Direct determination of nutrient elements in plant leaves by double pulse laser-induced breakdown spectroscopy: evaluation of 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 leaf samples by double pulse laser-induced breakdown spectroscopy (DP LIBS). Matrix-matching calibration (MMC) was evaluated 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 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, the 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 performed better. Emission lines used to propose 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 using inductively coupled plasma optical emission spectrometry (ICP OES). The root mean square errors of prediction (RMSEP) for the direct models were 0.60 g kg⁻¹ to (Ca), 0.47 g kg⁻¹ (Mg), 9.3 mg kg⁻¹ to (Mn), and 0.28 g kg⁻¹ to (P); for inverse model was 0.55 g kg⁻¹ to (Ca), 0.39 g kg−1 (Mg), 10.5 mg kg⁻¹ to (Mn), and 0.21 g kg⁻¹ to (P). The calibration strategies proposed in this study may minimize matrix effects in direct solid analysis in soybean and sugar cane leaf samples, performing the determination of Ca, Mg, Mn, and P by DP LIBS using a single calibration model.