基于近红外技术的覆盆子药材品质分析

doi:10.16178/j.issn.0528-9017.20250226

浙江农业科学 ›› 2025, Vol. 66 ›› Issue (9): 2126-2136.DOI: 10.16178/j.issn.0528-9017.20250226

基于近红外技术的覆盆子药材品质分析

汪传宝¹(), 胡淳莉², 占建勇³, 李海申¹, 姜玲⁴, 郑平汉⁵, 王志安¹, 孙健¹^,⁶^,^*()

1.浙江省中药研究所有限公司,浙江杭州 310023
2.淳安县食品药品检测中心,浙江淳安 311700
3.浙江省淳安县农业农村局,浙江淳安 311700
4.淳安县农业农村发展服务中心,浙江淳安 311700
5.淳安县临岐镇中药材办公室,浙江淳安 311703
6.道地药材品质保障与资源持续利用全国重点实验室, 北京 100700

收稿日期:2025-03-25 出版日期:2025-09-11 发布日期:2025-10-14
通讯作者: 孙健,男,山东莱州人,高级工程师,博士,主要从事中药材抗性育种和生态栽培,E-mail: 21116025@zju.edu.cn。
作者简介:汪传宝,男,安徽黄山人,硕士,主要从事中药资源品质评价,E-mail: 1712740643@qq.com。
基金资助:
中央本级重大增减支项目“名贵中药资源可持续利用能力建设项目”(2060302-2202-09);浙江省农业(中药材新品种选育)新品种选育重大科技专项(2021C02074);浙江省科技计划(省属科研院所扶持专项)

Quality analysis of Rubus chingii Hu. based on near-infrared technology

WANG Chuanbao¹(), HU Chunli², ZHAN Jianyong³, LI Haishen¹, JIANG Ling⁴, ZHENG Pinghan⁵, WANG Zhian¹, SUN Jian¹^,⁶^,^*()

1. Zhejiang Research Institute of Traditional Chinese Medicine Co., Ltd., Hangzhou 310023, Zhejiang
2. Chun 'an County Food and Drug Testing Center, Chun'an 311700, Zhejiang
3. Zhejiang Chun'an County Agricultural and Rural Bureau, Chun'an 311700, Zhejiang
4. Chun'an County Agricultural and Rural Development Service Center, Chun'an 311700, Zhejiang
5. Chun'an County Linqi Town Chinese Herbal Medicine Office, Chun'an 311703, Zhejiang
6. State Key Laboratory for Quality Ensurance and Sustainable Use of Genuine Herbs, Beijing 100700

Received:2025-03-25 Online:2025-09-11 Published:2025-10-14

摘要/Abstract

摘要：

为构建基于近红外光谱数据的定量预测模型,实现对覆盆子中鞣花酸和山柰酚-3-O-芸香糖苷含量的快速预测,本研究收集了不同来源的覆盆子样本,采集其近红外光谱(NIR),通过高效液相(HPLC)检测其鞣花酸和山柰酚-3-O-芸香糖苷的含量,运用MATLAB R2020b软件对光谱数据进行预处理,运用竞争自适应重加权抽样CARS筛选特征波长,建立偏最小二乘(PLS)和随机森林(RF)模型筛选出最佳预处理方法,进而筛选出最佳的预测模型。结果表明,应用SNV+FD+CARS+PLS模型可获得鞣花酸的最优预测结果,定量模型测试集的相关系数($R_{\mathrm{p}}^{2}$)为0.903 8,应用SG+FD+CARS+PLS模型可获得山柰酚-3-O-芸香糖苷的最优预测结果,$R_{\mathrm{p}}^{2}$为0.758 6。经归一化和一阶导数组合处理的近红外数据在正交偏最小二乘判别分析(OPLS-DA)模型中能够判别区分覆盆子合格品,累积方差值R²Y为0.728,预测率Q²为0.681。本研究结果表明,应用近红外光谱技术可以快速预测覆盆子中鞣花酸和山柰酚-3-O-芸香糖苷的含量,对覆盆子样品进行品质初步判别。

关键词: 覆盆子, 鞣花酸, 山柰酚-3-O-芸香糖苷, 近红外光谱

Abstract:

