Determination and Correction of Systematic Errors in Surface-Enhanced LIBS

Abstract

Surface-enhanced laser-induced breakdown spectroscopy (SENLIBS) enhances the detection sensitivity for liquid analysis by transforming the sample into a solid-phase analyte layer on a substrate surface. However, this process introduces solid substrate-induced matrix effects, which manifest as proportional and constant systematic errors and ultimately compromise quantitative accuracy. This study employs ANCOVA to identify systematic errors by comparing the slopes of Basic Calibration (BC), Youden Calibration (YC), and Standard Addition Calibration (SAC). To address these errors, a novel calibration strategy that combines matrix dilution with Youden calibration is then proposed. The method was validated with Cr and Pb in water; it improved quantitative accuracy by up to 97.5% and 97.0%, respectively, across different standard addition systems. Recovery tests with real wastewater samples further confirmed its practicality, achieving a Pb recovery rate of 95.0%. The proposed hybrid correction framework provides a robust, model-based solution for enhancing the accuracy of SENLIBS in trace element analysis.