摘要
人参是传统中药材中的贵重品种,具有较高的经济价值。人参生长的地域性很强,不同产地人参有效成分含量存在差异,人参因“道地”与否,会导致其质量、医学效用和经济价值的差异,因此人参产地识别的意义重大。目前常通过磨粉提取等制备,再采用化学或光学等多种手段检验人参产地,但会造成样本破坏。而基于外观性状或芦头特征的鉴别,因主观性差异不能作为标准化的识别方法。如何用高精度、无损、快速检测识别的方法,对人参的产地进行识别分析,是该研究的主要立足点。通过采用高光谱成像技术,对已知产地信息的人参样本,通过获取从400~2 500 nm的反射光谱,经过基于白板的绝对和相对辐射校正处理,构建了高光谱反射率数据集。采用随机森林的机器学习方法,构建了基于高光谱数据的全光谱人参产地识别模型,并对不同尺度的地域划分规则分别开展了产地识别精度验证,发现不同产地的人参光谱有明显区别。其中东三省与否的产地识别精度,可以达到98.2%。同时利用随机森林基于决策树构建的优势,获得了人参产地识别的光谱重要性结果,为专用轻量化仪器研发指明特征光谱。高光谱人参产地识别研究作为严格的无损检测方式,将对人参等道地药材的产地识别、药材图谱指纹认知和挖掘、药材鉴定和质量评价等提供理论支撑和技术手段。
Ginseng is a valuable variety of traditional Chinese medicine with high economic value.The growth is very regional,and the effective ingredients of ginseng from different origins are different.Whether ginseng is “authentic” or not,it will cause differences in its quality,medical utility and economic value,so the identification of ginseng origin is of great significance.After powder extraction and other preparations,chemical or optical methods are used to test the origin of ginseng,but this will cause damage to the sample.Besides,the identification based on appearance traits or rhizome head characteristics can not be used as a standardized recognition method because of human subjective differences or easy to be falsified.The main standpoint of this article is how to use high-precision,non-destructive,and rapid detection and identification methods to identify and analyze the origin of ginseng.This experiment uses hyperspectral imaging technology,for ginseng samples with known origin information,the hyperspectral reflectance dataset was constructed by obtaining reflectance spectra from 400 to 2 500 nm,after absolute and relative radiometric corrections based on the whiteboard.A full spectrum ginseng origin recognition model based on hyperspectral data was constructed,and the accuracy of origin recognition was verified for different scales of regional division rules.It was found that the ginseng spectra from different origins were significantly different.The accuracy of origin identification of the northeastern provinces or not can reach 98.2%.The spectral importance results of ginseng origin recognition were given,indicating the characteristic spectrum for developing a special lightweight instrument.As a strict non-destructive detection method,hyperspectral ginseng origin identification research will provide theoretical support and technical means for identifying the origin of authentic Chinese medicinal materials such as ginseng,fingerprint recognition and mining of medicinal materials,identification and quality evaluation,etc.
作者
李梦
张小波
刘绍波
陈兴峰
黄璐琦
史婷婷
杨瑞
刘舒
郑逢杰
LI Meng;ZHANG Xiao-bo;LIU Shao-bo;CHEN Xing-feng;HUANG Lu-qi;SHI Ting-ting;YANG Rui;LIU Shu;ZHENG Feng-jie(School of Pharmacy,Henan University of Chinese Medicine,Zhengzhou 450046,China;State Key Laboratory Breeding Base of Dao-di Herbs,National Resource Center for Chinese Materia Medica,Chinese Academy of Chinese Medical Sciences,Beijing 100700,China;Big Date Center,Space Star Technology Co.,Ltd.,Beijing 100086,China;State Environmental Protection Key Laboratory of Satellite Remote Sensing,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;State Key Laboratory Breeding Base of Dao-di Herbs,Chinese Academy of Chinese Medical Sciences,Beijing 100700,China;Key Laboratory of Remote Sensing of Gansu Province,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China;Jilin Provincial Key Laboratory of Chinese Medicine Chemistry,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences,Changchun 130022,China;School of Space Information,Space Engineering University,Beijing 101416,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2022年第4期1217-1221,共5页
Spectroscopy and Spectral Analysis
基金
国家自然基金重大项目(81891014)
国家科技重大专项“重大新药创制”项目(2019ZX09201-005)
国家中医药管理局委托项目(GZY-KJS-2019-001)
中央本级重大增减支项目(2060302)
吉林省重大科技专项课题人参产业战略提升重大科技专项(20200504001YY-01)资助。
关键词
高光谱
随机森林
可解释性
人参
中药材
产地
Hyperspectral
Random forest
Interpretability
Ginseng
Traditional Chinese medicinal materials
Origin
