Adulteration detection of natural samples using a class-modelling approach – Application to rooibos and honeybush herbal teas

Abstract

Class-models are constructed based on data from samples of authentic products only. They are applied for adulteration detection, and their ability to do that is assessed by testing the model against randomly selected authentic samples mixed with adulterant(s). However, due to natural within-class variance, the choice of samples used for blend preparation influences the distribution of adulterated samples in the parameters space. Therefore, it is difficult to assess the actual ability of the class-model to detect adulterated samples. The present study addressed this issue using the example of honeybush and rooibos teas adulteration detection based on their elemental profiles. An approach based on simulated adulterated samples using pure authentic samples is presented to achieve the most representative set of adulterated samples. Evaluation of the ability of the class-model to detect adulteration was more reliable when based on simulated data than on experimental data, e.g., the class-model constructed for rooibos recognised all actual rooibos samples experimentally mixed with ≥20% honeybush as adulterated but in the case of the simulated adulterated samples, more than 90% of rooibos samples adulterated with 20% honeybush were misclassified as pure rooibos samples. The detection of adulterated rooibos samples was improved by 78% using the Partial Least Squares (PLS) regression model.