A predictive model for the determination of cadmium concentration in cocoa beans using laser-induced plasma spectroscopy

Abstract

This study proposes a predictive model to determine the concentration of cadmium (Cd) in cocoa beans based on laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR-1 or PLS-1). The multivariate calibration model was developed using 46 cocoa bean samples, with Cd concentrations up to 1 mg kg-1. The increase of the LIBS signal in the Cd emission lines was evident when the cocoa bean sample was subjected to a solid-liquid-solid transformation (SLST). The range error ratio (RER) was 7.92, which allowed it to be classified as a screening model. Monte Carlo cross-validation was used, with 60% of samples for calibration and the remaining for testing. The standard error of cross-validation (SECV) and standard error of calibration (SEC) were 0.12 mg kg-1 and 0.05 mg kg-1, respectively. The proposed procedure is framed within the alternatives for the chemical analysis of cocoa.

Keywords

• inorganic contaminants
• heavy metals
• partial least square regression
• atomic spectroscopy