Self-absorption correction in calibration-free laser-induced breakdown spectroscopy for quantitative elemental profiling and chemometric classification of Spinacia oleracea | Synapse
June 3, 2026Open Access
Self-absorption correction in calibration-free laser-induced breakdown spectroscopy for quantitative elemental profiling and chemometric classification of Spinacia oleracea
Key Points
The aim is to enhance the accuracy of elemental profiling in spinach leaves using a calibration-free approach combined with machine learning.
Utilized laser-induced breakdown spectroscopy with self-absorption correction.
Applied chemometric techniques to classify elemental profiles of spinach leaves.
Employed machine learning algorithms to analyze the data.
Successfully identified elemental changes between healthy and diseased spinach leaves.
Achieved rapid classification with high accuracy using machine learning techniques.
Demonstrated potential for real-time monitoring of plant health.
Abstract
LIBS combined with IRSAC correction and machine learning provides a rapid approach for monitoring elemental changes in healthy and diseased spinach leaves.