Multivariate statistical analysis of gas chromatograms to differentiate cocoa masses by geographical origin and roasting conditions

Abstract

Multivariate statistical methods were applied to the differentiation of cocoa from 13 geographical origins, taken at four roasting steps and supplied by five different manufacturers. An analysis of variance applied to 37 peak areas showed that in only nine instances was the variability of the response influenced by the origin, the degree of roasting, and the supplier. These peaks, identified by gas chromatography–mass spectrometry, were used as variables to perform principal components analysis, hierarchical clustering, and discriminant analysis. It was established that unroasted masses fall into five groups, which essentially differ in the amounts of two components, hexane and 2-methoxy-4-methylphenol. These two chemical components may therefore be good origin markers. On the other hand, the analysis of cocoa at the end of the roasting process demonstrates the importance of the thermal treatment conditions. Indeed, the use of hot-air roasters favours the synthesis of aldehydes. Discriminant analysis also shows the influence of time and temperature on the production of 2,3-diethyl-4-methylpyrazine, which has a typical odour of roasted peanut. This study shows that gas chromatography is an ideal technique for the objective discrimination of cocoa origin and roasting conditions.