Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra.

Determination of trace elements in soils with laser-induced breakdown spectroscopy is significantly affected by the matrix effect, due to large variations in chemical composition and physical property of different soils. Spectroscopic data treatment with univariate models often leads to poor analytical performances. We have developed in this work a multivariate model using machine learning algorithms based on a back-propagation neural network (BPNN).

Determination of Metal Elements in Wine Using Laser-Induced Breakdown Spectroscopy (LIBS).

We developed a method for sensitive elemental analysis of wines using laser-induced breakdown spectroscopy (LIBS). In order to overcome the inefficiency of direct ablation of bulk wine (an organic liquid), a thin layer of wine residue was prepared on a metallic target according to an appropriated heating procedure applied to an amount of liquid wine dropped on the target surface. The obtained ensemble was thus ablated.