文献类型：期刊文献

中文题名：互联网共享模式下采用(近红外)光谱技术进行中药(材)质量快速检测的探索

英文题名：Exploration of rapidly determining quality of traditional Chinese medicines by( NIR) spectroscopy based on internet sharing mode

作者：倪力军[1];栾绍嵘[1];张立国[1]

机构：[1]华东理工大学化学与分子工程学院,上海200237

年份：2016

卷号：41

期号：19

起止页码：3520

中文期刊名：中国中药杂志

外文期刊名：China Journal of Chinese Materia Medica

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；PubMed(收录号：28925143);

语种：中文

中文关键词：(近红外)光谱技术;中药(材)质量;模型移植;互联网共享模式

外文关键词：NIR spectroscopy; quality of traditional Chinese medicine; calibration transfer; internet sharing mode

摘要：中药品种及药材中活性成分繁多,采用传统方法进行中药质量检测的任务繁重。该研究提出基于(近红外)光谱技术与互联网平台实现中药(材)质量快速检测的思路。通过开发成本低、便携的多源复合光谱仪实现中药样品光谱的现场快速检测,利用互联网建立企业间共享中药样品光谱与质量检测数据的数据库,采用笔者团队提出的KNN保形映射方法(KNNKSR)预测样品中有效成分含量。以58个银杏叶样品的4台近红外光谱与2台多源光谱信息、以及第三方公开数据库的80个玉米样品的3台近红外光谱信息和样品中主要成分含量信息构成的数据库对上述思路进行验证,并与偏最小二乘(PLS)及模型转移结果进行比较,发现KNN-KSR可以在不进行光谱校正的情况下,获得优于传统PLS回归建模的模型移植结果,而PLS方法如果不进行光谱校正,模型移植通常会产生很大误差;多源复合光谱仪对银杏叶总黄酮、总内酯的分析结果与近红外光谱相当,且KNN-KSR结果优于PLS。该研究提出的方法和思路有待积累更多类型样品及测试信息进行验证。
Because of the numerous varieties of herbal species and active ingredients in the traditional Chinese medicine（ TCM）,the traditional methods employed could hardly satisfy the current determination requirements of TCM. The present work proposed an idea to realize rapid determination of the quality of TCM based on near infrared（ NIR） spectroscopy and internet sharing mode. Low cost and portable multi-source composite spectrometer was invented by our group for in-site fast measurement of spectra of TCM samples. The database could be set up by sharing spectra and quality detection data of TCM samples among TCM enterprises based on the internet platform. A novel method called as keeping same relationship between X and Y space based on K nearest neighbors（ KNN-KSR for short） was applied to predict the contents of effective compounds of the samples. In addition,a comparative study between KNN-KSR and partial least squares（ PLS） was conducted. Two datasets were applied to validate above idea： one was about 58 Ginkgo Folium samples samples measured with four near-infrared spectroscopy instruments and two multi-source composite spectrometers,another one was about 80 corn samples available online measured with three NIR instruments. The results show that the KNN-KSR method could obtain more reliable outcomes without correcting spectrum. However transforming the PLS models to other instruments could hardly acquire better predictive results until spectral calibration is performed. Meanwhile,the similar analysis results of total flavonoids and total lactones of Ginkgo Folium samples are achieved on the multi-source composite spectrometers and near-infrared spectroscopy instruments,and the prediction results of KNN-KSR are better than PLS. The idea proposed in present study is in urgent need of more samples spectra,and then to be verified by more case studies.