Elemental fingerprint profiling with multivariate data analysis to classify organic chocolate samples

Chocolate is an appreciated food derived from cacao fruit. The flavonoids and minerals present in the chocolate have benefits to health, and some specific minerals are known to be toxic. Because of differences in their production systems, organic chocolate has a distinguishable pattern in mineral concentrations than conventional chocolate. Aiming for authenticity and study of the toxic elements in organic chocolate, we present in this work a simple method to classify organic chocolate samples based on elemental fingerprint profiling and multivariate data analysis. Thirty-eight elements (toxic and essential) were determined in 36 chocolate samples (organic and conventional) by using inductively coupled plasma mass spectrometry to establish reference ranges and to identify differences in patterns of elements in both type of samples. Our results showed that Al, Zn, Mn, Cu, and Ba are the most present components for both types of chocolate, and higher concentrations of essential elements Fe, Zn, and Mg are found in conventional type, opposing the idea that organic food is rich in essential elements. Principal component analysis and linear discriminant analysis were used for multivariate data analysis, and sample differentiation was possible with 83% accuracy. We present a method to differentiate organic from conventional chocolate based on elemental fingerprint profiling and multivariate data analysis. Thirty-eight elements (toxic and essential) were determined in samples by inductively coupled plasmamass spectrometry. We found that Al, Zn, Mn, Cu, and Ba are the most commonly present elements for both types of chocolate, whereas higher concentrations of essential elements Fe, Zn, and Mg are found in the conventional samples.