To establish a quantitative prediction model based on near-infrared spectroscopy data, and to realize the rapid prediction of ellagic acid and kaempferol-3-O-rutinoside content in Rubus chingii Hu., the samples from different sources were collected, the near-infrared spectroscopy was collected, and the contents of ellagic acid and kaempferol-3-O-rutinoside were detected by HPLC. Spectral data were preprocessed by MATLAB R2020b software. The competitive adaptive reweighted sampled CARS were used to screen characteristic wavelengths, and partial least squares (PLS) and random forest (RF) models were established to screen the best pretreatment methods, and then the best prediction models were selected. Results showed that the optimal prediction results of ellagic acid were obtained by SNV+FD+CARS+PLS model, and the correlation coefficient ($R_{\mathrm{p}}^{2}$) of the quantitative model test set was 0.903 8. The optimal prediction results of kaempferol-3-O-rutinoside were obtained by SG+FD+CARS+PLS model, and the $R_{\mathrm{p}}^{2}$ of the quantitative model test set was 0.758 6. The NIR data treated by the combination of normalization and first derivative can distinguish Rubus chingii Hu. qualified products in OPLS-DA model, the cumulative variance value R²Y was 0.728, and the prediction rate Q² was 0.681. The results of this study indicated that the content of ellagic acid and kaempferol-3-O-rutinoside in Rubus chingii Hu. can be quickly predicted by NIR technique, and the quality of raspberry samples can be preliminarily judged.

Key words: Rubus chingii Hu., ellagic acid, kaempferol-3-O-rutinoside, near infrared spectrum

中图分类号:

S567.9

汪传宝, 胡淳莉, 占建勇, 李海申, 姜玲, 郑平汉, 王志安, 孙健. 基于近红外技术的覆盆子药材品质分析[J]. 浙江农业科学, 2025, 66(9): 2126-2136.

WANG Chuanbao, HU Chunli, ZHAN Jianyong, LI Haishen, JIANG Ling, ZHENG Pinghan, WANG Zhian, SUN Jian. Quality analysis of Rubus chingii Hu. based on near-infrared technology[J]. Journal of Zhejiang Agricultural Sciences, 2025, 66(9): 2126-2136.

图/表 10

图1 覆盆子样品中鞣花酸和山柰酚-3-O-芸香糖苷含量分布 a,鞣花酸和山柰酚-3-O-芸香糖苷的含量分布散点图;b,鞣花酸含量频率分布直方图;c,山柰酚-3-O-芸香糖苷含量频率分布直方图。

Fig.1 Content distribution of ellagic acid and kaempferol-3-O-rutinoside in Rubus chingii Hu.

图2 覆盆子样本近红外的原始光谱图

Fig.2 Original near-infrared spectrum of Rubus chingii Hu.samples

表1 覆盆子样本组分配

Table 1 Allocation of Rubus chingii Hu. sample groups

类别	样本	样本数量/个	最大值/%	最小值/%	平均值/%	标准偏差
鞣花酸	整体	80	0.516 0	0.047 0	0.177 9	0.101 8
	训练集	56	0.516 0	0.052 3	0.177 7	0.104 0
	测试集	24	0.426 3	0.047 0	0.178 2	0.098 7
山柰酚-3-O-芸香糖苷	整体	80	0.203 1	0.022 3	0.076 9	0.037 1
	训练集	56	0.203 1	0.035 8	0.089 6	0.037 1
	测试集	24	0.064 1	0.022 3	0.047 2	0.011 3

图3 近红外不同预处理方法的OPLS-DA分布图 a表示原始数据,b表示NDH处理,c表示SNV处理,d表示MSC处理,e表示FD处理,f表示NDH+FD处理;A代表全合格品,B代表鞣花酸合格品,C代表山柰酚-3-O-芸香糖苷合格品。

Fig.3 OPLS-DA distribution maps of different near-infrared preprocessing methods

表2 不同数据预处理方法下鞣花酸的预测结果

Table 2 Prediction results of ellagic acid under different data preprocessing methods

