Ultrafast laser filament-induced breakdown spectroscopy (FIBS) is a potential technique for quantitative analysis of trace elements. In this work, we investigate the effect of the distance between focusing lens and target surface on the FIBS quantitative analysis of Mn element in aluminum alloys, and several major parameters are calculated such as the linear correlation coefficient (R 2), limits of detection (LOD), relative standard deviation (RSD), and root-mean-square error of cross-validations (RMSECV). The results show that the quantitative analysis parameter values before and after filament position are different. The optimal value can be obtained at the filament region, the average values of total 23 positions of R 2, LOD, RSD, and RMAECV were 99.45%, 1.41 mg/kg, 7.12%, and 0.56%, respectively. Besides, the spatial distributions of quantitative analysis parameter values in filament region are noticeable, and this is essentially due to intensity clamping effect in a filament.

Filament-induced breakdown spectroscopy (FIBS) combined with machine learning algorithms was used to identify five aluminum alloys. To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys, principal component analysis (PCA) combined with support vector machine (SVM) and K-nearest neighbor (KNN) was used. The intensity and intensity ratio of fifteen lines of six elements (Fe, Si, Mg, Cu, Zn, and Mn) in the FIBS spectrum were selected. The distances between the focusing lens and the target surface in the pre-filament, filament, and post-filament were 958 mm, 976 mm, and 1000 mm, respectively. The source data set was fifteen spectral line intensity ratios, and the cumulative interpretation rates of PC1, PC2, and PC3 were 97.22%, 98.17%, and 95.31%, respectively. The first three PCs obtained by PCA were the input variables of SVM and KNN. The identification accuracy of the different positions of focusing lens and target surface was obtained, and the identification accuracy of SVM and KNN in the filament was 100% and 90%, respectively. The source data set of the filament was obtained by PCA for the first three PCs, which were randomly selected as the training set and test set of SVM and KNN in 3:2. The identification accuracy of SVM and KNN was 97.5% and 92.5%, respectively. The research results can provide a reference for the identification of aluminum alloys by FIBS.