Direct Determination of Nutrient Elements in Plant Leaves by Laser-Induced Breakdown Spectroscopy (LIBS): Evaluation Calibration Strategies Using Direct and Inverse Models for Matrix-Matching

Abstract

This study aims to develop a single calibration model to determine nutrient elements directly (Ca, Mg, Mn, and P) in soybean and sugar cane leaves samples by laser-induced breakdown spectroscopy (LIBS). It was evaluated matrix-matching calibration (MMC) using direct and inverse models. Forty-five samples were used to build the calibration model (23 soybean leaves and 22 sugar cane leaves), and fifteen leaves were used for the prediction test (8 soybean leaves and 7 sugar cane leaves) models. In the direct model, the analyte concentration in the sample is the independent variable, and the analytical signal is the dependent variable. In the inverse model analytical signal is the independent variable, and the analyte concentration in the sample is the dependent variable. In general, both models presented satisfactory results; however, the inverse model had a better performance. Emission lines used to proposition calibration models were selected using a linear Pearson's correlation (R) strategy between each spectral point and the Ca, Mg, Mn, and P concentration measured by reference methods. The predictive capabilities, based on the root mean square error of prediction (RMSEP), for direct model was 0.60 g/kg to (Ca), 0.47 g/kg (Mg), 9.3 mg/kg to (Mn), and 0.28 g/kg to (P); for inverse model was 0.55 g/kg to (Ca), 0.39 g/kg (Mg), 10.5 mg/kg to (Mn), and 0.21 g/kg to (P). The calibration strategies proposed in this study may minimize matrix effects in direct solid analysis in soybean and sugar cane leaves samples, performing the determination of Ca, Mg, Mn, and P by LIBS from a single calibration model.