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Rapid identification and quantification of adulteration in Dendrobium officinale using nuclear magnetic resonance spectroscopy combined with least-squares support vector machine


Dendrobium officinale (D. officinale) is commonly used as a functional food or herbal medicine worldwide, but often adulterated with low-priced materials. In this study, we attempted to develop an integrated method of least-squares support vector machine and nuclear magnetic resonance spectroscopy (LS-SVM-NMR) to identify and quantify the adulteration of D. officinale powder. We found that LS-SVM-NMR can identify the adulterated D. officinale powder with an overall accuracy of 100% and quantify its purity with an R2 of 0.999 and RMSE of 1.410 at model validation phase. In addition, our results from the double-blinded test revealed that LS-SVM-NMR can yield a classification accuracy of 98% for distinguishing the adulterated D. officinale powder, and predict its purity with an R2 of 0.998 and RMSE of 2.660. Moreover, adulteration evaluation reports can be obtained in 10 min. Therefore, LS-SVM-NMR proposed herein could be used as a promising and time-saving tool for the quality evaluation of D. officinale or even other food products.

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This study was supported by the Qianjiang Talent Project of Zhejiang Province (No. QJD1802023) and National Natural Science Foundation of China (Nos.: 21575105, 81503335).

Author information

HZ and HCG contributed to experimental design. LLP and LLJ contributed to sample collection. LLP and LLJ contributed to sample preparation and metabolomics data acquisition. LLJ and HZ contributed to data analysis, model development and writing. All authors have read, revised and approved the final manuscript.

Correspondence to Hong Zheng.

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Jiang, L., Pan, L., Gao, H. et al. Rapid identification and quantification of adulteration in Dendrobium officinale using nuclear magnetic resonance spectroscopy combined with least-squares support vector machine. Food Measure (2020).

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  • Adulteration
  • Food product
  • NMR
  • Quality evaluation
  • LS-SVM