Discrimination of Radix Astragali according to geographical regions by data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF)

Abstract

Data fusion of Laser-induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF) was proposed to identify Radix Astragali from different geographical regions. Firstly, the LIBS and IR spectra of 19 Radix Astragali samples were collected and analyzed. Then, an unsupervised discriminant model based on principal components analysis (PCA) with single LIBS and IR spectra was applied to identify Radix Astragali. The results indicated that three types of Radix Astragali samples can not be accurately distinguished using PCA. To distinguish Radix Astragali accurately, a supervised discriminant model based on RF was applied to identify Radix Astragali. The optimal RF discriminant models based on single LIBS or IR were obtained by selecting appropriate pretreatment methods. In low-level data fusion, the optimal LIBS and IR spectra were concatenated into a matrix to construct RF discriminant model. In mid-level data fusion, feature variables were extracted by variable importance (VI) measurement on basis of low-level fused data and the optimal variables were used as input variables to construct RF discriminant model. The results indicated that the predictive performance of RF model based on data fusion was better than the single LIBS or IR method. The predictive performance of RF model based on mid-level data fusion was best and the corresponding sensitivity, specificity and accuracy for the test set were 0.9667, 0.9833 and 0.9778. Additionally, the modeling time was 6.9 s. Summarily, RF model on basis of data fusion of LIBS and IR can provide a rapid and accurate discriminant method for the Radix Astragali from different regions.