Abstract
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.
Similar content being viewed by others
References
L. Xia, X. Liu, H. Guo, H. Zhang, J. Zhu, F. Ren, J. Func, Foods 4, 294 (2012)
X. Xing, S.W. Cui, S. Nie, G.O. Phillips, H.D. Goff, Q. Wang, Bioact. Carbohydr. Diet. Fibre 1, 131 (2013)
L.H. Pan, X.F. Li, M.N. Wang, X.Q. Zha, X.F. Yang, Z.J. Liu et al., Int. J. Biol. Macromol. 64, 420 (2014)
T.B. He, Y.P. Huang, L. Yang, T.T. Liu, W.Y. Gong, X.J. Wang et al., Int. J. Biol. Macromol. 83, 34 (2016)
G.Y. Zhang, S.P. Nie, X.J. Huang, J.L. Hu, S.W. Cui, M.Y. Xie, G.O. Phillips, J. Agric. Food Chem. 64, 2485 (2015)
S. Bansal, A. Singh, M. Mangal, A.K. Mangal, S. Kumar, Crit. Rev. Food Sci. Nutr. 57, 1174 (2017)
T.G. Kang, D.Q. Dou, J.K. Zhang, Planta Med. 74, 10 (2008)
X. Zhao, F. Ma, P. Li, G. Li, L. Zhang, Q. Zhang, W. Zhang, X. Wang, Food Chem. 176, 465 (2015)
M. Alizadeh, S. Pirsa, N. Faraji, Food Anal. Methods 10, 2092 (2017)
F. Ghasemi, S. Pirsa, M. Alizadeh, F. Mohtarami, Sep. Sci. Technol. 53, 117 (2018)
L. Zeng, X. Wu, Y. Li, D. Lu, C. Sun, Anal. Methods 7, 543 (2015)
P. Mishra, C.B. Cordella, D.N. Rutledge, P. Barreiro, J.M. Roger, B. Diezma, J. Food Eng. 168, 7 (2016)
S. Lohumi, S. Lee, H. Lee, B.K. Cho, Trends Food Sci. Technol. 46, 85 (2015)
R. Hachem, G. Assemat, N. Martins, S. Balayssac, V. Gilard, R. Martino, M. Malet-Martino, J. Pharmaceut. Biomed. Anal. 124, 34 (2016)
B. Druml, W. Mayer, M. Cichna-Markl, R. Hochegger, Food Chem. 178, 319 (2015)
G. Ding, G. Xu, W. Zhang, S. Lu, X. Li, S. Gu, X.Y. Ding, Eur. Food Res. Tech. 227, 1283 (2008)
H. Xu, B. Hou, J. Zhang, M. Tian, Y. Yuan, Z. Niu, X. Ding, Int. J. Food Sci. Tech. 47, 1695 (2012)
X. Dong, C. Jiang, Y. Yuan, D. Peng, Y. Luo, Y. Zhao, L. Huang, J. Sci. Food Agric. 98, 549 (2018)
L. Yang, W.R. Wu, H. Zhou, H.L. Lai, F. Fu, Chin. J. Nat. Med. 17, 337 (2019)
C. Chu, H. Yin, L. Xia, D. Cheng, J. Yan, L. Zhu, Molecules 19, 3718 (2014)
K.Z. Yu, H. Yan, H.C. Tai, N.P. Zhang, X.L. Cheng, Z.X. Guo et al., Microsc. Res. Tech. 80, 745 (2017)
Y. Yuan, X. Liu, J. Wang, J. Zhang, Microsc. Res. Tech. 82, 483 (2019)
Y. Wei, W. Fan, X. Zhao, W. Wu, H. Lu, Anal. Lett. 48, 817 (2015)
Y. Wang, Z.T. Zuo, T. Shen, H.Y. Huang, Y.Z. Wang, Anal. Lett. 51, 2792 (2018)
Z. Ye, J.R. Dai, C.G. Zhang, Y. Lu, L.L. Wu, A.G. Gong et al., Evid. Based Complement. Alternat. Med. 2017, 8647212 (2017)
U. Holzgrabe, NMR Spectroscopy in Pharmaceutical Analysis (Elsevier, Amsterdam, 2017)
E. Destandau, T. Michel, C. Elfakir, R. Soc. Chem. 21, 113 (2013)
F. Savorani, G. Tomasi, S.B. Engelsen, J. Magn. Reson. 202, 190 (2009)
J. Xia, I.V. Sinelnikov, B. Han, D.S. Wishart, Nucl. Acids Res. 43, W251 (2015)
H. Zheng, H.F. Lu, Comput. Electron. Agric. 83, 47 (2012)
X. Ding, L. Xu, Z. Wang, K. Zhou, H. Xu, Y. Wang, Planta Med 68, 191 (2002)
X. Ding, Z. Wang, K. Zhou, L. Xu, H. Xu, Y. Wang, Planta Med 69, 587 (2003)
T. Li, J. Wang, Z. Lu, J. Biochem. Biophys. Meth. 62, 111 (2005)
S.C.W. Sze, K.Y.B. Zhang, P.C. Shaw, P.P.H. But, T.B. Ng, Y. Tong, Biotechnol. Appl. Biochem. 49, 149 (2008)
S. Zhu, Z. Niu, Q. Xue, H. Wang, X. Xie, X. Ding, Acta Pharm. Sin. B 8, 969 (2018)
Y. Zhao, L. Zha, B. Han, H. Peng, Microsci. Res. Technol. 81, 1191 (2018)
J.A.K. Suykens, J. Vandewalle, Neural Proc. Lett. 9, 293 (1999)
J.A.K. Suykens, J. Vandewalle, Int. J. Circ. Theor. Appl. 27, 605 (2015)
G. Zyskind, J. Am. Stat. Assoc. 58, 1125 (1963)
Acknowledgements
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
Authors and Affiliations
Contributions
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.
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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 14, 1427–1432 (2020). https://doi.org/10.1007/s11694-020-00392-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11694-020-00392-6