Hou Fuguo, Fan Xuehua, Gui Xinjing, Li Han, Li Haiyang, Wang Yanli, Shi Junhan, Zhang Lu, Yao Jing, Li Xuelin, Liu Ruixin
School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China.
Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Front Chem. 2023 Jun 15;11:1188219. doi: 10.3389/fchem.2023.1188219. eCollection 2023.
is rich in volatile components and valuable as a medicine and edible spice. However, the quality of commercially available varies, and issues with mixed sources and adulteration by similar products are common. In addition, due to incomplete identification methods, rapid detection of the purchased quality is still an issue. In this study, we developed qualitative and quantitative evaluation models to assess the variety and quality of using GC, electronic tongue, and electronic nose to provide a rapid and accurate variety and quality evaluation method of . The models performed well; the qualitative authenticity model had an accuracy of 1.00 (n = 64), the accuracy of the qualitative origin model was 0.86 (n = 44), and the quantitative model was optimal on the sensory fusion data from the electronic tongue and electronic nose combined with borneol acetate content, with = 0.7944, RMSEF = 0.1050, and RMSEP = 0.1349. The electronic tongue and electronic nose combined with GC quickly and accurately evaluated the variety and quality of , and the introduction of multi-source information fusion technology improved the model prediction accuracy. This study provides a useful tool for quality evaluation of medicine and food.
富含挥发性成分，作为药物和食用香料很有价值。然而，市售产品的质量参差不齐，常见混合来源和被类似产品掺假的问题。此外，由于鉴定方法不完善，快速检测所购产品的质量仍是一个问题。在本研究中，我们开发了定性和定量评估模型，利用气相色谱法（GC）、电子舌和电子鼻来评估产品的品种和质量，以提供一种快速准确的产品品种和质量评估方法。这些模型表现良好；定性真伪模型的准确率为1.00（n = 64），定性产地模型的准确率为0.86（n = 44），定量模型在结合了电子舌和电子鼻的感官融合数据以及乙酸龙脑酯含量时表现最佳，R² = 0.7944，RMSEF = 0.1050，RMSEP = 0.1349。电子舌和电子鼻结合气相色谱法能够快速准确地评估产品的品种和质量，多源信息融合技术的引入提高了模型预测准确率。本研究为药品和食品的质量评估提供了一个有用的工具。
