It is well-known that aviation alloys of different grades show large differences in mechanical properties. At present, alloys must be strictly distinguished in the manufacturing plant because their close appearance and density are easily confused In this work, the wavelet transform (WT) method combined with the least squares support vector machine (LSSVM) is applied to the classification and identification of aviation alloys by laser-induced breakdown spectroscopy (LIBS). This experiment employed six different grades of aviation alloy as the classification samples and obtained 100 sets of spectral data for each sample. This research included the steps of preprocessing the obtained spectral data, model training, and parameter optimization. Finally, the accuracy of the training set was 99.98%, and the accuracy of the test set was 99.56%. Therefore, it is concluded that the model has superior generalization capacity and portability. The result of this work illustrates that LIBS technology can be adopted for the rapid identification of aviation alloys, which is of great significance for on-site quality control and efficiency improvement of aerospace parts manufacturing.

The fast and reliable analysis of electrolytes such as K, Na, Ca in human blood serum has become an indispensable tool for diagnosing and preventing diseases. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated as a powerful analytical technique on elements. To apply LIBS to the quantitative analysis of electrolyte elements in real time, a self-developed portable laser was used to measure blood serum samples supported by glass slides and filter paper in this work. The partial least squares regression (PLSR) method was employed for predicting the concentrations of K, Na, Ca from serum LIBS spectra. Great prediction accuracies with excellent linearity were obtained for the serum samples, both on glass slides and filter paper. For blood serum on glass slides, the prediction accuracies for K, Na, Ca were 1.45%, 0.61% and 3.80%. Moreover, for blood serum on filter paper, the corresponding prediction accuracies were 7.47%, 1.56% and 0.52%. The results show that LIBS using a portable laser with the assistance of PLSR can be used for accurate quantitative analysis of elements in blood serum in real time. This work reveals that the handheld LIBS instruments will be an excellent tool for real-time clinical practice.