Discrimination of Samar and Talh honey produced in the Gulf Cooperation Council (GCC) region using multivariate data analysis

Abstract

Lately, ensuring clear discrimination of the authenticity of natural honey is a concern not only to consumers but also to producers, traders and industries. The intrinsically simple nature of the honey composition, the distinguished health benefits as well as the high price make adulteration and falsification of the honey very common and the detection of authenticity very difficult. Samar honey (n = 59) and Talh honey (n = 64) produced from two species of Acacia trees (Acacia tortilis and Acacia gerrardii Benth, respectively) in different countries, including the United Arab Emirates, Saudi Arabia, Oman, and Yemen, has been studied by applying Multivariate Data Analysis. The discrimination is based on the official chemical quality parameters that inform about the nectary sources, including glucose, fructose, sucrose content, total reducing sugar, moisture content, acidity and diastase activity. Results show that the total reducing sugar, glucose and fructose were the most important positive loading descriptors that influence the quality of Samar and Talh honey. In addition, most of the Talh honey samples clustered at the top of the hierarchy, while Samar honey samples clustered at the bottom. The Multivariate Data Analysis indicates that the acidity and diastase activity are the most effective characteristics influencing the floral and geographical discrimination of both types of honey. This is the first study in the GCC area to discriminate between Samar and Talh honey using Multivariate Data Analysis by applying the principal component analysis and hierarchical cluster analysis. The Multivariate Data Analysis can be a helpful method to differentiate between Talh and Samar honey.