Building a stable and accurate model for heavy metal detection in mulberry leaves based on a proposed analysis framework and laser-induced breakdown spectroscopy

Abstract

Laser-induced breakdown spectroscopy (LIBS) was used to rapidly detect heavy metals in mulberry leaves. For the purpose of increasing detection stability and accuracy, a novel analysis framework consisting of a Kohonen self-organizing map (SOM), a variable selection method using the successive projection algorithm (SPA) and uninformative variable elimination (UVE), and a consensus modeling strategy was proposed for processing LIBS data to determine copper (Cu) and chromium (Cr) content. Results showed that the best regression model for Cu and Cr content achieved the residual predictive deviation (RPD) values of 10.0494 and 8.3874, respectively, and root mean square error of prediction (RMSEP) values of 110.4550 and 41.4561, respectively. The proposed strategy provides a high-accuracy and rapid alternative to the traditional method for monitoring heavy metals in mulberry leaves, which could guarantee the quality of mulberry leaves and potentially be used in food-related industries.