Derivative Savitzky-Golay+SNV+Detrend”，校正相关系数（Rc）为0.9022，交叉验证均方根误差（root mean square error of cross validation，RMSECV）为1.9101，预测相关系数（Rp）为0.9021，预测均方根误差（root mean square error of prediction，RMSEP）为1.9606 mg RT/100 g。外部验证相关系数R²为0.923，预测模型偏差范围为-4.86—8.47 mg/100 g。黑色花生预测模型最佳光谱预处理方法为“Derivative Savitzky-Golay+SNV+Deresolve”，Rc为0.9521，RMSECV为1.6978，Rp为0.915，RMSEP为2.292 mg RT/100 g，外部验证相关性系数R²为0.907，预测模型偏差范围为-4.56—2.87 mg/100 g。用非相应颜色模型进行交叉验证，相关系数为0.0015—0.0975。【结论】花生种皮颜色严重影响花生籽仁黄酮含量的近红外检测精准度，构建的红色、黑色花生黄酮近红外预测模型适用于相应种皮颜色花生的黄酮含量检测。

关键词: 红色花生, 黑色花生, 黄酮含量, 种皮颜色, 近红外模型

Abstract:

【Objective】Flavonoid content is one of the critical quality indicators for peanut seed. Near-infrared spectroscopy (NIR) is an effective method for rapid detection of flavonoid content in peanut. However, the differences of testa color may affect the accuracy of the detection results. Therefore, the construction of NIR prediction models for peanuts with red and black testa can provide a guarantee for efficient and rapid detection of flavonoid content in special peanut kernels. 【Method】In this study, 232 peanut germplasms with different testa colors were selected as materials, including 108 peanut with red testa and 124 peanut with black testa. The total flavonoid content was determined by aluminum chloride chromogenic method, with rutin serving as the standard (RT: rutin). Using the Swedish Broadcom DA7250 Diode Array Analyzer for spectral acquisition, within a scanning spectral range of 950-1 650 nm. Employing the Unscrambler X10.4 modeling software, various calibration models were established through both single and composite processing, utilizing diverse derivative and scattering spectral preprocessing methods, based on full-band partial least squares (PLS) modeling. By comparing the correlation coefficients and errors among different models, the optimal processing method was selected to establish a prediction model for flavonoid content in both red and black peanut kernels. For model external validation, materials were derived from a recombinant inbred line population derived from the parents of Silihong and Jinonghei 3, with 30 lines with red testa and 30 lines with black testa each undergoing external cross-validation.【Result】The results showed that flavonoid content of peanut with red testa was between 60.33-122.49 mg RT/100 g, with an average of 94.34 mg RT/100 g. The flavonoid content of peanut with black testa was between 64.98-121.55 mg RT/100 g, with an average of 95.59 mg RT/100 g. The best spectral pretreatment method of the peanut with red testa prediction model was “Derivative Savitzky-Golay+ SNV+Detrend”, yielding a correction correlation coefficient (Rc) of 0.9022, a root means square error of cross validation (RMSECV) of 1.9101, a prediction correlation coefficient (Rp) of 0.9021, and a root mean square error of prediction (RMSEP) of 1.9606 mg RT/100 g. The external validation correlation coefficient (R²) was 0.923, with a prediction model deviation range of -4.86-8.47 mg/100 g. The best spectral pre-treatment method for the peanut with black testa prediction model was “Derivative Savitzky-Golay+SNV+Deresolve”,resulting in an Rc of 0.9521, an RMSECV of 1.6978, the correlation coefficient (Rp) of the peanut with black testa prediction model was 0.915, and RMSEP of 2.292 mg RT/100 g, the correlation coefficient R² of external verification was 0.907, with a prediction model deviation range of -4.56-2.87 mg/100 g. Cross-validation was carried out with non-corresponding color models, and the correlation coefficient was between 0.0015-0.0975. 【Conclusion】The testa color strongly affected the accuracy of detection, and the near-infrared prediction models constructed in this study are suitable for the detection of flavonoid content in peanuts with red and black testa,which provide an important selection method for breeding characteristic peanuts with high flavonoids.

Key words: peanut with red testa, peanut with black testa, flavonoid content, testa color, near infrared model

李鑫瑜, 侯名语, 崔顺立, 刘盈茹, 李秀坤, 刘立峰. 红色和黑色花生籽仁黄酮含量近红外模型的构建[J]. 中国农业科学, 2025, 58(7): 1284-1295.

LI XinYu, HOU MingYu, CUI ShunLi, LIU YingRu, LI XiuKun, LIU LiFeng. Construction of Near Infrared Spectrometry Model for Flavonoids Content of Peanut with Red and Black Testa[J]. Scientia Agricultura Sinica, 2025, 58(7): 1284-1295.

