Chemometric analysis of metal contents in different types of chocolates

Abstract

The relationships between the contents of various metals (Cu, Ni, Pb and Al) in 38 different milk chocolate samples were studied using a chemometric approach. The chemometric expressions were generated using a training set of 25 chocolate samples and the predictive ability of the resulting models was evaluated against a test set of 13 chocolate samples. The chemometric analysis was based on the application of multiple linear regression analysis (MLR). MLR was performed in order to select the significant models for predicting the metal contents. The MLR equations that represent the content of one metal as a function of the contents of other metals were established. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. It enables the researchers to establish reliable relationships between the contents of various metals which can be used for their prediction in different types of chocolate prior to their analysis. This can reduce the trial-and-error element and experimental costs in the production.