School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, 266113, China.
Qingdao Traditional Chinese Medicine Hospital (Qingdao Hiser Hospital), Qingdao University, Qingdao, 266033, China.
Talanta. 2025 Jan 1;282:127016. doi: 10.1016/j.talanta.2024.127016. Epub 2024 Oct 9.
The accurate identification of Radix Astragali holds significant scientific importance for evaluating the quality and medicinal efficacy of this herb. In this study, we introduced an efficient methodology, integrating laser induced breakdown spectroscopy (LIBS) and Raman spectroscopy, to identify Radix Astragali samples. Additionally, convolutional neural network (CNN) models were constructed and trained using low-, mid-, and high-level data fusion strategies. The results demonstrated significant improvements in sample classification using all fusion strategies, surpassing the performance achieved when applying LIBS or Raman data individually. Notably, mid-level fusion achieved the highest level of accuracy (93.44 %), with the low- and high-level fusion methods slightly lower at 88.34 % and 90.10 %, respectively. The newly proposed methodology showcased its significance in the rapid and accurate identification of Radix Astragali samples, thereby improving analytical capabilities in Radix Astragali research.
准确鉴定黄芪具有重要的科学意义，可用于评估这种草药的质量和药用功效。在这项研究中，我们引入了一种高效的方法，结合激光诱导击穿光谱（LIBS）和拉曼光谱，来鉴定黄芪样品。此外，还构建并训练了卷积神经网络（CNN）模型，使用低、中、高级数据融合策略。结果表明，所有融合策略都显著提高了样品分类的准确性，超过了单独使用 LIBS 或拉曼数据的性能。值得注意的是，中级融合达到了最高的准确性（93.44%），而低级和高级融合方法的准确性分别略低，为 88.34%和 90.10%。新提出的方法在快速准确地鉴定黄芪样品方面显示出了重要意义，从而提高了黄芪研究中的分析能力。
