Li Lian, Li Zhi Min, Wang Yuan Zhong
Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, People's Republic of China.
College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, People's Republic of China.
Plant Methods. 2022 Aug 13;18(1):102. doi: 10.1186/s13007-022-00935-6.
Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy.
(1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions.
This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.
杜仲叶作为一种药食同源植物，是一种具有很大发展潜力的优质工业原料，有望成为一种有价值的经济作物。影响杜仲叶品质的因素众多，如干燥方法和产地不同等。因此，其质量安全受到全球关注，利用人工智能技术鉴定药用植物成为一种趋势。在本研究中，我们试图评估杜仲叶不同干燥方法及产地溯源的比较与鉴别。作为一种优越策略，基于傅里叶变换近红外光谱的二维相关光谱（2DCOS）被直接与残差神经网络（ResNet）相结合。
（1）通过探索性分析和层次聚类分析，不同产地的各类样本比不同干燥方法的样本能更好地聚类在一起；（2）共获得3204幅二维相关光谱图像，与异步二维相关光谱和综合二维相关光谱相比，同步二维相关光谱更适合杜仲叶的鉴别与分析；（3）用于鉴别杜仲叶不同干燥方法和产地的基于同步二维相关光谱的优越残差神经网络模型优于偏最小二乘判别模型，训练集、测试集和外部验证的准确率均为100%；（4）通过对19个气候数据与不同产地的相关性分析，新疆样本与其他样本存在显著差异。
本研究通过该实例验证了残差神经网络模型鉴别的优越性，为其他药用植物或真菌的相关研究提供了实际参考。
