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Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology

Ting Zhu1, Xincan Wu1, Ling Ma1

  • 1School of Mathematics and Computer Science, Key Laboratory of Forest Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, China.

Journal of Medicinal Food
|June 26, 2024
PubMed

Insights

This study introduces a novel electronic nose (e-nose) method using an eccentric temporal convolutional network (ETCN) for rapid mold detection in Chinese herbal medicines (CHM). The technique achieves 94.3% accuracy, ensuring safer traditional medicine.

Area of Science:

  • Food Science
  • Analytical Chemistry
  • Traditional Chinese Medicine

Background:

  • Mold contamination is a major issue affecting Chinese herbal medicine (CHM) quality and efficacy.
  • Accurate and rapid detection of mold in CHM is crucial for quality control.

Purpose of the Study:

  • To develop a fast and precise method for detecting mold in CHM, specifically *Atractylodes macrocephala*.
  • To utilize electronic nose (e-nose) technology combined with advanced deep learning models for mold identification.

Main Methods:

  • Employing stochastic resonance (SR) to denoise e-nose data.
  • Developing an eccentric temporal convolutional network (ETCN) model to analyze sensor data.
  • Validating the SR-enhanced ETCN (SR-ETCN) model using data from eight sensors.

Main Results:

  • The SR-ETCN method achieved a high accuracy of 94.3% for mold detection in CHM.
  • The proposed model demonstrated superior performance compared to seven other models.
  • The method effectively captured temporal and spatial information from e-nose signals.

Conclusions:

  • The SR-ETCN method offers a reliable and efficient solution for detecting mold in Chinese herbal medicines.
  • This technology can significantly expedite the quality assessment of CHM.
  • Ensuring the safety and efficacy of traditional medicinal practices through improved quality control.
Keywords:
Chinese herbal medicinesConvolutional neural networksDeep learningElectronic noseMold contamination testingStochastic resonanceTemporal convolutional network

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