Authentication of honeybush and rooibos herbal teas based on their elemental composition. (May 2021) - Małyjurek, Zuzanna; Zawisza, Beata; de Beer, Dalene; Joubert, Elizabeth; Walczak, Beata
Abstract: Rooibos ( Aspalathus linearis ) and honeybush ( Cyclopia species) are popular indigenous herbal teas originating from South Africa. Both are enjoyed for their taste and aroma and more importantly, valued for their medicinal properties such as antioxidant, anti-diabetic, anti-inflammatory or immunomodulatory activity. In the European Union, rooibos and honeybush are protected as Geographical Indications. The Geographical Indication refers to products with unique characteristics that are related to their geographical origin. The authentication of products labelled as GIs is regarded as an issue of food quality and safety. Routine quality control procedures of GIs products prevent their fraud and counterfeiting on the market. However, techniques to determine adulteration or mislabelling of rooibos and honeybush do not exist yet. Therefore, in this study, the authentication of rooibos and honeybush based on their elemental composition was investigated. The methodology presented in this study combines energy-dispersive X-ray fluorescence spectrometry (EDXRF) for elemental analysis and a one-class classification approach. Elemental composition of plant material highly depends on soil on which the plant has been grown, but also on some other factors such as, e.g., atmospheric pollution, or plant metabolism. Determination of the elemental composition of the samples by EDXRF is non-destructive and does not require any complex sample preparation. One-class classification methods are well suited for authentication and origin verification problems. Based on the EDXRF data of samples from studied teas, individual class models were constructed for rooibos and honeybush. Several linear and nonlinear classification techniques were tested in order to find the model that handles the authentication task the best. For honeybush tea, the best classification results were obtained with the use of a nonlinear method based on Potential Functions. For rooibos tea, the highest classification outcomes were obtained by nonlinear One Class Partial Least Squares (OC-PLS) and the machine learning technique, Support Vector Domain Description (SVDD). The methodology implemented in the present study has the potential to be successfully applied for routine authentication of honeybush and rooibos teas. Highlights: Honeybush and rooibos are authenticated based on their elemental composition. Potential function-based model yields the best classification results for honeybush. For classification of rooibos, nonlinear OC-PLS and SVDD are recommended.