Development of Near Infrared Spectroscopy for Rapid Quality Assessment of Red Ginseng

Transcription

1 CHEM. RES. CHINESE UNIVERSITIES 2009, 25(5), Development of Near Infrared Spectroscopy for Rapid Quality Assessment of Red Ginseng NIE Li-xing *, WANG Gang-li and LIN Rui-chao National Institute for the Control of Pharmaceutical and Biological Products, Beijing , P. R. China Abstract Near infrared spectroscopy(nirs) was developed as a rapid analysis method for the qualitative and quantitative assessment of the quality of red ginseng. Discriminant analysis(da) based on principal component analysis and Mahalanobis distance was used to distinguish red ginseng from counterfeits non-destructively. The result shows that the proposed method could distinguish red ginseng from counterfeits correctly and no misclassified sample was found in both training and test sets. The partial least squares(pls) algorithm was used to predict the sum of ginsenosides Re and Rg 1 and the content of ginsenoside Rb 1. Two calibration models were developed to correlate NIR spectra with the reference values determined by HPLC method. The correlation coefficient(r), the root mean square error of calibration(rmsec) and the root mean square error of prediction(rmsep) were as follows: R=0.9827, RMSEC=0.0163%, RMSEP=0.0250% for the sum of ginsenosides Re and Rg 1 ; R=0.9869, RMSEC=0.0156%, RMSEP=0.0256% for content of ginsenoside Rb 1. The overall results demonstrate that NIRS coupled with chemometrics could be successfully applied as a rapid, precise and cost-effective method not only to identify the red ginseng from counterfeits but also to determine simultaneously some chemical compositions in red ginseng. Keywords Near infrared spectroscopy; Red ginseng; Discriminant analysis; Partial least squares Article ID (2009) Introduction Red ginseng(radix Ginseng Rubra) is the steamed and dried root of the cultivar of Panax ginseng C.A.Mey. It s commonly used in East Asian countries to improve psychological function, exercise performance, immune function, and condition associated with diabetes. The main active components in red ginseng are saponins such as ginsenosides Rb 1, Re, Rf, Rg l, etc. They are responsible for various pharmacological activities of red ginseng [1 3]. Since commercial red ginseng has good reputation and high price, the counterfeits made from various other plants roots are flooding the market. They are very similar to red ginseng when they are viewed with the naked eyes but are made of entirely different original plants. To control the quality of red ginseng, the assay of ginsenosides Re, Rg l and Rb 1 is required in the Pharmacopoeia of the People s Republic of China [4]. However, the assigned HPLC method is complex and time-consuming. Near infrared spectroscopy(nirs) is a rapid, cost effective and non-destructive method allowing, besides the identification of samples, the simultaneous determination of components in a mixture by multivariate data analysis. It has become a widely used analytical method of quality control of pharmaceutic and natural products [5 9]. Compared to routine methods, NIRS offers much shorter analysis time and higher throughput, which makes over-all quality control of raw materials and products possible. In this study, a non-destructive method was developed to distinguish red ginseng from its counterfeits by NIR spectroscopy combined with discriminant analysis. Besides, NIR spectroscopy with PLS algorithm was used to determine simultaneously ginsenosides Re, Rg l and Rb 1 in this herb. 2 Materials and Methods 2.1 Materials and Reagents The analytical grade reagents, including chloroform and n-butanol, were purchased from Beijing Reagent Company(China). Acetonitrile (HPLC reagent-grade) was from Merck(Darmstadt, Germany). Bidistilled water was purified by Quantum IX Cartridge water purification system(millipore, USA). Ginsenosides Re, Rg l and Rb 1 were from National Institute for the Control of Pharmaceutical and Biological Products. Red ginseng samples of 80 and 163 counterfeits were collected from herb markets all over *Corresponding author. nielixing@163.com Received May 26, 2008; accepted October 23, 2008.

