摘要
天麻在《神农本草经》中列为上品,其主要分布在我国西南地区,不同产地质量差别较大,野生与栽培品价格差别尤为悬殊。用传统性状鉴别方法难以客观准确区分天麻的质量,故本文采用近红外光谱技术结合模式识别方法,建立能够快速准确区分不同产地和不同生长、栽培方式的天麻药材的方法。结果表明,利用近红外光谱技术结合多类分类算法,可以实现天麻产地和栽培方式的快速判别,其交叉验证判别准确率分别达到94.3%和96.4%,且所建多类分类模型中光谱数据未经预处理。因此,本研究所建立的天麻产地和质量的快速判定方法可以推广为现场应用。
Gastrodia elatais graded as top medication in theShen Nong’s Herbal Classic. It was mainly distributed in＆amp;nbsp;southwest China. Its quality varied with geographical location. And the quality difference between wild and cultivated sample was extreme. Identifications using traditional methods were unable to accurately distinguish the quality ofG. elata. Therefore, near-infrared （NIR） spectroscopy combined with pattern recognition method was used to distinguish the quality ofG. elata from different geographical locations as well as cultivated or wild. The results demonstrated that using NIR spectroscopy combined with multiclass classification algorithm, the geographical location ofG. elata can be accurately distinguished. The prediction accuracy can reach as high as 94.3% and 96.4% for both applications. Besides, the classification model was built without preprocessing; hence, it can be extended to be applied on-site.
出处
《世界科学技术-中医药现代化》
2015年第7期1405-1408,共4页
Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology
基金
科学技术部"国家重大新药创新"科技重大专项():清开灵注射液安全性关键技术研究
负责人:乔延江
关键词
天麻
产地判别
野生
近红外光谱
多类分类算法
Gastrodia elata
geographical location
wild
near-infrared spectroscopy
multiclass classifier
