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Rapid and accurate detection of Dendrobium officinale adulterated with lower-price species using NMR characteristic markers integrated with artificial neural network

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Abstract

Dendrobium officinale (D. officinale) as a well-known herbal and functional food material is often adulterated with lower-price Dendrobium species. In this study, we aimed to develop an integrated method of nuclear magnetic resonance spectroscopy and artificial neural network (NMR-ANN) to identify and quantify the adulteration of D. officinale powder with other cheaper species. Microwave-assisted water extraction was selected as a time-saving and green method for sample preparation. The results indicate that the NMR-ANN method effectively quantified the adulteration rate of D. officinale powder with the root mean squared error (RMSE) of 4.92, mean absolute error (MAE) of 3.56 and coefficient of determination (R2) of 0.98 in the model test phase and with the RMSE of 7.65, MAE of 6.30 and R2 of 0.98 in the double-blinded test phase. The whole evaluation process can be done in 6 min and 5 s. Therefore, the NMR-ANN method can be used as a rapid, green and accurate tool for evaluating the quality of D. officinale or even other food materials.

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All data are contained within the article. The data and materials used in this study are available on request from the corresponding author.

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Funding

This study was supported by the 2022 Student Partnering with Faculty/Staff Research Program of Wenzhou-Kean University, China (No.: WKUSPS202238).

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  1. Kaiyan Gong and Xiaoli Yin contributed equally to this work.

Authors and Affiliations

  1. School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China

    Kaiyan Gong, Xiaoli Yin, Na Ying, Mengjun Wu, Yuxin Lyu & Hong Zheng

  2. College of Science and Technology, Wenzhou-Kean University, Wenzhou, 325060, China

    Lingling Jiang

  3. Wenzhou Municipal Key Laboratory for Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou, 325060, China

    Lingling Jiang

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  1. Kaiyan Gong
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Contributions

Conceptualization: HZ and LLJ; Data curation: HZ; Formal analysis: HZ and KYG; Funding acquisition: LLJ; Investigation: KYG, NY, MJW and YXL; Methodology: KYG, NY, MJW and YXL; Project administration: LLJ; Resources: HZ; Supervision: HZ and LLJ; Visualization: KYG, XLY, HZ and LLJ; Writing - original draft: HZ and LLJ; Writing - review & editing: XLY, KYG, NY, MJW, YXL, HZ and LLJ.

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Correspondence to Hong Zheng or Lingling Jiang.

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Gong, K., Yin, X., Ying, N. et al. Rapid and accurate detection of Dendrobium officinale adulterated with lower-price species using NMR characteristic markers integrated with artificial neural network. Food Measure (2024). https://doi.org/10.1007/s11694-024-02538-2

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