Class-modelling of overlapping classes. A two-step authentication approach

Honeybush is an indigenous herbal tea highly valued for its aroma, flavour and medicinal properties. It is protected as Geographical Indication (GI) since it is produced from a number of Cyclopia species that are endemic to South Africa. Most commonly used for honeybush tea production are C. intermedia, C. subternata and C. genistoides, differing slightly, but distinctly in flavour. Demand for species-specific honeybush tea instead of mixtures have increased, meriting a strategy for authentication of C. intermedia, C. subternata and C. genistoides. Samples of these three species were analysed, using hyperspectral imaging (HSI) in the near-infrared spectral range. The data were pre-processed and used for class-modelling, a general approach well suited for authentication purposes. Unfortunately, since the HSI data of Cyclopia species studied are very similar, the classification results obtained with individual class-models are unsatisfactory, e.g., class-models constructed for C. genistoides and C. subternata yielded correct classification rate (CCR) values of 76.4 and 83.1%, respectively. On the other hand, discriminant modelling, which is another type of classification technique, led to good classification outcomes (CCR 98.9%). However, the classical discriminant model cannot be applied for authentication purposes since it always assigns a new sample to one of the classes studied, even if in reality, it belongs to none of them. Counterfeits or non-representative samples would be incorrectly assigned by the discriminant model to one of the authentic classes. Therefore, in this study, a two-step authentication of overlapping classes is proposed, which combines the advantages of class-modelling and discriminant methods. When applied to the authentication of Cyclopia species studied, the two-step approach yielded a CCR of 97.4%, which is a significant improvement compared to results obtained with the individual class-models. The proposed approach is general and can be applied when classes studied are very similar, and individual class-models lead to unsatisfactory results.