Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1
1. College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, China
2. Cooperative Innovation Center of Unconventional Oil and Gas Exploration and Development in Shaanxi Province, Xi'an 710065, China
3. School of Chemistry and Environment Science, Shaanxi University of Technology, Hanzhong 723000, China
Abstract：The quality of Salvia miltiorrhiza in different origins varies greatly, and it is urgent to establish an accurate and rapid analytical method for discrimination. Laser-induced breakdown spectroscopy (LIBS) has the advantages of fast, real-time, high efficiency, which overcomes many problems of traditional analysis methods. Artificial neural network (ANN) has strong learning and generalization abilities, a fast and accurate analysis method. Therefore, a method for discriminating Salvia miltiorrhiza from different geographical origins was developed by using LIBS combined with ANN. In the experiment, the samples of Salvia miltiorrhiza from six different origins, such as Anhui and Gansu provinces were collected, and the spectra of Salvia miltiorrhiza samples were collected by LIBS spectrometer. Then, comparing the element characteristic peaks of LIBS, it was found that there are differences in the element emission intensity of Salvia miltiorrhiza from different origins, such as Fe (238.20, 373.71 nm) and Ca (315.89, 317.93 nm). A supervised classification model was established by the ANN method combined with 5 different spectral preprocessing methods: maximum and minimum normalization (MMN), mean centralization (MC), standard normal transformation (SNV), Savitzky-Golay smooth filtering (SG) and multiple scattering correction (MSC). The RAW-ANN model has achieved a test set classification accuracy of 94.54%; SNV and MC methods did not improve the classification ability of the ANN model; And the three preprocessing methods of MMN, SG, and MSC all have improved the classification performance of the ANN model. The SG-ANN model achieved the best classification effect, with a test set classification accuracy of 98.15%. At the same time, it has higher sensitivity, precision and specificity, of which Anhui and Henan provinces have the best discrimination results, with sensitivity, precision and specificity reaching 100.00%. The other four orgins' sensitivity, precision and specificity are also above 95.00%. The results showed that selecting an appropriate spectral preprocessing method could significantly improve the classification ability of the ANN method and build a more relevant qualitative analysis model. The above results show that LIBS combined with ANN is a promising method for analysing and identifying Salvia miltiorrhiza, which provides a new idea for the quality supervision system of Chinese medicinal materials.
Key words：Laser-induced breakdown (LIBS) spectroscopy; Salvia miltiorrhiza; Artificial neural network; Identification of traditional Chinese medicine
孙成玉，焦 龙，闫娜莹，闫春华，屈 乐，张晟瑞，马 羚. 激光诱导击穿光谱结合人工神经网络鉴别不同产地的丹参药材[J]. 光谱学与光谱分析, 2023, 43(10): 3098-3104.
SUN Cheng-yu, JIAO Long, YAN Na-ying, YAN Chun-hua, QU Le, ZHANG Sheng-rui, MA Ling. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104.