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图/表 7

表1

图1

图2

表2

表3

红色花生化学测定值和近红外模型预测值结果比较"

序号 No.	化学测定值 Chemical measured value	预测值 Predicted value			测定值与预测值差异 Difference between measured and predicted values
序号 No.	化学测定值 Chemical measured value	红色花生籽仁黄酮含量近红外检测模型 NIR detection model of peanut with red testa flavonoids	黑色花生籽仁黄酮含量近红外检测模型 NIR detection model of peanut with black testa flavonoids	粉色花生籽仁黄酮含量近红外检测模型 NIR detection model of peanut with pink testa flavonoids	红色花生籽仁黄酮含量近红外检测模型 NIR detection model of peanut with red testa flavonoids	黑色花生籽仁黄酮含量近红外检测模型 NIR detection model of peanut with black testa flavonoids	粉色花生籽仁黄酮含量近红外检测模型 NIR detection model of peanut with pink testa flavonoids
1	100.74	96.72	87.41	71.49	4.02	13.33	29.25
2	84.22	86.78	87.87	72.10	-2.57	-3.65	12.12
3	100.08	97.89	106.09	72.90	2.18	-6.01	27.18
4	94.53	94.88	99.54	73.76	-0.35	-5.01	20.77
5	95.74	95.04	85.89	66.43	0.70	9.85	29.31
6	100.27	98.43	101.71	67.43	1.83	-1.44	32.84
7	113.03	110.78	120.98	83.73	2.25	-7.95	29.30
8	109.22	100.75	97.99	67.83	8.47	11.23	41.39
9	85.60	90.46	94.87	74.01	-4.86	-9.27	11.59
10	116.74	113.57	101.54	67.86	3.18	15.20	48.89
11	91.98	92.62	94.07	74.48	-0.64	-2.09	17.50
12	88.67	89.12	94.61	81.95	-0.45	-5.94	6.72
13	86.53	88.12	85.95	59.10	-1.59	0.58	27.43
14	93.24	94.46	102.72	81.53	-1.21	-9.47	11.71
15	104.03	100.76	116.03	84.30	3.27	-12.00	19.73
16	120.84	118.80	101.60	76.40	2.04	19.24	44.44
17	101.96	99.77	103.84	83.99	2.19	-1.88	17.97
18	96.24	90.60	93.52	62.27	5.65	2.72	33.97
19	96.93	95.27	90.01	70.93	1.67	6.93	26.01
20	106.60	103.08	93.51	73.04	3.52	13.09	33.56
21	94.17	96.36	87.50	67.48	-2.20	6.67	26.69
22	91.25	94.58	80.39	64.07	-3.34	10.86	27.17
23	100.50	98.38	114.62	78.86	2.13	-14.11	21.64
24	91.55	95.78	129.40	81.78	-4.22	-37.85	9.77
25	124.08	120.05	96.84	70.82	4.02	27.24	53.26
26	102.53	100.78	103.08	66.34	1.75	-0.55	36.19
27	95.50	92.89	95.32	65.63	2.61	0.18	29.87
28	102.65	98.00	97.11	66.28	4.65	5.53	36.37
29	113.36	114.68	93.47	66.82	-1.32	19.89	46.54
30	86.17	89.95	84.89	62.40	-3.78	1.28	23.77

表3

表4

黑色花生化学测定值和近红外模型预测值结果比较"

