Abstract
AbstractTraditional Chinese medicinal materials (TCMM) play an important role in the prevention and treatment of human diseases. Laser‐induced breakdown spectroscopy (LIBS) technology has great advantages in the detection of heavy metals in Chinese medicinal materials. In this study, the standard curve method and internal standard method were used to quantitatively analyze the heavy metal Cu in Angelica sinensis and Scutellaria baicalensis, and also partial least squares (PLS) and back‐propagation artificial neural network (BP‐ANN) was performed by LIBS combined with stoichiometry; the univariate and multivariate regression models have been established to improve the quantitative analysis performance of LIBS. It can be seen that the results calculated by the internal standard method and the standard curve method have large errors. The results calculated by BP‐ANN are closer to the real value, followed by the PLS method. Therefore, LIBS technology combined with machine learning method can accurately and rapidly analyze heavy metals in TCMM.
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