模型	预处理方法	训练集			测试集
模型	预处理方法	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
RF	Raw	0.498 9	0.070 6	1.425 4	0.116 6	0.096 5	1.086 8
	NDH	0.645 0	0.063 6	1.693 6	0.413 4	0.064 5	1.333 7
	NDH+FD	0.805 5	0.049 2	2.288 0	0.577 3	0.042 5	1.571 2
	MSC	0.777 6	0.047 6	2.139 8	0.352 1	0.074 5	1.269 1
	MSC+FD	0.635 9	0.057 2	1.672 2	0.599 5	0.069 5	1.614 1
	SG	0.500 8	0.073 7	1.428 1	0.124 3	0.086 3	1.091 6
	SG+FD	0.754 9	0.050 9	2.038 0	0.283 4	0.081 9	1.206 7
	SNV	0.706 2	0.052 8	1.861 7	0.317 1	0.086 8	1.236 1
	SNV+FD	0.781 6	0.047 2	2.159 2	0.413 4	0.076 7	1.333 8
	FD	0.798 2	0.043 6	2.246 0	0.418 8	0.084 1	1.339 9
	SD	0.814 5	0.043 7	2.342 9	0.201 3	0.089 2	1.143 0
PLS	Raw	0.587 9	0.066 6	1.571 9	0.565 3	0.059 8	1.549 4
	NDH	0.627 3	0.063 0	1.652 9	0.400 4	0.074 4	1.319 2
	NDH+FD	0.889 9	0.030 6	3.041 1	0.662 3	0.069 3	1.757 8
	MSC	0.680 0	0.045 8	1.783 8	0.536 1	0.090 8	1.499 8
	MSC+FD	0.929 4	0.024 7	3.797 9	0.585 2	0.075 7	1.586 0
	SG	0.555 0	0.065 8	1.512 7	0.403 1	0.082 4	1.314 7
	SG+FD	0.744 6	0.052 5	1.996 8	0.259 3	0.081 2	1.186 9
	SNV	0.715 4	0.052 0	1.891 6	0.396 9	0.083 5	1.315 3
	SNV+FD	0.899 7	0.030 9	3.186 6	0.735 9	0.055 0	1.987 8
	FD	0.905 0	0.034 2	3.274 3	0.302 6	0.051 4	1.223 2
	SD	0.902 9	0.031 7	3.237 9	0.520 6	0.067 6	1.475 3

表2 不同数据预处理方法下鞣花酸的预测结果

Table 2 Prediction results of ellagic acid under different data preprocessing methods

模型	预处理方法	训练集			测试集
模型	预处理方法	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
RF	Raw	0.498 9	0.070 6	1.425 4	0.116 6	0.096 5	1.086 8
	NDH	0.645 0	0.063 6	1.693 6	0.413 4	0.064 5	1.333 7
	NDH+FD	0.805 5	0.049 2	2.288 0	0.577 3	0.042 5	1.571 2
	MSC	0.777 6	0.047 6	2.139 8	0.352 1	0.074 5	1.269 1
	MSC+FD	0.635 9	0.057 2	1.672 2	0.599 5	0.069 5	1.614 1
	SG	0.500 8	0.073 7	1.428 1	0.124 3	0.086 3	1.091 6
	SG+FD	0.754 9	0.050 9	2.038 0	0.283 4	0.081 9	1.206 7
	SNV	0.706 2	0.052 8	1.861 7	0.317 1	0.086 8	1.236 1
	SNV+FD	0.781 6	0.047 2	2.159 2	0.413 4	0.076 7	1.333 8
	FD	0.798 2	0.043 6	2.246 0	0.418 8	0.084 1	1.339 9
	SD	0.814 5	0.043 7	2.342 9	0.201 3	0.089 2	1.143 0
PLS	Raw	0.587 9	0.066 6	1.571 9	0.565 3	0.059 8	1.549 4
	NDH	0.627 3	0.063 0	1.652 9	0.400 4	0.074 4	1.319 2
	NDH+FD	0.889 9	0.030 6	3.041 1	0.662 3	0.069 3	1.757 8
	MSC	0.680 0	0.045 8	1.783 8	0.536 1	0.090 8	1.499 8
	MSC+FD	0.929 4	0.024 7	3.797 9	0.585 2	0.075 7	1.586 0
	SG	0.555 0	0.065 8	1.512 7	0.403 1	0.082 4	1.314 7
	SG+FD	0.744 6	0.052 5	1.996 8	0.259 3	0.081 2	1.186 9
	SNV	0.715 4	0.052 0	1.891 6	0.396 9	0.083 5	1.315 3
	SNV+FD	0.899 7	0.030 9	3.186 6	0.735 9	0.055 0	1.987 8
	FD	0.905 0	0.034 2	3.274 3	0.302 6	0.051 4	1.223 2
	SD	0.902 9	0.031 7	3.237 9	0.520 6	0.067 6	1.475 3

