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Application of an olfactory system to detect and distinguish bitter chocolates with different percentages of cocoa

Chocolate is one of the most consumed products in the world. Chocolate is a mixture of roasted cocoa beans and ultrafine particles of sugar (or without containing cocoa butter). The mixture is supplied with different percentages of cocoa in the market. In this research, the ability of an olfactory system is evaluated as a fast, nondestructive, and low‐cost method to detect bitter chocolates with different cocoa percentages. For this purpose, bitter chocolates with three different percentages of cocoa (78, 85, and 96%) were selected for evaluation. Separation rate of electronic nose (E‐nose) dataset obtained 99%, by using principal components analysis method. To assess the classification accuracy of the bitter chocolate samples, linear discriminant analysis, and support vector machine applied. The results showed that both methods were able to classify bitter chocolates of different percentages of cocoa with the highest accuracy of 100%. This research demonstrates the potential of using the olfactory system as a fast, nondestructive, low‐cost, and reliable method for distinguishing and categorizing different cocoa percentages in bitter chocolates with high precision. PRACTICAL APPLICATIONS: Most researches were focused on a specific percentage of cocoa, while there are few reports on the detection of different percentages of cocoa of the same type. In our study, the linear discriminant analysis and support vector machine methods were employed to the distinguishing of various percentages of cocoa of same type (bitter chocolate), and it was observed that both methods achieved the better classification performance, which indicated the feasibility of distinguishing different percentages by E‐nose. It is well‐known that the detection of different cocoa percentages from the same type would take a long time and require developed instrumental laborites with experts, thus it is of great significance to establish a rapid nondestructive detection method for the related study in the field of the cocoa industry.