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Spectroscopic profiling-based geographic herb identification by neural network with random weights.

Yinsheng Zhang¹, Wenhao Ma¹, Ruiqi Hou¹

¹School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|May 13, 2022

Summary

The neural network with random weights (NNRW) model efficiently identifies the geographical origin of Radix Astragali using spectroscopic data. This method offers a fast and accurate solution for resource-limited applications.

Keywords:

Daodi medicinal material Neural network with random weights Raman spectroscopy Spectroscopic profiling Ultraviolet spectroscopy

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Area of Science:

Spectroscopic analysis
Cheminformatics
Traditional Chinese Medicine (TCM) authentication

Background:

Daodi medicinal materials are crucial in TCM, requiring reliable methods for geographical origin identification.
Spectroscopic profiling offers a data-driven approach to authenticate medicinal materials.
The efficacy of the neural network with random weights (NNRW) model for spectroscopic data in origin identification was previously unexplored.

Purpose of the Study:

To validate the performance of the NNRW model for geographical origin identification of Radix Astragali using spectroscopic data.
To compare NNRW with other machine learning models (MLP, SVM, DTC) in terms of accuracy and training time.
To assess the potential of NNRW for resource-limited edge computing applications.

Main Methods:

Collected Raman and UV spectroscopic profiles from 160 Radix Astragali samples across four geographic regions.
Trained and evaluated four classification models: NNRW, Multi-layer Perceptron (MLP), Support Vector Machine (SVM), and Decision Tree Classifier (DTC).
Assessed model performance based on validation accuracy and training/fitting times.

Main Results:

NNRW achieved a validation accuracy of 96.3% with an exceptionally fast training time of 0.372 milliseconds.
Other models showed high accuracies: SVM (98.4%), MLP (98.0%), and DTC (92.8%), but with significantly longer training times.
NNRW demonstrated a substantial reduction in training time while maintaining high prediction accuracy.

Conclusions:

The NNRW model is a highly efficient and accurate tool for the geographical origin identification of Radix Astragali based on spectroscopic data.
NNRW presents a promising, computationally inexpensive solution for authenticating medicinal materials in resource-limited environments and edge computing scenarios.
This study validates NNRW's applicability to spectroscopic profiling, opening avenues for its use in TCM authentication and quality control.