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

Zuzanna Małyjurek1, Dalene de Beer2, Hèlené van Schoor3

  • 1Institute of Chemistry, University of Silesia, Katowice, Poland.

Analytica Chimica Acta
|January 16, 2022
PubMed
Summary
This summary is machine-generated.

A new two-step authentication method accurately identifies honeybush tea species, crucial for protecting its Geographical Indication. This technique combines hyperspectral imaging with advanced modeling to distinguish closely related Cyclopia species, ensuring tea authenticity.

Keywords:
AuthenticationHoneybushLocal featuresRandom forestScatter correction

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Food Science

Background:

  • Honeybush tea, derived from endemic South African Cyclopia species, is valued for its unique flavor, aroma, and medicinal properties.
  • Geographical Indication (GI) protection necessitates the authentication of specific Cyclopia species (C. intermedia, C. subternata, C. genistoides) used in honeybush tea production.
  • Increasing demand for species-specific honeybush tea requires reliable methods to differentiate between closely related Cyclopia species.

Purpose of the Study:

  • To develop and validate a robust authentication strategy for distinguishing between three key honeybush tea Cyclopia species: C. intermedia, C. subternata, and C. genistoides.
  • To address the limitations of individual class-modeling and discriminant modeling techniques for authenticating highly similar plant species.

Main Methods:

  • Hyperspectral imaging (HSI) in the near-infrared spectral range was employed to analyze samples of C. intermedia, C. subternata, and C. genistoides.
  • Data pre-processing and class-modeling were performed, revealing limitations in distinguishing between similar HSI data of Cyclopia species.
  • A novel two-step authentication approach was developed, integrating class-modeling and discriminant methods to overcome the limitations of individual techniques.

Main Results:

  • Individual class-models yielded unsatisfactory correct classification rates (CCR) for C. genistoides (76.4%) and C. subternata (83.1%).
  • Discriminant modeling achieved a high CCR of 98.9% but is unsuitable for authentication as it misclassifies unknown samples.
  • The proposed two-step authentication method achieved a high CCR of 97.4%, demonstrating significant improvement over individual methods for closely related species.

Conclusions:

  • The developed two-step authentication approach effectively distinguishes between highly similar Cyclopia species used in honeybush tea production.
  • This method offers a reliable solution for authenticating honeybush tea, safeguarding its Geographical Indication and ensuring product integrity.
  • The proposed generalizable approach is applicable to the authentication of other plant species with similar spectral characteristics.