Self-absorption correction in calibration-free laser-induced breakdown spectroscopy for quantitative elemental profiling and chemometric classification of Spinacia oleracea

Abstract

Although Laser-Induced Breakdown Spectroscopy (LIBS) is a fast multi-elemental technique, with increasing applications in agricultural diagnostics, self-absorption can affect its quantitative sensitivity. In the present work, Calibration Free LIBS (CF-LIBS) of healthy and diseased spinach leaves was combined with Internal Reference Self Absorption Correction (IRSAC) to enhance the consistency of the spectral results to profile the elements. Leaf spot infection is known to interfere with ionic transport and chlorophyll metabolism, leading to variations in macronutrients (Ca, Mg, and K) and micronutrients (Fe, Mn). Accordingly, corrected LIBS emissions were used to evaluate physiologically relevant trends. Plasma temperature and electron density were determined using Boltzmann plots and Stark broadening to verify local thermodynamic equilibrium (LTE) conditions. To establish quantitative reliability, CF-LIBS concentrations were cross-validated against inductively coupled plasma optical emission spectroscopy (ICP-OES), yielding deviations within 7–10%, consistent with values reported in established LIBS validation studies. Multivariate analysis using principal component analysis (PCA) demonstrated clear clustering between healthy and diseased samples, while supervised machine learning (ML) classifiers achieved >90% accuracy. The integrated IRSAC corrected CF-LIBS and ML framework demonstrates the potential of spectroscopically validated elemental profiling and may support further development of portable systems for precision agriculture and food-quality monitoring.