Determination of the nutrient profile in plant materials using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid models.

Abstract

Nutrient profile determination for plant materials is an important task to determine the quality and safety of the human diet. Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectrometry of the material component analytical technique. However, quantitative analysis of plant materials using LIBS usually suffers from matrix effects and nonlinear self-absorption. To overcome this problem, a hybrid quantitative analysis model of the partial least squares-artificial neural network (PLS-ANN) was used to detect the compositions of plant materials in the air. Specifically, fifty-eight plant materials were prepared to split into calibration, validation and prediction sets. Nine nutrient composition profiles of Mg, Fe, N, Al, B, Ca, K, Mn, and P were employed as the target elements for quantitative analysis. It demonstrated that the prediction ability can be significantly improved by the use of the PLS-ANN hybrid model compared to the method of standard calibration. Take Mg and K as examples, the root-mean-square errors of calibration (RMSEC) of Mg and K were decreased from 0.0295 to 0.0028 wt.% and 0.2884 to 0.0539 wt.%, and the mean percent prediction errors (MPE) were decreased from 5.82 to 4.22% and 8.82 to 4.12%, respectively. This research provides a new way to improve the accuracy of LIBS for quantitative analysis of plant materials.