Abstract: Abstract: Laser-induced breakdown spectroscopy (LIBS), as a kind of atomic emission spectroscopy (AES), has been considered to be a future "Superstar" in the field of chemical analysis and green analytical techniques due to its unique features, like little or no sample preparation, stand-off or remote analysis, fast and multi-element analysis, wide application in various aspects. To establish the soil type system in China and more comprehensively understand the type of elements in the soil, soil types were studied to improve the utilization of land resources and offer a theoretical guide for agricultural scientific production. This research focused on investigating the soil types using LIBS coupled with chemometrics methods. A laboratorial LIBS device working in air was employed to obtain the 300 (every 50 LIBS spectra acquired from one type of soil) LIBS spectra of 6 soil samples. Based on the contents of main materials (SiO2, Al2O3, Fe2O3, FeO, MgO, CaO, Na2O, K2O) of 6 kinds of standard soil samples, their corresponding LIBS curve characteristics were analyzed. Then 7 characteristic spectral lines at Si I 390.55 nm, Al I 394.40 nm, Fe I 422.74 nm, Mg I 518.36 nm, Na I 588.96 nm, Ca II 393.36 nm and K I 766.49 nm (I represented atomic spectral line, II meant ionic spectral lines) were identified. Based on 300 spectra at 7 characteristic spectral lines from 6 standard reference soils, of which 200 were in training set and 100 in prediction set divided by sample set partitioning based on joint x-y distances (SPXY) method, the principal component analysis (PCA) was carried out on the training set and an obvious cluster was observed from the score plot of the first 2 principal components (PCs). Meanwhile, partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) models were introduced to establish the discriminant models and the correct rates of discrimination were 98% and 100%, respectively. Then, the performances of PLS-DA and LS-SVM models were evaluated using receiver operating characteristic (ROC) curve. The results demonstrated that the LS-SVM discriminant model with the parameter area of 1 was superior to the model of PLS-DA with the area of 0.99569, which illustrated that the LS-SVM discriminant model was robust. Based on this, 7 types of soils from different places were used to conduct the same experiments to acquire 385 (every 55 LIBS spectra acquired from one type of soil) and then to verify the selected seven characteristic spectral lines and discriminant model. PCA on the training set of 255 LIBS spectra from 7 types of soil samples also displayed apparent cluster. Then, the LS-SVM model based on the training set from 7 types of soils was built to predict the prediction set of 130 LIBS spectra and the prediction accuracy was 100%. The performance of the model was also evaluated using ROC and it exhibited an excellent result. The research reveals that LIBS technology coupled with chemometrics methods can achieve the discriminant analysis of different types of soils, which provides a theoretical guidance for soil quality assessment, management, planning and reasonable use.