Gong Jian-Ting, Zhao Li-Ying, Xu Dong, Li Jia-Hui, Chen Xin, Zou Hui-Qin, Yan Yong-Hong
Beijing Institute of Chinese Medicine Beijing 100035, China.
Beijing Boda Lvzhou Medical Technology Co., Ltd. Beijing 101113, China.
Zhongguo Zhong Yao Za Zhi. 2020 May;45(10):2389-2394. doi: 10.19540/j.cnki.cjcmm.20200221.302.
This study was aimed to develop a simple, rapid and reliable method for identifying Armeniacae Semen Amarum from different processed products and various rancidness degrees. The objective odor information of Armeniacae Semen Amarum was obtained by electronic nose. 105 batches of Armeniacae Semen Amarum samples were studied, including three processed products of Armeniacae Semen Amarum, fried Armeniacae Semen Amarum and peeled Armeniacae Semen Amarum, as well as the samples with various rancidness degrees: without rancidness, slight rancidness, and rancidness. The discriminant models of different processed products and rancidness degrees of Armeniacae Semen Amarum were established by Support Vector Machine(SVM), respectively, and the models were verified based on back estimation of blind samples. The results showed that there were differences in the characteristic response radar patterns of the sensor array of different processed products and the samples with different rancidness degrees. The initial identification rate was 95.90% and 92.45%, whilst validation recognition rate was 95.38% and 91.08% in SVM identification models. In conclusion, differentiation in odor of different processed and rancidness degree Armeniacae Semen Amarum was performed by the electronic nose technology, and different processed and rancidness degrees Armeniacae Semen Amarum were successfully discriminated by combining with SVM. This research provides ideas and methods for objective identification of odor of traditional Chinese medicine, conducive to the inheritance and development of traditional experience in odor identification.
本研究旨在建立一种简单、快速且可靠的方法,用于鉴别不同炮制产品及不同酸败程度的苦杏仁。通过电子鼻获取苦杏仁的目标气味信息。对105批次苦杏仁样品进行研究,包括苦杏仁的三种炮制产品(炒苦杏仁、燀苦杏仁)以及不同酸败程度的样品(无酸败、轻度酸败、酸败)。分别采用支持向量机(SVM)建立苦杏仁不同炮制产品及酸败程度的判别模型,并基于盲样回判对模型进行验证。结果表明,不同炮制产品及不同酸败程度样品的传感器阵列特征响应雷达图存在差异。在SVM识别模型中,初始识别率分别为95.90%和92.45%,验证识别率分别为95.38%和91.08%。综上所述,利用电子鼻技术可对不同炮制及酸败程度苦杏仁的气味进行区分,并结合SVM成功鉴别不同炮制及酸败程度的苦杏仁。本研究为中药气味的客观识别提供了思路和方法,有助于传统气味鉴别经验的传承与发展。