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Journal of Food Measurement and Characterization

, Volume 13, Issue 4, pp 3349–3356 | Cite as

Rapid and undamaged identification of the Semen cuscutae and its adulterants based on image analysis and electronic nose analysis

  • Qiang Zhang
  • Liang-Liang Zhang
  • Jian-Guo XuEmail author
  • Guo-Ting Cui
Original Paper
  • 12 Downloads

Abstract

In this paper, image analysis and electronic nose analysis were used to develop a rapid, reliable and undamaged method of identifying the Semen cuscutae and its adulterants including radish seed and Sinapis alba seeds. The results showed that the highest identification rate was 100% for the training set and 96.5% for the test set based on image analysis and various chemometric techniques including principal component analysis, linear discriminant analysis (LDA), k-nearest neighbor, random forests, artificial neural network and support vector machine analysis. LDA analysis based on electronic nose analysis exhibited better discrimination result, ranging from 95.5–100% for the correct classification rate and 95.4–100% for the cross-validation rate, respectively. LDA model based on electronic nose data from 16 to 30 s was the best, and both the correct classification rate and cross-validation rate reached 100%. These results provided a simple, fast and non-destructive method to identify the true and false of Semen cuscutae, which can serve as a reference to identify the authenticity of the medicinal plants.

Keywords

Semen cuscutae Identification Image Electronic nose Chemometric techniques 

Notes

Acknowledgements

Authors are greatly thankful to the Natural Science Foundation of Shanxi Province, China (No. 201601D011070) for financial support and guidelines.

