目的 采用电子鼻技术,探讨建立适合太子参的电子鼻检测方法,分别对不同产地加工、不同产地的太子参气味进行检测。方法 依据获得的客观化气味信息,结合多元统计分析方法对太子参进行产地加工方法和产地的区分判别。结果 太子参烫干品与晒干品的气味差异较大,不同水烫时间的烫干品之间没有差异。主成分分析法(PCA)对不同产地的太子参区分能力较弱,山东、安徽、福建等3个产地的太子参区分结果不明显。判别因子分析法(DFA)对不同产地太子参区分效果显著,通过该方法建立的模型正确判别率能达到100%。结论 研究表明,通过运用电子鼻技术对太子参气味特征的表征,并与多元统计分析方法结合,可实现利用太子参特殊气味对太子参不同产地加工、不同产地样品鉴别,且区分效果较为理想,为药材质量鉴定提供一种技术手段,为今后对中药的气味客观化表达及产地、加工区分提供思路和借鉴。
Abstract
OBJECTIVE Pseudostellaria Radix, due to its sketchy description and individual difference during the odor evaluation, fails to come to researchers' attention. To analyze the odor of the Pseudostellaria Radix samples from different habitat processing methods and areas, and apply electronic nose technology to creating a new method for the determination of Pseudostellaria Radix. METHODS According to the response values obtained by using electronic nose technology, two multivariate statistical methods to the data to distinguish the samples collected from different areas and habitat processing methods were applied. From the aspect of response values, the sunned Pseudostellaria Radix after boiling get lower values than the sunned ones, while the values of the samples from different producing origins vary very slightly. RESULTS The results of the multivariate statistical methods show that principal component analysis (PCA) can differ the sunned samples from the sunned ones after boiling. With regard to the sunned ones after boiling, there is no significant difference among the three boiling time groups from 30 s to 120 s. While the two discrimination models constructed during the discriminant factor analysis (DFA) have respectively favorable identification ability in habitat processing methods and producing areas. CONCLUSION The electronic nose technology can be used to express the odor of Pseudostellaria Radix, and in combination with multivariate statistical methods, it can differentiate Pseudostellaria Radix from different areas and habitat processing methods.
关键词
太子参 /
电子鼻 /
产地 /
产地加工 /
识别模式
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Key words
Pseudostellaria Radix /
electronic nose /
region /
habitat processing method /
identification model
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中图分类号：
R282
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参考文献
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脚注
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基金
北京中医药科技发展资金项目资助(JJ2018-35)
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