Simple defocus laser irradiation to suppress self-absorption in laser-induced breakdown spectroscopy (LIBS).

This study introduces a novel and simple way to suppress the self-absorption effect in laser-induced breakdown spectroscopy (LIBS) by utilizing a defocusing laser irradiation technique. For this purpose, a Nd:YAG laser with a wavelength of 1,064 nm and repetition rate of 10 Hz with energy in the range of 10 mJ-50 mJ was used. The laser irradiation was focused by using a 150-mm-focal-length plano-convex lens onto the sample surface under defocusing of approximately -6 mm. Potassium chloride (KCl) and sodium chloride (NaCl) pellet samples were used to demonstrate this achievement. When the defocus position is adjusted to -6 mm for KCl and NaCl samples, the self-reversal in the emission lines of K I 766.4 nm, K I 769.9 nm, Na I 588.9 nm, and Na I 589.5 nm vanish. Meanwhile, the FWHM values of K I 766.4 and K I 769.9 nm are 0.29 nm and 0.23 nm, respectively, during -6 mm defocus laser irradiation, as opposed to 1.24 nm and 0.86 nm under tight focus laser irradiation. Additionally, this work demonstrates that, when the laser energy is changed between 10 and 50 mJ, no self-reversal occurs in the emission lines when -6 mm defocus laser irradiation is applied. Finally, a linear calibration curve was generated using KCl at a high concentration ranging between K concentrations from 16.6% to 29%. It should be noted that, even at such high K concentrations, the calibration curve is still linear. This means that self-absorption is almost negligible. This simple change in defocus laser irradiation will undoubtedly contribute to the suppression of the self-absorption phenomenon, which disrupts LIBS analytical results.

Laser-Induced Breakdown Spectroscopy Combined with Nonlinear Manifold Learning for Improvement Aluminum Alloy Classification Accuracy.

Laser-induced breakdown spectroscopy (LIBS) spectra often include many intensity lines, and obtaining meaningful information from the input dataset and condensing the dimensions of the original data has become a significant challenge in LIBS applications. This study was conducted to classify five different types of aluminum alloys rapidly and noninvasively, utilizing the manifold dimensionality reduction technique and a support vector machine (SVM) classifier model integrated with LIBS technology. The augmented partial residual plot was used to determine the nonlinearity of the LIBS spectra dataset. To circumvent the curse of dimensionality, nonlinear manifold learning techniques, such as local tangent space alignment (LTSA), local linear embedding (LLE), isometric mapping (Isomap), and Laplacian eigenmaps (LE) were used. The performance of linear techniques, such as principal component analysis (PCA) and multidimensional scaling (MDS), was also investigated compared to nonlinear techniques. The reduced dimensions of the dataset were assigned as input datasets in the SVM classifier. The prediction labels indicated that the Isomap-SVM model had the best classification performance with the classification accuracy, the number of dimensions and the number of nearest neighbors being 96.67%, 11, and 18, respectively. These findings demonstrate that the combination of nonlinear manifold learning and multivariate analysis has the potential to classify the samples based on LIBS with reasonable accuracy.

Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy.

The feasibility and accuracy of several combination classification models, i.e., quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naïve Bayes (BNB), and support vector machine (SVM) classification models combined with either sequential feature selection (SFS) or dimensionality reduction methods, for classifying soil with laser-induced breakdown spectroscopy (LIBS) had been explored in this study. Each algorithm combination was compared to assess their classification performance. After eliminating the irrelevant features of the data using sequential feature selection (SFS), the performances were all improved for the studied four classification models, and the best accuracy reached 97.88% by SFS-SVM. The dimensions of the data were then reduced using variational autoencoder (VAE), truncated singular value decomposition (TSVD), and isometric mapping (Isomap), respectively. The classification accuracy improved for all combination models with dimensionality reduction, and impressive accuracies of 98.12% from TSVD-SVM and 98.24% from VAE-SVM were obtained. These results demonstrate an effective way to reduce uncorrelated features, high dimensionality, and redundant information in the LIBS dataset. In addition, coupling classification models with feature selection and dimensionality reduction techniques could significantly optimize the classification performance of LIBS.