Research on the identification of the production origin of Angelica dahurica using LIBS technology combined with machine learning algorithms
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Abstract:
Different production origins of Angelica dahurica have varying pharmacological effects. In order to achieve fast and accurate identification of the production origin of Angelica dahurica, this study combines laser induced breakdown spectroscopy (LIBS) technology with machine learning algorithms to identify the original production origin of Angelica dahurica. Sliced samples of Angelica dahurica were taken from four regions: Hebei, Henan, Zhejiang, and Sichuan. The spectral data from the sliced samples were used as features, and different algorithms including support vector machine (SVM), backpropagation (BP) neural network, genetic algorithm-backpropagation (GA-BP) neural network, particle swarm optimization-backpropagation (PSO-BP) neural network, convolutional neural network (CNN), and CNN-SVM were employed to classify the origin of Angelica dahurica samples. The results show that the average prediction accuracy of the BP, GA-BP, and PSO-BP algorithms reached 89.64%, 89.66%, and 89.93% respectively. The average prediction accuracy of the SVM, CNN, and CNN-SVM algorithms reached 89.92%, 90.32%, and 90.53% respectively. The average prediction accuracy improved when the two algorithms were combined, and the CNN-SVM algorithm showed a 44% increase in the lowest prediction accuracy compared to the SVM algorithm. Overall, the combination of the CNN-SVM algorithm and LIBS technology demonstrated the best performance for identifying the origin of Angelica dahurica, a traditional Chinese medicinal herb, and can provide reference for the origin identification of medicinal materials.
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Project Supported:
National Natural Science Foundation of China (NO.62173122), Hebei Provincial Natural Science Key Project (NO.F2021201031) and Hebei Provincial Funding Project for Introducing Overseas Students (NO.C20210312)
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