2 634 CHEM. RES. CHINESE UNIVERSITIES Vol.25 China. The English names, Chinese spelling, original Table 1. plants and numbers of all samples are listed in Table 1 Sample information Product Sample Original plant No. Certified product Red ginseng(hongshen) Panax ginseng C.A.Mey 80 Counterfeits panicled fame flower(lulan) Talinum paniculatum(jacq.) Gaertn. 19 funneled Physochlaina(Huashanshen) Physochlaina infundibularis Kuang 30 Indian lettuce(shanwoju) Lactuca indica.l 25 balloon flower(jiegeng) Platycodongrandiflorum(Jacq.) A, DC 25 bighorn vetch(yejiangdou) Vigna vexillata (L.) Benth. 25 pokeweed(shanglu) Phytolacca acinosa Roxb Spectra Collection For qualitative analysis, all the samples were directly measured by the fiber optic reflectance probe of Antaris FT-NIR analyzer(method Development Sampling System, Thermo Nicolet Co., Madison, USA) in the wavenumber range of cm 1 with a resolution of 8 cm 1 and a scan number of 80. For quantitative analysis, the red ginseng samples were cut and ground into powder. The final powder samples were prepared by passing the ground powder through a 60-mesh sieve. For each sample, about 4 g of powder was filled in a sample vial(1 cm in diameter and 4 cm in thickness) for analysis. Then the vial was placed on the sampling window for scanning the spectrum collected by the integration sphere module standard to the instrument. 2.3 Reference Analysis The determination of the sum of ginsenosides Re and Rg 1 and the content of ginsenoside Rb 1 were carried out by HPLC method [4]. The HPLC system consisted of a HPLC system(model 2695, Waters, USA) and a photo diode array detector(dad, Model 2996, Waters, USA). Data were recorded on a computer based data system(empowered Waters, USA). calibration models were developed for the sum of ginsenosides Re and Rg l and the content of ginsenoside Rb 1. For each model, 10 samples were selected randomly for test, the rest for training. The content of the components was equally distributed over the range in both the training and test sets. The selections of wavelengths, mathematical pretreatment and PLS factors were based on the lowest root mean square error of cross-validation(rmsecv). 3 Results and Discussion 3.1 Qualitative Analysis of DA Algorithm Fig.1 shows the mean spectra of each class for the original data. Since NIR spectra generally contain a number of broad and overlapping bands and there are lots of components in plant samples, it is difficult to distinguish red ginseng from the counterfeits without any data analysis. 2.4 Chemometrics and Data Analysis All chemometrical modeling was performed via TQ Analyst Software(Thermo Nicolet Co.). The spectra used for the data analysis covered the range from 4000 to cm 1, and the data were measured at cm 1 intervals, which resulted in 1557 variables. Discriminant analysis(da) based on principal components and Mahalanobis distance [10] was proposed to identify red ginseng from its counterfeits. Sample spectra were divided into training and test sets at random selection. A partial least squares(pls) algorithm was used for quantitative analysis. Individual NIRS Fig.1 Mean spectra of red ginseng samples and counterfeits a. Red ginseng; b. root of pokeweed; c. root of panicled fame flower; d. root of Indian lettuce; e. root of bighorn vetch; f. root of balloon flower; g. root of funneled physochlaina. The discriminant analysis classification technique can be used to determine the class or classes of known materials which are most similar to an unknown material by calculating the unknown distance from each class center in Mahalanobis distance units. It applies the spectral information in the specified region or regions of an unknown sample spectrum to a stored calibration model to determine which class of

3 No.5 NIE Li-xing et al. 635 standards is most similar to the unknown. During calibration, by means of the software a mean spectrum is calculated and then a distribution model is generated by estimating the variance at each frequency in the analysis range. In this study, a unique distribution model for each class was used; by virtue of the software the class average was subtracted from each standard and only the information from a given class was used to create a unique variance spectrum for that class. When an unknown sample is coming, by right of the software a principal component analysis is performed on the spectra of the standards and those results are used to determine score values for the unknown sample spectrum. The score plots are used to produce Mahalanobis distance values, which in turn are used to rank the classes. The result of discriminant analysis is the name of the class that is most similar to the spectrum of the unknown sample. On building the DA model, each kind of plant was categorized as one class. First 4 spectra were Table 2 randomly selected from each class to constitute the test set and then the training set was composed of the other remained spectra. Whole range of the original spectra was used to build the calibration model. Since the common components, such as fiber and starch, took up large proportions of ingredients in the 7 kinds of plant roots, 15 principle components were selected to calculate the Mahalanobis distance values. In this way, the information of low content components that were unique in each plant played important role in differentiating the samples. The loadings of the 15 principle components in each category are shown in Table 2. DA model was established based on the Mahalanobis distance values of samples in the training set. The result shows that all the samples in the training set were identified correctly. Then the class of samples in the test set was predicted by the proposed model, and the calculated class was in good agreement with the actual class. The identification results of the training set and the test set are shown in Table 3. Loadings of 15 principle components in 7 categories Categories Principle component panicled funneled Red ginseng fame flower Physochlaina Indian lettuce balloon flower bighorn vetch pokeweed Table 3 Identification results of DA model Product Categories No. of samples No. of correctly classified samples No. of misclassified samples Training set Test set Training set Test set Training Test set Certified product Red ginseng t 0 0 Counterfeits panicled fame flower funneled physochlaina Indian lettuce balloon flower bighorn vetch pokeweed Quantitative Analysis of PLS Algorithm Partial least squares(pls) algorithm is a quantitative spectral deconvolution technique to extract relevant information from complex spectra. In this study, individual NIRS calibration was developed for the sum of ginsenosides Re and Rg l and the content of ginsenoside Rb 1 in red ginseng. From Table 4, it is seen that the range of y-value in the training set covered the range in the test set, therefore the distribution of the samples was appropriate in the training and

