紫外-可见光谱传感对甘草品种及产地的准确鉴别

doi:10.3964/j.issn.1000-0593(2024)09-2647-10

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摘要：甘草是具有补脾益气、清热解毒、祛痰止咳等功能的中药材，由于遗传和地理环境的影响，不同品种和产地的品质及价格差异很大，常有不法商家为了利益以次充好。针对与甘草品质密切相关的甘草酸和甘草苷的结构特性，通过6种能与其发生特异性光学反应的有机染料和金属离子构建了一种甘草品种和产地快速鉴别的光谱传感方法。收集了新疆、内蒙古、宁夏、北京、乌兹别克斯坦五个甘草主产地的175个样品，分别测定甘草提取液和加入不同的金属离子-有机染料的紫外-可见光谱数据，通过比较两者之间紫外-可见光谱的差异，筛选出6种能与甘草提取液发生特异性光学反应的金属离子和有机染料，用于紫外-可见光谱传感点的构建。通过构建的紫外-可见光谱传感方法对采集的所有样品进行检测分析，并结合偏最小二乘判别分析（PLS-DA）和随机森林（RF）算法对甘草的品种和产地进行鉴别。结果显示，原始甘草品种和产地的谱峰在多处重叠，PLS-DA分类结果中的Q²仅为0.75，即纯光谱对甘草能进行初步鉴定但预测准确度不高。为了进一步提高其鉴别准确度，在甘草提取液中加入金属离子和有机染料产生谱峰变化，相比于甘草原始紫外-可见光谱，甘草品种和产地的谱峰都有较为明显的差异。采用矢量编码偏最小二乘判别分析（Dummy codes-PLSDA）和随机森林（RF）算法建立甘草品种和产地的鉴别模型，发现6种传感材料的融合光谱的准确率均达到100%，显著提高了金属离子-有机染料传感阵列对甘草的识别能力，RF模型准确率明显高于PLS-DA模型，其中单一传感点的RF和PLSDA模型的鉴别准确率分别大于98.65%和91.30%，远高于原始光谱的65.22%。通过金属离子-有机染料，构建了一种用于甘草品种和产地准确鉴别的紫外-可见光谱传感方法。为其他中药材的快速鉴别提供了一种新思路，有利于消费者的健康和利益的保障，促进中药资源的可持续发展。

关键词：甘草；金属离子-染料；紫外-可见传感；偏最小二乘判别分析(PLS-DA)；真实性鉴别

Abstract：Gancao is a kind of Chinese herbal medicine with the functions of tonifying the spleen and benefiting the qi, clearing away heat and detoxifying toxins, expelling phlegm, relieving cough, etc. Due to the influence of genetics and geography, there is a great difference in the quality and price of different varieties and places of origin, and there are often unscrupulous merchants who use the second-rate to make the best to gain benefits. Aiming at the structural properties of glycyrrhizic acid and glycyrrhizin, which are closely related to the quality of Gancao, this paper constructs a spectral sensing method for the rapid identification of Gancao varieties and origins through six organic dyes and metal ions that can react with them optically in a specific manner. In this paper, 175 samples from five main Gancao production areas, Xinjiang, Inner Mongolia, Ningxia, Beijing, and Uzbekistan, were collected to measure the UV-visible spectral data of Gancao extracts and the addition of different metal ions-organic dyes, respectively. By comparing the differences in UV-visible spectrograms, we have screened out six metal ions and organic dyes that can react with Gancao extracts with specific optical reactions and used them to construct UV-visible sensing points. All the collected samples were then detected and analyzed by the constructed UV-visible spectral sensing method and combined with partial least squares discriminant analysis (PLS-DA) and random forest (RF) algorithms to identify the varieties and origins of Glycyrrhiza glabra. The results showed that the spectral peaks of the original Gancao species and origin overlapped in several places, and the Q2 in the PLS-DA classification results was only 0.75. That is., the pure spectra provided preliminary identification of Gancao but with low predictive accuracy. To further improve its identification accuracy, metal ions, and organic dyes were added to the Gancao extracts to produce spectral peak variations, which were more pronounced for both Gancao species and origin compared to the original UV-Vis spectra of Gancao. Using vector-coded partial least squares discriminant analysis (Dummy codes-PLSDA) and random forest (RF) algorithms to establish identification models of Gancao species and origin, it was found that the accuracies of the fusion spectra of the six sensing materials reached 100%, which significantly improved the recognition ability of the metal ion-organic dye sensing array for Gancao, and the accuracy of the RF model was significantly higher than that of the PLS-DA model, in which the identification accuracies of RF and PLSDA models with a single sensing site were greater than 98.65% and 91.30%, respectively, and much higher than 65.22% of the original spectra. Therefore, in this paper, an ultraviolet-visible spectral sensing method was constructed to accurately identify Gancao species and their origin using metal ions-organic dyes-. The method provides a new idea for rapidly identifying other Chinese herbal medicines, protecting consumers' health and interests and promoting sustainable development of Chinese medicine resources.