序号 No.	化学测定值 Chemical measured value	预测值 Predicted value			测定值与预测值差异 Difference between measured and predicted values
序号 No.	化学测定值 Chemical measured value	红色花生籽仁黄酮含量近红外检测模型NIR detection model of peanut with red testa flavonoids	黑色花生籽仁黄酮含量近红外检测模型NIR detection model of peanut with black testa flavonoids	粉色花生籽仁黄酮含量近红外检测模型NIR detection model of peanut with pink testa flavonoids	红色花生籽仁黄酮含量近红外检测模型NIR detection model of peanut with red testa flavonoids	黑色花生籽仁黄酮含量近红外检测模型NIR detection model of peanut with black testa flavonoids	粉色花生籽仁黄酮含量近红外检测模型NIR detection model of peanut with pink testa flavonoids
1	99.46	90.86	100.96	69.73	8.60	-1.50	29.73
2	95.36	89.75	98.49	70.84	5.61	-3.12	24.52
3	99.03	94.34	99.59	66.64	4.69	-0.56	32.39
4	77.50	94.26	79.70	62.99	-16.76	-2.19	14.51
5	92.84	90.49	96.11	65.57	2.35	-3.27	27.27
6	89.24	90.45	87.09	70.72	-1.21	2.15	18.52
7	86.31	91.69	87.29	76.02	86.31	-0.97	10.30
8	100.93	93.69	101.97	67.91	7.24	-1.04	33.02
9	85.55	85.86	86.79	61.57	-0.31	-1.23	23.99
10	100.46	94.98	101.10	74.34	5.48	-0.64	26.12
11	93.98	89.00	97.79	69.67	4.98	-3.82	24.31
12	82.65	93.53	83.31	69.66	-10.89	-0.67	12.99
13	91.22	116.75	93.21	85.81	-25.53	-1.99	5.41
14	94.81	89.57	96.50	68.09	5.24	-1.68	26.73
15	95.96	104.26	93.76	72.79	-8.30	2.20	23.16
16	102.03	90.35	100.34	68.13	11.68	1.69	33.90
17	102.08	94.85	99.21	72.56	7.23	2.87	29.52
18	84.74	91.18	89.30	63.85	-6.44	-4.56	20.89
19	90.81	94.40	89.79	65.71	-3.58	1.02	25.11
20	90.03	104.64	97.99	71.88	-14.61	-7.96	18.15
21	88.39	94.55	88.55	71.45	-6.16	-0.16	16.94
22	93.26	93.58	94.09	66.59	-0.32	-0.83	26.67
23	82.84	89.47	81.51	77.70	-6.63	1.33	5.14
24	81.46	98.13	81.06	68.72	-16.67	0.39	12.74
25	87.89	95.14	89.06	66.02	-7.25	-1.18	21.87
26	95.74	89.68	93.14	65.51	6.07	2.60	30.23
27	97.10	105.47	98.04	72.53	-8.37	-0.94	24.58
28	90.06	101.49	93.24	82.16	-11.42	-3.18	7.90
29	92.31	102.18	93.13	76.84	-9.87	-0.81	15.47
30	84.98	95.88	87.78	70.50	-10.90	-2.80	14.48

表4

图3

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种皮颜色 Testa color	平均值 Mean (mg RT/100 g)	标准差 SD	最大值 Max (mg RT/100 g)	最小值 Min (mg RT/100 g)	变异系数 CV (%)
红色Red	94.34	11.13	122.49	60.33	11.80
黑色Black	95.59	9.57	121.55	64.98	10.01

种皮颜色 Testa color	预处理方法 Pre-processing method	校正相关系数 Rc	交叉验证均方根误差 RMSECV	预测相关系数 Rp	预测均方根误差 RMSEP
黑色 Black	原始数据Original data	0.488	5.960	0.456	6.309
	Derivative Savitzky-Golay	0.759	4.056	0.636	4.969
	Normalize	0.661	4.899	0.550	5.568
	Baseline	0.629	5.049	0.538	5.608
	SNV	0.705	4.552	0.630	5.065
	Spectroscopic	0.572	5.082	0.528	5.303
	Detrend	0.693	4.626	0.679	4.788
	Deresolve	0.669	4.581	0.602	5.071
	Derivative Savitzky-Golay+Baseline	0.909	2.252	0.853	2.868
	Derivative Savitzky-Golay+Baseline+Detrend	0.910	2.406	0.864	3.015
	Derivative Savitzky-Golay+SNV	0.904	2.457	0.846	3.337
	Derivative Savitzky-Golay+Deresolve	0.901	2.655	0.851	3.301
	Derivative Savitzky-Golay+SNV+Deresolve	0.952	1.698	0.915	2.292
红色 Red	原始数据Original data	0.589	4.216	0.488	4.702
	Derivative Savitzky-Golay	0.751	3.300	0.631	4.027
	Baseline	0.594	4.797	0.510	5.297
	SNV	0.607	5.105	0.530	5.679
	Spectroscopic	0.581	5.052	0.510	5.559
	Detrend	0.619	4.884	0.532	5.484
	Deresolve	0.575	5.135	0.456	6.079
	Derivative Savitzky-Golay+SNV	0.873	2.235	0.808	2.830
	Derivative Savitzky-Golay+Deresolve	0.900	2.009	0.834	2.635
	Derivative Savitzky-Golay+Detrend	0.890	2.124	0.828	2.614
	Derivative Savitzky-Golay+Deresolve+SNV	0.904	1.888	0.851	2.372
	Derivative Savitzky-Golay+Detrend+Baseline	0.909	1.855	0.841	2.841
	Derivative Savitzky-Golay+SNV+Detrend	0.902	1.910	0.902	1.961