Leaf-Face Dendrobium Classifier Based on an Integrated Electrochemical Tongue and Machine Learning

Area of Science:

• Analytical Chemistry
• Biosensing Technology
• Traditional Chinese Medicine

Background:

• Botanical sourcing significantly affects herbal medicine safety and efficacy, hindering traditional Chinese medicine (TCM) industry growth.
• Accurate and rapid identification of plant resources is crucial for TCM development and quality control.

Purpose of the Study:

• To develop a portable, intelligent, and integrated platform for the accurate identification of Dendrobium bonsai subspecies and origins.
• To establish a versatile solution for the point-of-need classification of diverse medicinal plants.

Main Methods:

• Innovation of a Smart Electronic Tongue (SET) device featuring a hollow microneedle array for leaf extraction and a five-electrode chip for electrochemical readout.
• Utilized differential pulse voltammetry on self-assembled monolayers to generate high-dimensional electrochemical fingerprints of Dendrobium subtypes.
• Employed machine learning algorithms for automatic authentication and classification of plant varieties.

Main Results:

• The SET platform successfully achieved automatic authentication of eight Dendrobium varieties.
• Demonstrated a high discriminatory accuracy of 95% in classifying different Dendrobium subtypes.
• The device integrates miniaturization, leaf-face extraction, on-demand sample suction, and electrochemical analysis.

Conclusions:

• The developed Smart Electronic Tongue (SET) offers a rapid, convenient, and accurate method for identifying Dendrobium plant varieties.
• This bionic sensing setup provides a versatile solution for classifying medicinal plants at the point of need, supporting the TCM industry.
• The technology holds promise for ensuring the quality and safety of herbal medicines through reliable botanical authentication.