Key words：Gancao; Metal ions-dye; UV-VIS sensor; PLS-DA; Authenticity identification

收稿日期: 2024-04-24 修订日期: 2024-07-17

中图分类号:

TQ460.7+2

基金资助: 国家自然科学基金项目（22378433），中南民族大学中央高校基本科研业务费（YZY22001, CZQ23005）资助

通讯作者: 陈亨业 E-mail: chenhengye@126.com

作者简介: 胡爱芬，女，2002年生，中南民族大学药学院药物分析系本科生 e-mail: 2861280951@qq.com

引用本文:

胡爱芬，黄云，任理雪，王欣雨，陈亨业，付海燕. 紫外-可见光谱传感对甘草品种及产地的准确鉴别[J]. 光谱学与光谱分析, 2024, 44(09): 2647-2656.
HU Ai-fen, HUANG Yun, REN Li-xue, WANG Xin-yu, CHEN Heng-ye, FU Hai-yan. Accurate Identification of Gancao Varieties and Origins Using UV-VIS Spectrum Sensor. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2647-2656.

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https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2024)09-2647-10 或 https://www.gpxygpfx.com/CN/Y2024/V44/I09/2647

[1] ZHAO Jian-jun, YONG Jing-jiao, LU Zong-zhi, et al(赵建军, 雍婧姣, 路宗志, 等). Journal of Chinese Medicinal Materials(中药材), 2024, (3): 661.
[2] DENG Tao-mei, PENG Can, PENG Dai-yin, et al(邓桃妹, 彭灿, 彭代银, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2021, 46(11): 2660.
[3] Fan X, Hong T, Yang Q, et al. Food Chemistry, 2022, 378: 132121.
[4] JIN Yu-jing, HUANG Shi-jing(金雨静, 黄世敬). Journal of Beijing University of Traditional Chinese Medicine(北京中医药大学学报), 2024, 47(7): 917.
[5] KANG Min(亢敏). Cereals & Oils(粮食与油脂), 2024, 37(7): 63.
[6] HUANG Jian, XU Jia, FAN Jing-jing, et al(黄健, 徐佳, 范婧婧, 等). Chinese Journal of New Drugs(中国新药杂志), 2024, 33(4): 345.
[7] Kazlauskaite J A, Matulyte I, Marksa M, et al. Pharmaceutics, 2023, 15(2): 464.
[8] Wang J M, Ren C, Bi W H, et al. Journal of Ethnopharmacology, 2023, 303: 115948.
[9] WANG Ling, GU Wei(王岭, 顾伟). Chinese Journal of Cancer Biotherapy(中国肿瘤生物治疗杂志), 2024, 31(4): 416.
[10] Yi Y, Li J, Lai X, et al. Journal of Advanced Research, 2022, 36: 201.
[11] QIN Xiao-pei, JIAO Ling-xia, ZHOU Jue, et al(秦晓佩, 焦凌霞, 周珏, 等). Food Science and Technology(食品科技), 2023, 48(11): 231.
[12] WANG Qing, HONG Ye, LIU Zhen-yu, et al(王晴, 洪叶, 柳振宇, 等). Science and Technology of Food Industry(食品工业科技), 2023, 44(1): 398.
[13] MA Jun, MAN Qiong, YAN Xiao, et al(马骏, 曼琼, 闫潇, 等). Chinese Traditional Patent Medicine(中成药), 2023, 45(8): 2718.
[14] ZHOU Jing-jing, ZHOU Jie, DOU Xia, et al(周晶晶, 周洁, 窦霞, 等). Chinese Archives of Traditional Chinese Medicine(中华中医药学刊), 2024, 42(5): 244.
[15] ZHANG Yu-shan, YUAN Xin-yue, NAN Hong-mei, et al(张玉珊, 袁欣月, 南红梅, 等). Journal of Chinese Medicinal Materials(中药材), 2024, (5): 1208.
[16] TONG Yi-yang, GUO Zhi-yong, WEN Yu, et al(童一洋, 郭志永, 闻宇, 等). West China Journal of Pharmaceutical Sciences(华西药学杂志), 2024, 39(3): 327.
[17] Quan N M, Phung H M, Uyen L, et al. Food Chemistry Advances, 2023, 2: 100281.
[18] LI Yu, ZHANG Ke-can, PENG Li-juan, et al(李宇, 张克灿, 彭丽娟, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2024, 44(1): 103.
[19] WU Chen-lu, WANG Qiu-yue, SUO Tong-chuan, et al(吴晨璐, 王秋悦, 所同川, 等). Chinese Journal of Modern Applied Pharmacy(中国现代应用药学), 2023, 40(6): 736.
[20] Guan Y, Wang S, Lei G, et al. Food Chemistry, 2024, 451: 139442.
[21] Wu M, Fan Y, Zhang J, et al. Food Chemistry, 2024, 447: 138968.
[22] MAO Kang, XUE Jia-qi, CHEN Zhuo, et al(毛康, 薛家奇, 陈卓, 等). Rock and Mineral Analysis(岩矿测试), 2024，8：1.
[23] LANG Duo-yong, LI Xiao-kang, YANG Li, et al(郎多勇, 李小康, 杨丽, 等). Journal of Chinese Medicinal Materials(中药材), 2022, 45(07): 1531.