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Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM)
تبعیض سریع miltiorrhiza مریم گلی با توجه به مناطق جغرافیایی خود را با طیف سنجی شکست ناشی از لیزر (LIBS) و یادگیری ماشین افراطی بهینه سازی ازدحام ذرات (PSO-KELM)-2020 Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning
machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza
samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer.
An unsupervised classification model based on principal components analysis (PCA) was employed first
for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza
samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a
bigger challenge in classification based on PCA. A supervised classification model based on KELM was then
developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used
as the variable selection method to eliminate useless information and improve classification ability of the KELM
model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of
94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector
machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The
overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method
for classification and identification of Salvia miltiorrhiza samples in different regions. Keywords: Laser-induced breakdown spectroscopy | Particle swarm optimization | Kernel extreme learning machine | Salvia miltiorrhiza | Classification |
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