References

  1. 1.
    Y. Shoji, D. Haruya, N. Toshihiro, An alokaloid and two lignans from Cuscuta chinensis. Phytochemistry 37, 1755–1757 (1994)CrossRefGoogle Scholar
  2. 2.
    X.M. Du, T. Kohinata, Y.T. Guo, M. Kazumoto, Components of the ether-insoluble resin glycoside-like fraction from Cuscuta chinensis. Phytochemistry 48, 843–850 (1998)CrossRefGoogle Scholar
  3. 3.
    M. Ye, Y.N. Yan, L. Qiao, X.M. Ni, Studies on chemical constituents of Cuscuta chinensis China. J. Chin. Mater. Med. 27, 115–117 (2002)Google Scholar
  4. 4.
    S. Yang, X. Xu, H. Xu, S. Xu, Q. Lin, Z. Jia, T. Han, H. Zhang, Y. Zhang, H. Liu, Y. Gao, X. Li, Purification, characterization and biological effect of reversing the kidney-yang deficiency of polysaccharides from semen cuscutae. Carbohydr. Polym. 175, 249–256 (2017)CrossRefGoogle Scholar
  5. 5.
    F.L. Yen, T.H. Wu, L.T. Lin, C.C. Lin, Hepatoprotective and antioxidant effects of Cuscuta chinensis against acetaminophen-induced hepatotoxicity in rats. J. Ethnopharmacol. 111, 123–128 (2007)CrossRefGoogle Scholar
  6. 6.
    F.L. Yen, T.H. Wu, L.T. Lin, T.M. Cham, C.C. Lin, Concordance between antioxidant activities and flavonol contents in different extracts and fractions of Cuscuta chinensis. Food Chem. 108, 455–462 (2008)CrossRefGoogle Scholar
  7. 7.
    L.J. Yang, Q.F. Chen, F. Wang, G.L. Zhang, Antiosteoporotic compounds from seeds of Cuscuta chinensis. J. Ethnopharmacol. 135, 553–560 (2011)CrossRefGoogle Scholar
  8. 8.
    M. Ye, E.S. Chung, S.J. Lim, W.S. Kim, H. Yoon, S.K. Kim, K.S. Ahn, Y.P. Jang, H. Bae, Neuroprotective effects of Cuscutae Semen in a mouse model of Parkinson’s disease. Evid. Based Complement. Altern. Med. 2014, 1–11 (2014).  https://doi.org/10.1155/2014/150153 CrossRefGoogle Scholar
  9. 9.
    S.L. Sun, L. Guo, Y.C. Ren, B. Wang, R.H. Li, Y.S. Qi, H. Yu, N.D. Chang, M.H. Li, H.S. Peng, Anti-apoptosis effect of polysaccharide isolated from the seeds of Cuscuta chinensis Lam on cardiomyocytes in aging rats. Mol. Biol. Rep. 41, 6117–6124 (2014)CrossRefGoogle Scholar
  10. 10.
    Z. Wang, J.N. Fang, D.L. Ge, X.Y. Li, Chemical characterization and immunological activities of an acidic polysaccharide isolated from the seeds of Cuscuta chinensis Lam. Acta Pharmacol. Sin. 21(12), 1136–1140 (2000)PubMedGoogle Scholar
  11. 11.
    J.C. Liao, W.T. Chang, M.S. Lee, Y.J. Chiu, W.K. Chao, Y.C. Lin, M.K. Lin, W.H. Peng, Antinociceptive and anti-inflammatory activities of Cuscuta chinensis seeds in mice. Am. J. Chin. Med. 42, 223–242 (2014)CrossRefGoogle Scholar
  12. 12.
    J.X. Liu, L.C. Shi, J.P. Han, G. Li, H. Lu, J.Y. Hou, X.T. Zhou, F.Y. Meng, S.R. Downie, Identification of species in the angiosperm family Apiaceae using DNA barcodes. Mol. Ecol. Resour. 14, 1231–1238 (2014)CrossRefGoogle Scholar
  13. 13.
    L.N. Gu, R.X. Yu, A kind of dodder confused identification. Chin. J. Mod. Appl. Pharm. 17, 19–20 (2000)Google Scholar
  14. 14.
    H.Z. Zhang, H.Y. Li, L. Shao, P. Ma, The physical and chemical identification of dodder and adulterants. J. Chin. Med. Mat. 23, 134–135 (2000)Google Scholar
  15. 15.
    C. Jiang, Z. Cui, Y. Yuan, Y. Zhao, L. Huang, Authentication of cuscutae semen, raphani semen and their adulterants by rapid PCR. China J Chin Mater Med 41(2), 211–215 (2016)Google Scholar
  16. 16.
    Fu H (2013) The comparison of the cuscutae semen and its adulterant brassica papa L. seed. Chin. J. Tradit. Med. Sci. Technol. 20(6): 637–638.Google Scholar
  17. 17.
    L. Su, The development of the identification and quality evaluation of cuscutae semen. Low Carbon World 33, 168–169 (2015)Google Scholar
  18. 18.
    Z. Gao, L. Wang, X. Wang, Y. Liu, J. Han, Authenticity survey of cuscutae semen on markets using DNA barcoding. Chin Herb Med 9(3), 218–225 (2017)CrossRefGoogle Scholar
  19. 19.
    J. Liu, L. Lu, D. Zhang, Y. Tan, Q. Feng, Identification of cuscutae semen and its adulterants. J Anhui Agric Sci 45(3), 145–149 (2017)Google Scholar
  20. 20.
    G. Bacchetta, O. Grillo, E. Mattana, G. Venora, Morphocolorimetric characterization by image analysis to identify diaspores of wild plant species. Flora 203(8), 669–682 (2008)CrossRefGoogle Scholar
  21. 21.
    P. Zapotoczny, Discrimination of wheat grain varieties using image analysis and neural networks: part I Single kernel texture. J. Cereal Sci. 54, 60–68 (2011)CrossRefGoogle Scholar