表3 不同数据预处理方法下山柰酚-3-O-芸香糖苷的预测结果

Table 3 Prediction results of kaempferol-3-O-rutinoside under different data preprocessing methods

模型	预处理方法	训练集			测试集
模型	预处理方法	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
RF	Raw	0.559 3	0.023 1	1.520 0	0.142 4	0.037 0	1.103 1
	NDH	0.711 4	0.019 4	1.878 3	0.252 4	0.033 5	1.181 4
	NDH+FD	0.737 2	0.019 7	1.968 2	0.527 7	0.022 6	1.486 4
	MSC	0.705 8	0.020 3	1.860 5	0.258 8	0.030 5	1.186 5
	MSC+FD	0.747 2	0.018 9	2.007 0	0.267 7	0.030 4	1.193 7
	SG	0.676 1	0.017 6	1.773 1	0.195 1	0.041 2	1.138 6
	SG+FD	0.761 1	0.017 3	2.064 5	0.466 8	0.029 4	1.398 9
	SNV	0.676 6	0.021 3	1.774 3	0.203 3	0.032 0	1.144 4
	SNV+FD	0.720 5	0.018 1	1.908 5	0.443 8	0.031 8	1.369 8
	FD	0.752 8	0.019 7	2.029 4	0.376 3	0.022 2	1.293 5
	SD	0.785 1	0.015 8	2.176 7	0.191 1	0.038 1	1.135 8
PLS	Raw	0.414 3	0.022 9	1.318 5	0.402 2	0.037 8	1.321 2
	NDH	0.512 5	0.024 9	1.445 2	0.204 4	0.034 9	1.145 2
	NDH+FD	0.919 7	0.009 1	3.560 1	0.435 5	0.033 7	1.359 6
	MSC	0.534 5	0.027 9	1.497 0	0.185 1	0.022 8	1.131 6
	MSC+FD	0.924 8	0.010 0	3.678 4	0.226 3	0.033 2	1.161 3
	SG	0.413 9	0.029 3	1.318 0	0.216 8	0.027 4	1.154 3
	SG+FD	0.899 8	0.010 4	3.187 0	0.585 7	0.028 0	1.587 0
	SNV	0.527 6	0.027 9	1.468 1	0.299 3	0.020 8	1.220 3
	SNV+FD	0.888 4	0.011 4	3.021 1	0.546 9	0.028 7	1.517 5
	FD	0.867 3	0.012 8	2.769 8	0.454 2	0.030 3	1.382 6
	SD	0.871 7	0.013 2	2.816 6	0.475 2	0.026 7	1.410 1

表3 不同数据预处理方法下山柰酚-3-O-芸香糖苷的预测结果

Table 3 Prediction results of kaempferol-3-O-rutinoside under different data preprocessing methods

