Journal List > Nat Prod Sci > v.24(3) > 1102536

Lee, Kang, Kim, Kim, and Sung: Classficiation of Bupleuri Radix according to Geographical Origins using Near Infrared Spectroscopy (NIRS) Combined with Supervised Pattern Recognition

Abstract

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

REFERENCES

(1). Ashour M. L., Wink M. J.Pharm. Pharmacol. 2011; 63:305–321.
(2). Bermejo Benito P., Abad Martínez M. J., Silván Sen A. M., Sanz Gómez A., Fernández Matellano L., Sánchez Contreras S., Díaz Lanza A. M.Life Sci. 1998; 63:1147–1156.
(3). Cheng X. Q., Li H., Yue X. L., Xie J. Y., Zhang Y. Y., Di H. Y., Chen D. F. J.Ethnopharmacol. 2010; 130:363–368.
(4). Sun X. B., Matsumoto T., Yamada H. J.Pharm. Pharmacol. 1991; 43:699–704.
(5). Wen S., Huifu X., Hao H.Immunopharmacol. Immunotoxicol. 2011; 33:433–437.
(6). Zhu L., Liang Z. T., Yi T., Ma Y., Zhao Z. Z., Guo B. L., Zhang J. Y., Chen H. B.BMC Complement. Altern. Med. 2017; 17:305–316.
(7). Li X., Jia Y., Song A., Chen X., Bi K.Yakugaku Zasshi. 2005; 125:815–819.
(8). Bao Y., Li C., Shen H., Nan F.Anal. Chem. 2004; 76:4208–4216.
(9). Liau B. C., Hsiao S. S., Lee M. R., Jong T. T., Chiang S. T. J.Pharm. Biomed. Anal. 2007; 43:1174–1178.
(10). Lee J., Yang D. H., Suh J. H., Kim U., Eom H. Y., Kim J., Lee M. Y., Kim J., Han S. B. J. Chromatogr. B.Analyt. Technol. Biomed. Life Sci. 2011; 879:3887–3895.
(11). Huang H. Q., Su J., Zhang X., Shan L., Zhang W. D. J.Chromatogr. A. 2011; 1218:1131–1138.
(12). Tian R. T., Xie P. S., Liu H. P. J.Chromatogr. A. 2009; 1216:2150–2155.
(13). Qin X., Dai Y., Liu N. Q., Li Z., Liu X., Hu J., Choi Y. H., Verpoorte R.Planta Med. 2012; 78:926–933.
(14). Lin X., Xue L., Zhang H., Zhu C.Anal. Bioanal. Chem. 2005; 382:1610–1615.
(15). Gong F., Wang B. T., Chau F. T., Liang Y. Z.Anal. Lett. 2005; 38:2475–2492.
(16). McGoverin C. M., Weeranantanaphan J., Downey G., Manley M. J.Near Infrared Spec. 2010; 18:87–111.
(17). Chen Y., Xie M. Y., Yan Y., Zhu S. B., Nie S. P., Li C., Wang Y. X., Gong X. F.Anal. Chim. Acta. 2008; 618:121–130.
(18). Luo X. F., Yu, X.;Wu, X. M.;Cheng, H. B.;Qu H. B.Microchem. J. 2008; 90:8–12.
(19). Wang L., Lee F. S. C., Wang X.LWT-Food Sci. Technol. 2007; 40:83–88.
(20). Chen Q., Zhao J., Lin H. Spectrochim.Acta A. Mol. Biomol. Spectrosc. 2009; 72:845–850.
(21). Lin H., Zhao J., Chen Q., Zhou F., Sun L. Spectrochim Acta. A.Mol. Biomol. Spectrosc. 2011; 79:1381–1385.
(22). Lee D. Y., Kim S. H., Kim Y. C., Kim H. J., Sung S. H.Microchem. J. 2011; 99:213–217.
(23). Berrueta L. A., Alonso-Salces R. M., Héberger K.J. Chromatogr. A. 2007; 1158:196–214.
(24). Li B., Wei Y., Duan H., Xi L., Wu X.Vib. Spectrosc. 2012; 62:17–22.
(25). Chiang L. H., Russell E. L., Braatz R. D.Chemometrics Intell. Lab. Syst. 2000; 50:243–252.
(26). Jiang H., Liu G. H., Xiao X., Yu S., Mei C., Ding Y.Food Anal. Methods. 2012; 5:928–934.
(27). Luts J., Ojeda F., Van de Plas R., De Moor B., Van Huffel S., Suykens J. A.Anal. Chim. Acta. 2010; 665:129–145.
(28). Ballabio D., Consonni V.Anal. Methods. 2013; 5:3790–3798.
(29). Rubin T. N., Chambers A., Smyth P., Steyvers M.Mach. Learn. 2012; 88:157–208.