  22. 22.
    S. Mahajana, A. Dasa, H.K. Sardanaa, Image acquisition techniques for assessment of legume quality. Trends Food Sci. Technol. 42, 116–133 (2015)CrossRefGoogle Scholar
  23. 23.
    S. Song, L. Yuan, X. Zhang, K. Hayat, H. Chen, F. Liu, Z. Xiao, Y. Niu, Rapid measuring and modelling flavour quality changes of oxidised chicken fat by electronic nose profiles through the partial least squares regression analysis. Food Chem. 141, 4278–4288 (2013)CrossRefGoogle Scholar
  24. 24.
    X. Hong, J. Wang, Z. Hai, Discrimination and prediction of multiple beef freshness indexes based on electronic nose. Sens. Actuat. B 161(1), 381–389 (2012)CrossRefGoogle Scholar
  25. 25.
    X. Hong, J. Wang, S. Qiu, Authenticating cherry tomato juices-discussion of different data standardization and fusion approaches based on electronic nose and tongue. Food Res. Int. 60, 173–179 (2014)CrossRefGoogle Scholar
  26. 26.
    A.R. Di Rosa, F. Leone, F. Cheli, V. Chiofalo, Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment—a review. J. Food Eng. 210, 62–75 (2017)CrossRefGoogle Scholar
  27. 27.
    M. Esteki, Z. Shahsavari, J. Simal-Gandara, Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products. Food Control 91, 100–112 (2018)CrossRefGoogle Scholar
  28. 28.
    M.J. Hidalgo, D.C. Fechner, E.J. Marchevsky, R.G. Pellerano, Determining the geographical origin of Sechium edule fruits by multielement analysis and advanced chemometric techniques. Food Chem. 210, 228–234 (2016)CrossRefGoogle Scholar
  29. 29.
    A.D. Girolamoa, C. Holst, M. Cortese, S. Cervellieri, M. Pascale, F. Longobardi, L. Catucci, A.R. Porricelli, V. Lippolis, Rapid screening of ochratoxin A in wheat by infrared spectroscopy. Food Chem. 282, 95–100 (2019)CrossRefGoogle Scholar
  30. 30.
    J. Xing, H. Wang, K. Luo, S. Wang, Y. Bai, J. Fan, Predictive single-step kinetic model of biomass devolatilization for CFD applications: a comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF). Renew. Energy 136, 104–114 (2019)CrossRefGoogle Scholar
  31. 31.
    A. Bablani, D.R. Edla, S. Dodia, Classification of EEG data using k-nearest neighbor approach for concealed information test. Proc. Comput. Sci. 143, 242–249 (2018)CrossRefGoogle Scholar
  32. 32.
    L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  33. 33.
    P.M. Granitto, F. Biasioli, E. Aprea, D. Mott, C. Furlanello, T.D. Märk, F. Gasperi, Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques. Sens. Actuat. B 121, 379–385 (2007)CrossRefGoogle Scholar
  34. 34.
    B. Li, Y. Wei, H. Duan, L. Xi, X. Wu, Discrimination of the geographical origin of Codonopsis pilosula using near infrared diffuse reflection spectroscopy coupled with random forests and k-nearest neighbor methods. Vib. Spectrosc. 62, 17–22 (2012)CrossRefGoogle Scholar
  35. 35.
    V.F. Rodríguez-Galiano, F. Abarca-Hernández, B. Ghimire, M. Chica-Olmo, P.M. Atkinson, C. Jeganathan, Incorporating spatial variability measures in land-cover classification using random forest. Proc. Comput. Sci. 3, 44–49 (2011)Google Scholar
  36. 36.
    L. Benali, G. Notton, A. Fouilloy, C. Voyant, R. Dizene, Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew. Energy 132, 871–884 (2019)CrossRefGoogle Scholar
  37. 37.
    J. Cai, J. Lu, Q. Li, S. Guo, Y. Dai, Analysis on volatile chemical components of semen brassicae by HS-SPME combined with GC-MS. China Pharm. 4, 26–27 (2014)Google Scholar
  38. 38.
    X. Pei, J. Lu, Q. Li, S. Guo, Analysis on volatile components in Cuscuta chinensis from different habitats by HS-SPME-GC-MS. China Pharm. 27(21), 3006–3009 (2016)Google Scholar
  39. 39.
    Q. Xia, J. Lu, Q. Li, HS-SPME-GC-MS analysis on changes in volatile components in raphani semen before and after stir-frying. Chin. J. Exp. Tradit. Med. Formul. 32(2), 57–61 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Qiang Zhang
    • 1
  • Liang-Liang Zhang
    • 2
  • Jian-Guo Xu
    • 2
    Email author
  • Guo-Ting Cui
    • 3
  1. 1.School of Life ScienceShanxi Normal UniversityLinfenChina
  2. 2.School of Food ScienceShanxi Normal UniversityLinfenChina
  3. 3.College of Food and BioengineeringHenan University of Science and TechnologyLuoyangChina

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