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.

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.

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, , 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.