模型	预处理方法	训练集			测试集
模型	预处理方法	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
RF	Raw	0.559 3	0.023 1	1.520 0	0.142 4	0.037 0	1.103 1
	NDH	0.711 4	0.019 4	1.878 3	0.252 4	0.033 5	1.181 4
	NDH+FD	0.737 2	0.019 7	1.968 2	0.527 7	0.022 6	1.486 4
	MSC	0.705 8	0.020 3	1.860 5	0.258 8	0.030 5	1.186 5
	MSC+FD	0.747 2	0.018 9	2.007 0	0.267 7	0.030 4	1.193 7
	SG	0.676 1	0.017 6	1.773 1	0.195 1	0.041 2	1.138 6
	SG+FD	0.761 1	0.017 3	2.064 5	0.466 8	0.029 4	1.398 9
	SNV	0.676 6	0.021 3	1.774 3	0.203 3	0.032 0	1.144 4
	SNV+FD	0.720 5	0.018 1	1.908 5	0.443 8	0.031 8	1.369 8
	FD	0.752 8	0.019 7	2.029 4	0.376 3	0.022 2	1.293 5
	SD	0.785 1	0.015 8	2.176 7	0.191 1	0.038 1	1.135 8
PLS	Raw	0.414 3	0.022 9	1.318 5	0.402 2	0.037 8	1.321 2
	NDH	0.512 5	0.024 9	1.445 2	0.204 4	0.034 9	1.145 2
	NDH+FD	0.919 7	0.009 1	3.560 1	0.435 5	0.033 7	1.359 6
	MSC	0.534 5	0.027 9	1.497 0	0.185 1	0.022 8	1.131 6
	MSC+FD	0.924 8	0.010 0	3.678 4	0.226 3	0.033 2	1.161 3
	SG	0.413 9	0.029 3	1.318 0	0.216 8	0.027 4	1.154 3
	SG+FD	0.899 8	0.010 4	3.187 0	0.585 7	0.028 0	1.587 0
	SNV	0.527 6	0.027 9	1.468 1	0.299 3	0.020 8	1.220 3
	SNV+FD	0.888 4	0.011 4	3.021 1	0.546 9	0.028 7	1.517 5
	FD	0.867 3	0.012 8	2.769 8	0.454 2	0.030 3	1.382 6
	SD	0.871 7	0.013 2	2.816 6	0.475 2	0.026 7	1.410 1

图4 覆盆子鞣花酸和山柰酚-3-O-芸香糖苷的CARS波长光谱选择图图A和C分别为鞣花酸和山柰酚-3-O-芸香糖苷的抽样变量、RMSECV值和回归系数路径变化趋势。图B和D分别为鞣花酸和山柰酚-3-O-芸香糖苷通过CARS绘制的波长分布图。

Fig.4 CARS wavelength spectral selection diagrams of ellagic acid and kaempferol-3-O-rutinoside from Rubus chingii Hu.

表4 CARS算法处理后不同数据预处理方法下的鞣花酸预测结果

Table 4 Prediction results of ellagic acid under different data preprocessing methods after the CARS algorithm processing

模型	预处理方法	变量/ 个	训练集			测试集
模型	预处理方法	变量/ 个	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
CARS-RF	NDH+FD+CARS	41	0.804 1	0.044 0	2.279 5	0.363 4	0.084 0	1.280 3
	MSC+FD+CARS	36	0.769 1	0.042 6	2.099 7	0.467 7	0.090 1	1.400 1
	SG+FD+CARS	8	0.627 0	0.067 1	1.652 2	0.375 0	0.060 4	1.292 1
	SNV+FD+CARS	28	0.768 3	0.049 6	2.096 2	0.516 5	0.065 4	1.469 1
CARS-PLS	NDH+FD+CARS	41	0.918 3	0.028 9	3.530 2	0.815 5	0.043 2	2.378 4
	MSC+FD+CARS	36	0.914 8	0.028 7	3.456 4	0.810 0	0.046 7	2.343 8
	SG+FD+CARS	8	0.654 7	0.061 9	1.717 1	0.559 1	0.059 5	1.538 4
	SNV+FD+CARS	28	0.930 7	0.025 9	3.833 1	0.903 8	0.032 6	3.292 6

表4 CARS算法处理后不同数据预处理方法下的鞣花酸预测结果

Table 4 Prediction results of ellagic acid under different data preprocessing methods after the CARS algorithm processing

