Abstract

An optical system based on visible∕near-infrared spectroscopy (Vis∕NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis∕NIR region between 325 and 1075nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.

© 2007 Optical Society of America

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References

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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [PubMed]

2007

Y. He, X. L. Li, and X. F. Deng, "Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model,"J. Food Eng. 79, 1238-1242 (2007).
[CrossRef]

D. Gray, K. LeVanseler, M. Pan, and E. H. Waysek, "Evaluation of a method to determine flavonol aglycones in Ginkgo biloba dietary supplement crude materials and finished products by high-performance liquid chromatography: collaborative study," J. AOAC Int. 90, 43-53 (2007).
[PubMed]

2006

S. J. Ding, E. Dudley, L. J. Chen, S. Plummer, J. Tang, R. P. Newton, and A. G. Brenton, "Title: Determination of active components of Ginkgo biloba in human urine by capillary high-performance liquid chromatography/mass spectrometry with on-line column-switching purification,"Rapid Commun. Mass Spectrom. 20, 3619-3624 (2006).
[CrossRef] [PubMed]

Y. Dou, H. Mi, L. Z. Zhao, Y. Q. Ren, and Y. L. Ren, "Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis,"Anal. Biochem. 351, 174-180 (2006).
[CrossRef]

M. J. Dubber and I. Kanfer, "Application of reverse-flow micellar electrokinetic chromatography for the simultaneous determination of fiavonols and terpene trilactones in Ginkgo biloba dosage forms," J. Chromatogr. A 1122, 266-274 (2006).
[CrossRef] [PubMed]

Y. He, X. L. Li, and Y. N. Shao, "Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model," Spectrosc. Spectral Anal. (Beijing) 26, 850-853 (2006).

Y. He, S. J. Feng, X. F. Deng, and X. L. Li, "Study on lossless discrimination of varieties of yogurt using the visible/NIR-spectroscopy," Food Res. Int. 39, 645-650 (2006).
[CrossRef]

Y. C. Feng and C. Q. Hu, "Construction of universal quantitative models for determination of roxithromycin and erythromycin ethylsuccinate in tablets from different manufacturers using near infrared reflectance spectroscopy," J. Pharm. Biomed. Anal. 41, 373-384 (2006).
[CrossRef] [PubMed]

2005

R. DeMaesschalck and T. VandenKerkhof, "Implementation of a simple semi-quantitative near-infrared method for the classification of clinical trial tablets,"J. Pharm. Biomed. Anal. 37, 109-114 (2005).
[CrossRef]

M. J. Dubber, V. Sewram, N. Mshicileli, G. S. Shephard, and I. Kanfer, "The simultaneous determination of selected flavonol glycosides and aglycones in Ginkgo biloba oral dosage forms by high-performance liquid chromatography-electrospray ionisation-mass spectrometry,"J. Pharm. Biomed. Anal. 37, 723-731 (2005).
[CrossRef] [PubMed]

2004

G. L. Hu, X. Y. Lu, L. Luo, and Z. D. Xu, "Simultaneous determination of total flavones and total lactones in ginkgo extracts by near infrared spectroscopy," Chin. J. Anal. Chem. 32, 1061-1063 (2004).

H. Q. Qian and P. S. Xie, "Methodology study of HPLC fingerprint of herbal medicine-a case analysis of the fingerprint of total flavonoids extracted from Ginkgo biloba leaves," J. Instrum. Anal. 23, 7-11 (2004).

Y. P. Xu, T. W. Yao, and J. W. Jiang, "HPLC fingerprint of the tablets of Ginkgo biloba L," Journal Zhejiang Univ. , Sci. 33, 24-28 (2004).

1990

T. Sato, S. Kawano, and M. Iwamoto, "Detection of foreign fat adulteration of milk by near infrared spectroscopic method,"J. Dairy Sci. 73, 3408-3413 (1990).
[CrossRef]

1988

Anal. Biochem.

Y. Dou, H. Mi, L. Z. Zhao, Y. Q. Ren, and Y. L. Ren, "Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis,"Anal. Biochem. 351, 174-180 (2006).
[CrossRef]

Appl. Spectrosc.

Chin. J. Anal. Chem.

G. L. Hu, X. Y. Lu, L. Luo, and Z. D. Xu, "Simultaneous determination of total flavones and total lactones in ginkgo extracts by near infrared spectroscopy," Chin. J. Anal. Chem. 32, 1061-1063 (2004).

Food Res. Int.

