A review of calibration-free laser-induced breakdown spectroscopy

Improving laser-induced breakdown spectroscopy regression modelsviatransfer learning

“…The inclusion of LIBS into these instruments is mainly motivated by LIBS's ability of rapid and real-time sensing, [15][16][17] low demands on sample preparation, 18,19 and stand-off sensing capabilities. [20][21][22] In contrast, despite the emergence of various calibration-free approaches, 23,24 quantitative LIBS analysis in most cases remains reliant on the construction of calibration models (univariate or multivariate calibration curves or machine learning models). [25][26][27] In addition, LIBS is generally characterized by low repeatability 28,29 and a considerable susceptibility to various matrix effects.…”

Quantitative accuracy improvement of laser-induced breakdown spectroscopy by a modified corner-cutting method for continuum background removal

“…4 The continuum BG leads to unsatisfactory quantitative analysis results, especially for calibration-free LIBS (CF-LIBS). 5,6…”

Intrinsic Performance of Monte Carlo Calibration-Free Algorithm for Laser-Induced Breakdown Spectroscopy

“…However, their typical accuracy and precision are modest and degrade toward semi-quantitative analysis for concentrations below 1%. The main applications of CF LIBS can be found in industry, geology, biology, archeology, or even space exploration [ 1 , 2 ].…”

“…They can be divided into two categories: those that use the experimental spectrum to determine the plasma parameters (the inverse problem) and those that specify the plasma parameters to reproduce the experimental spectrum (the direct problem). The most frequently used version of the first category is the Boltzmann plot (BP) or Saha–Boltzmann plot (SBP) method [ 2 , 3 ]. It relies on the assumptions of stoichiometric ablation, plasma uniformity, and optical thinness.…”