Fig. 1.
Average NIR reflectance specrtra of Bupleuri Radix obtained from raw data.
nps-24-164f1.tif
Fig. 2.
Average NIR reflectance specrtra of Bupleuri Radix with 2nd derivative preprocessing.
nps-24-164f2.tif
Fig. 3.
Two dimensional score plot of the top two principal components (PCs) for all samples.
nps-24-164f3.tif
Fig. 4.
Cross validation classification rates of QDA (a) and RBF-SVM (b) models at different PCs.
nps-24-164f4.tif
Table 1.
Summary of testing Bupleuri Radix samples
Geographical origins Number of samples
Training set Test set
South Korea
  Goheung 35 17
  Whasun 16 7
China
  Shanxi 10 5
  Gansu 17 8
  Shaanxi 11 5
Table 2.
Classification results by PLS-DA model
PLS-DA Goheung Whasun Shanxi Gansu Shaanxi Classification rate (%)
per group all groups
Training set (n=89) Goheung 35 0 0 0 0 100 83.3
Whasun 0 16 0 0 0 100
Shanxi 0 0 8 0 2 80
Gansu 0 0 0 17 0 100
Shaanxi 1 0 0 7 3 27.3
Test set (n=42) Goheung 17 0 0 0 0 100 88.8
Whasun 0 7 0 0 0 100
Shanxi 0 0 2 0 3 40
Gansu 0 0 0 8 0 100
Shaanxi 0 0 0 4 1 20

∗The rows indicate the true sample class and that the columns refer to the observed class.

Table 3.
Classification results by QDA model
QDA Goheung Whasun Shanxi Gansu Shaanxi Classification rate (%)
per group all groups
Training set (n=89) Goheung 35 0 0 0 0 100 97.8
Whasun 0 16 0 0 0 100
Shanxi 0 0 10 0 0 100
Gansu 0 0 0 17 0 100
Shaanxi 0 0 0 2 9 81.8
Test set (n=42) Goheung 17 0 0 0 0 100 95.2
Whasun 0 7 0 0 0 100
Shanxi 0 0 5 0 0 100
Gansu 0 0 0 8 0 100
Shaanxi 0 0 0 2 3 60

∗ The rows indicate the true sample class and that the columns refer to the observed class.

Table 4.
Classification results by RBF-SVM model
RBF-SVM Goheung Whasun Shanxi Gansu Shaanxi Classification rate (%)
per group all groups
Training set (n=89) Goheung 35 0 0 0 0 100 96.6
Whasun 0 16 0 0 0 100
Shanxi 0 0 10 0 0 100
Gansu 0 0 0 17 0 100
Shaanxi 0 0 0 3 8 72.7
Test set (n=42) Goheung 17 0 0 0 0 100 85.7
Whasun 0 7 0 0 0 100
Shanxi 0 0 3 0 2 60
Gansu 0 0 0 7 1 87.5
Shaanxi 0 0 1 2 2 40

∗ The rows indicate the true sample class and that the columns refer to the observed class

TOOLS
Similar articles