Y. He, S. J. Feng, X. F. Deng, and X. L. Li, "Study on lossless discrimination of varieties of yogurt using the visible/NIR-spectroscopy," Food Res. Int. 39, 645-650 (2006).
[CrossRef]

J. AOAC Int.

D. Gray, K. LeVanseler, M. Pan, and E. H. Waysek, "Evaluation of a method to determine flavonol aglycones in Ginkgo biloba dietary supplement crude materials and finished products by high-performance liquid chromatography: collaborative study," J. AOAC Int. 90, 43-53 (2007).
[PubMed]

J. Chromatogr. A

M. J. Dubber and I. Kanfer, "Application of reverse-flow micellar electrokinetic chromatography for the simultaneous determination of fiavonols and terpene trilactones in Ginkgo biloba dosage forms," J. Chromatogr. A 1122, 266-274 (2006).
[CrossRef] [PubMed]

J. Dairy Sci.

T. Sato, S. Kawano, and M. Iwamoto, "Detection of foreign fat adulteration of milk by near infrared spectroscopic method,"J. Dairy Sci. 73, 3408-3413 (1990).
[CrossRef]

J. Food Eng.

Y. He, X. L. Li, and X. F. Deng, "Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model,"J. Food Eng. 79, 1238-1242 (2007).
[CrossRef]

J. Instrum. Anal.

H. Q. Qian and P. S. Xie, "Methodology study of HPLC fingerprint of herbal medicine-a case analysis of the fingerprint of total flavonoids extracted from Ginkgo biloba leaves," J. Instrum. Anal. 23, 7-11 (2004).

J. Pharm. Biomed. Anal.

Y. C. Feng and C. Q. Hu, "Construction of universal quantitative models for determination of roxithromycin and erythromycin ethylsuccinate in tablets from different manufacturers using near infrared reflectance spectroscopy," J. Pharm. Biomed. Anal. 41, 373-384 (2006).
[CrossRef] [PubMed]

R. DeMaesschalck and T. VandenKerkhof, "Implementation of a simple semi-quantitative near-infrared method for the classification of clinical trial tablets,"J. Pharm. Biomed. Anal. 37, 109-114 (2005).
[CrossRef]

M. J. Dubber, V. Sewram, N. Mshicileli, G. S. Shephard, and I. Kanfer, "The simultaneous determination of selected flavonol glycosides and aglycones in Ginkgo biloba oral dosage forms by high-performance liquid chromatography-electrospray ionisation-mass spectrometry,"J. Pharm. Biomed. Anal. 37, 723-731 (2005).
[CrossRef] [PubMed]

Journal Zhejiang Univ.

Y. P. Xu, T. W. Yao, and J. W. Jiang, "HPLC fingerprint of the tablets of Ginkgo biloba L," Journal Zhejiang Univ. , Sci. 33, 24-28 (2004).

Rapid Commun. Mass Spectrom.

S. J. Ding, E. Dudley, L. J. Chen, S. Plummer, J. Tang, R. P. Newton, and A. G. Brenton, "Title: Determination of active components of Ginkgo biloba in human urine by capillary high-performance liquid chromatography/mass spectrometry with on-line column-switching purification,"Rapid Commun. Mass Spectrom. 20, 3619-3624 (2006).
[CrossRef] [PubMed]

Spectrosc. Spectral Anal.

Y. He, X. L. Li, and Y. N. Shao, "Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model," Spectrosc. Spectral Anal. (Beijing) 26, 850-853 (2006).

Other

I. Murray, "Forage analysis by near infrared spectroscopy," in Sward Measurement Handbook, A. Davies, R. D. Baker, S. A. Grant, A. S. Laidlaw, eds. (British Grassland Society, 1993), pp. 285-312.

B. G. Osborne, T. Feam, and P. H. Hindle, "Theory of near infrared spectrophotometry," in Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, 2nd ed. (Longman Scientific and Technical, 1993), pp. 13-35.

E. R. Deaville and P. C. Flinn, "Near infrared (NIR) spectroscopy: an alternative approach for the estimation of forage quality and voluntary intake," in Forage Evaluation in Ruminant Nutrition, D. I. Givens, E. Owen, R. F. E. Axford, and H. M. Omedi, eds. (CABI Publications, 2000), pp. 301-320.
[CrossRef]

D. C. Montgomery and E. A. Peck, Introduction to Linear Regression (Wiley, 1982).

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Figures (4)

Fig. 1
Fig. 1

Schematic diagram of the setup for Vis∕NIR measurement of ginkgo tablet, where 1 is the light source, 2 is the beam splitter system, 3 is the reflector, 4 is the sample chamber, 5 is the diffuse reflection detector, and 6 is the data collection.

Fig. 2
Fig. 2

(Color online) Nine absorbance spectral curves from 400 1000 nm for different tablet variety.

Fig. 3
Fig. 3

(Color online) The loading values versus wavelengths (a–f) in the PCA model.

Fig. 4
Fig. 4

(Color online) The variety prediction of left 45 tablets using optimal wavelengths by SMLR.

Tables (1)

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Table 1 Prediction Results for 45 Tablet Samples by PC-ANN Model

Equations (3)

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X = T P 1 + E ,
rms = 1 n i = 1 n ( y ^ i y i ) 2 ,
Y = ( 25098711.23 × 10 6 + 34788208.65 × 10 6 ) × ( X 1 + 52679845.24 × 10 6 ) × ( X 2 + 74562338.84 × 10 6 ) × ( X 3 84756142.96 × 10 6 ) × ( X 4 30622582.84 × 10 6 ) × ( X 5 34130228.49 × 10 6 ) × ( X 6 21259874.62 × 10 6 ) X 7 ,

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