4 636 CHEM. RES. CHINESE UNIVERSITIES Vol.25 Table 4 Reference measurements in training and test sets(%) Sum of ginsenoside Re and Rg l Ginsenoside Rb 1 Set Range Mean Standard deviation Range Mean Standard deviation Training Test test sets. In the application of PLS algorithm, it is generally known that the mathematical pretreatment, the wavelengths range and the number of PLS factors are critical parameters, and they are selected according to the results of full cross-validation. In full crossvalidation, the spectrum of one sample of the calibration set is selected from this set and a PLS model is built with the remained spectra of the calibration set. The left-out sample is predicted with this model and the procedure is repeated with leaving out each of the samples of the calibration set. The optimum parameters used for each model were determined by the lowest root mean square error of cross validation (RMSECV)(Table 5). For the two models, multiplicative scatter correction(msc) was employed to correct the physical influences by adjusting the spectra based on the ranges of wavelengths supposed to carry no specific chemical information. First and second derivative(1st D and 2nd D) spectra were obtained to eliminate baseline drifts and enhance small spectral differences. To avoid enhancing the noise induced by derivative, spectra were first smoothed by Norris derivative filter. Fig.2 shows the pretreated spectra. Primarily, the calibration is done to develop the model, predicting later the unknown concentrations with spectral data from new samples. The accuracy of the model is described by the correlation coefficient (R) and the value of the root mean square error of calibration(rmsec). The accuracy in the model used to predict unknown samples is expressed by the value of the root mean square error of prediction(rmsep). The value represents the average difference between measured and predicted response values of samples in the training and test sets. Table 5 shows the NIRS calibration results obtained by the PLS models for red ginseng. The scatter plot in Fig.3 shows the model for determining the sum of ginsenosides Re and Rg l and Table 5 Optimum parameters and calibration results of two calibration models Fig.2 Pretreated red ginseng spectra obtained from first(a) and second(b) derivative data 80 spectra were overlaid. the content of ginsenoside Rb 1. Compared with the ginsenoside Rb 1 model, the model of the sum of ginsenosides Re and Rg l was a little worse because the reference value was a summation. Ginsenoside Sum of Re and Rg 1 Rb 1 Spectra pretreatment MSC+2nd D MSC+1st D /cm PLS factor R RMSEC (%) RMSEP (%) Fig.3 Reference measurement vs. NIRS prediction of training( ) and test set( ) for sum of ginsenosides Re and Rg l (A) and ginsenoside Rb 1 (B)

5 No.5 NIE Li-xing et al Conclusions It could be concluded that NIR spectroscopy technique has a high potential to identify the red ginseng from counterfeits without destruction and determine simultaneously some chemical compositions in red ginseng. Thus, a simple, rapid and reliable overall characterization of red ginseng quality could be obtained at a low cost. It might be an application for preliminary screening in quality inspection of herbal medicine. In comparison to time-consuming chemical methods, the results obtained in this study represent a considerable improvement in estimating the red ginseng quality by NIRS. References [1] Kim H. Y., Kim K. J., Agric. Food Chem., 2007, 55(8), 2816 [2] Kimura Y., Sumiyoshi M., Kawahira K., et al., Br. J. Pharmacol., 2006, 148(6), 860 [3] Zhang H., Zhou Q. M., Li X. D., Arch. Pharm. Res., 2006, 29(2), 145 [4] China Pharmacopoeia Committee, Pharmacopoeia of the People s Republic of China, 2005 Ed., Vol.Ⅰ, Chemical Industry Press, Beijing, 2005, 7 [5] Rosa S. S., Barata P. A., Martins J. M., et al., Pharm. Biomed. Anal., 2008, 47(2), 320 [6] Wu Y. W., Sun S. Q., Zhou Q., et al., Pharm. Biomed. Anal., 2008, 46(3), 498 [7] Bodson C., Rozet E., Ziemons E., et al., Pharm. Biomed. Anal., 2007, 45(2), 356 [8] MI H., Guo Y., Li W. L., et al., Chem. Res. Chinese Universities, 2007, 23(1), 116 [9] Du L. N., Wu L. H., Lu J. H., et al., Chem. Res. Chinese Universities, 2007, 23(5), 518 [10] McCarthy W. J., TQ Analyst User s Guide, Thermo Nicolet Co., Madison, 2000