模型	预处理方法	变量/ 个	训练集			测试集
模型	预处理方法	变量/ 个	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
CARS-RF	NDH+FD+CARS	41	0.804 1	0.044 0	2.279 5	0.363 4	0.084 0	1.280 3
	MSC+FD+CARS	36	0.769 1	0.042 6	2.099 7	0.467 7	0.090 1	1.400 1
	SG+FD+CARS	8	0.627 0	0.067 1	1.652 2	0.375 0	0.060 4	1.292 1
	SNV+FD+CARS	28	0.768 3	0.049 6	2.096 2	0.516 5	0.065 4	1.469 1
CARS-PLS	NDH+FD+CARS	41	0.918 3	0.028 9	3.530 2	0.815 5	0.043 2	2.378 4
	MSC+FD+CARS	36	0.914 8	0.028 7	3.456 4	0.810 0	0.046 7	2.343 8
	SG+FD+CARS	8	0.654 7	0.061 9	1.717 1	0.559 1	0.059 5	1.538 4
	SNV+FD+CARS	28	0.930 7	0.025 9	3.833 1	0.903 8	0.032 6	3.292 6

表5 CARS算法处理后不同数据预处理方法下的山柰酚-3-O-芸香糖苷预测结果

Table 5 Prediction results of kaempferol-3-O-rutinoside under different data preprocessing methods after the CARS algorithm processing

模型	预处理方法	变量/ 个	训练集			测试集
模型	预处理方法	变量/ 个	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
CARS-RF	NDH+FD+CARS	15	0.722 1	0.018 6	1.914 1	0.397 4	0.031 3	1.315 9
	MSC+FD+CARS	18	0.702 7	0.021 5	1.850 7	0.625 3	0.018 3	1.668 8
	SG+FD+CARS	21	0.759 1	0.019 6	2.056 0	0.492 0	0.020 3	1.433 2
	SNV+FD+CARS	34	0.732 4	0.019 3	1.950 7	0.477 9	0.026 1	1.413 7
CARS-PLS	NDH+FD+CARS	15	0.821 0	0.015 5	2.384 8	0.749 9	0.024 3	2.057 3
	MSC+FD+CARS	18	0.817 5	0.014 8	2.361 7	0.598 6	0.026 4	1.612 2
	SG+FD+CARS	21	0.849 7	0.012 2	2.602 5	0.758 6	0.023 2	2.078 9
	SNV+FD+CARS	34	0.838 2	0.014 4	2.508 8	0.752 4	0.019 3	2.052 8

表5 CARS算法处理后不同数据预处理方法下的山柰酚-3-O-芸香糖苷预测结果

Table 5 Prediction results of kaempferol-3-O-rutinoside under different data preprocessing methods after the CARS algorithm processing

模型	预处理方法	变量/ 个	训练集			测试集
模型	预处理方法	变量/ 个	$R c 2$	RMSEC	RPD	$R p 2$	RMSEP	RPD
CARS-RF	NDH+FD+CARS	15	0.722 1	0.018 6	1.914 1	0.397 4	0.031 3	1.315 9
	MSC+FD+CARS	18	0.702 7	0.021 5	1.850 7	0.625 3	0.018 3	1.668 8
	SG+FD+CARS	21	0.759 1	0.019 6	2.056 0	0.492 0	0.020 3	1.433 2
	SNV+FD+CARS	34	0.732 4	0.019 3	1.950 7	0.477 9	0.026 1	1.413 7
CARS-PLS	NDH+FD+CARS	15	0.821 0	0.015 5	2.384 8	0.749 9	0.024 3	2.057 3
	MSC+FD+CARS	18	0.817 5	0.014 8	2.361 7	0.598 6	0.026 4	1.612 2
	SG+FD+CARS	21	0.849 7	0.012 2	2.602 5	0.758 6	0.023 2	2.078 9
	SNV+FD+CARS	34	0.838 2	0.014 4	2.508 8	0.752 4	0.019 3	2.052 8

图5 CARS-PLS模型下的鞣花酸和山柰酚-3-O-芸香糖苷测量值和预测值的散点图 A,鞣花酸训练集;B,鞣花酸测试集;C,山柰酚-3-O-芸香糖苷训练集;D,山柰酚-3-O-芸香糖苷测试集。

Fig.5 Scatter plots of the measured and predicted values of ellagic acid and kaempferol-3-O-rutinoside under CARS-PLS model

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基于近红外技术的覆盆子药材品质分析

Quality analysis of Rubus chingii Hu. based on near-infrared technology

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