Multiple chromatographic fingerprinting and its application to the quality control of herbal medicines

Analytica Chimica Acta 555 (2006) 217–224

Multiple chromatographic fingerprinting and its application to the quality control of herbal medicines Xiao-Hui Fan a , Yi-Yu Cheng a,∗ , Zheng-Liang Ye a , Rui-Chao Lin b , Zhong-Zhi Qian c a

Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310027, China b National Institute for the Control of Pharmaceutical and Biological Products, Beijing 100050, China c Committee of Chinese Pharmacopoeia, Beijing 100061, China Received 27 July 2005; received in revised form 2 September 2005; accepted 6 September 2005 Available online 17 October 2005

Abstract Recently, chromatographic fingerprinting has become one of the most powerful approaches to quality control of herbal medicines. However, the performance of reported chromatographic fingerprinting constructed by single chromatogram sometimes turns out to be inadequate for complex herbal medicines, such as multi-herb botanical drug products. In this study, multiple chromatographic fingerprinting, which consists of more than one chromatographic fingerprint and represents the whole characteristics of chemical constitutions of the complex medicine, is proposed as a potential strategy in this complicated case. As a typical example, a binary chromatographic fingerprinting of “Danshen Dropping Pill” (DSDP), the best-sold traditional Chinese medicine in China, was developed. First, two HPLC fingerprints that, respectively, represent chemical characteristics of depsides and saponins of DSDP were developed, which were used to construct binary chromatographic fingerprints of DSDP. Moreover, the authentication and validation of the binary fingerprints were performed. Then, a data-level information fusion method was employed to capture the chemical information encoded in two chromatographic fingerprints. Based on the fusion results, the lot-to-lot consistency and frauds can be determined either using similarity measure or by chemometrics approach. The application of binary chromatographic fingerprinting to consistency assessment and frauds detection of DSDP clearly demonstrated that the proposed method was a powerful approach to quality control of complex herbal medicines. © 2005 Elsevier B.V. All rights reserved. Keywords: Multiple chromatographic fingerprinting; Data fusion; Quality control of herbal medicines; Danshen Dropping Pill; Salvia miltiorrhiza Bunge; Panax notoginseng

1. Introduction With tremendous expansion in the use of herbal medicines worldwide, their quality control has been an important concern for both health authorities and the public [1,2]. Among a variety of quality control methods, chromatographic fingerprinting has gained more and more attention recently. It has been widely introduced and accepted by WHO [3], FDA [4], EMEA [5], German Commission E [6], British Herbal Medicine Association [7], Indian Drug Manufacturers’ Association [8], and some other official or nonofficial organizations as a strategy for the assessment of herbal medicines. Lately, Chinese manufacturers are also required by Chinese State Food and Drug Adminis-

∗

Corresponding author. Tel.: +86 571 87951138; fax: +86 571 87980668. E-mail address: [email protected] (Y.-Y. Cheng).

0003-2670/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.aca.2005.09.037

tration (SFDA) to standardize their botanical injections using chromatographic fingerprinting [9]. Chromatographic fingerprint is a chromatogram that represents the chemical characteristics of herbal medicine [10]. Generally, samples with similar chromatographic fingerprint have similar properties. As a result, chromatographic fingerprinting has potential to determine the identity, authenticity, and lot-to-lot consistency of herbal medicines. So far, there are lots of chromatographic fingerprints reports of herbal medicines such as Ginkgo biloba [11–14], Rhizoma chuanxiong [13], Salvia miltiorrhiza Bunge [15,16], Angelica sinensis (Oliv.) diels [17], Forsythia suspensa (Thunb.) Vahl [18], Flos Carthami [19], Shenmai injection [10,20], Tianjihuang [21], Cassia bark [22], etc. Meanwhile, high performance thin layer chromatography (HPTLC) [14], gas chromatography (GC) [18], high performance liquid chromatography (HPLC) [10–12,17,21,22], capillary electrophoresis (CE) [19], high-speed counter-current

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chromatography (HSCCC) [15,16], and some hyphenated chromatographic approaches are already employed to develop fingerprints. In all previous reports, chromatographic fingerprints for testing lot-to-lot consistency of herbal medicines were constructed by a single chromatogram. It was inadequate to represent all chemical patterns or characteristics when the compositions of the herbal medicine are too complex, e.g. multi-herb botanical drug product. It is almost impossible to develop appropriate analytical method (including samples preparation and chromatographic procedure) to represent all chemical characteristics of constituents in a chromatogram. In this complicated case, a combination of analytical methods with different separation principles and test conditions is recommended by FDA [4]. SFDA also indicates that combined chromatographic fingerprints might be useful [9]. Consequently, it is necessary to develop “multiple chromatographic fingerprints” for complex herbal medicine, which consist of more than one fingerprint and represent the whole characteristics of chemical constitutions of the complex medicine, to evaluate their quality, though obtaining reliable multiple chromatographic fingerprints and assessing the quality of herbal medicines through their multiple fingerprints are not trivial work. In this study, “Danshen Dropping Pill” (DSDP), which is composed of S. miltiorrhiza Bunge (Chinese Danshen) and Panax notoginseng (Chinese Sanqi), was investigated as a typical example, to develop multiple chromatographic fingerprinting for quality control. DSDP is the most popular traditional Chinese medicine for the prevention and treatment of coronary arteriosclerosis, angina pectoris, and hyperlipaemia [23–26], and has also been well sold as a diet supplement or a drug in a number of countries such as the USA, Russia, Singapore, South Korea, and UAE. In general, depsides and saponins, extracted from S. miltiorrhiza Bunge and P. notoginseng, respectively, are considered as the major active constituents of DSDP. Nevertheless, danshensu, a kind of depside, is the unique marking compound for the evaluation of the quality of this formula currently, which is set to at least 0.13 mg of danshensu per pill [23]. Apparently, this standard is not sufficient for quality control. To improve the standard of quality control of DSDP, chromatographic fingerprinting is necessary. Although a number of analytical methods including HPLC [27–29], direct MS [30], HPLC/MS [31,32] for S. miltiorrhiza Bunge or P. notoginseng have been reported, it is still difficult to represent the whole chemical characteristics of depsides and saponins of DSDP in a single chromatogram because of their significant difference in chemical properties. In the pilot study, we found that the major depsides can be clearly represented in a single chromatogram, but some of the major saponins, such as notoginsenoside R1 , cannot be well represented in the chromatogram due to their low concentration and weak UV absorption. In this work, two HPLC fingerprints that, respectively, represent chemical characteristics of depsides and saponins in DSDP were developed, which constructed binary chromatographic fingerprints of DSDP. Further, the binary chromatographic fingerprints were applied to testing lot-to-lot consistency and detecting frauds. The proposed methods are equally applicable to other complex herbal medicines for quality control.

2. Methodology and experiments 2.1. Methodology of multiple chromatographic fingerprinting In general, multiple chromatographic fingerprinting is composed of the multiple chromatographic fingerprints acquirement procedure (including analytical methods, authentication, and analytical methods validation) and fingerprints comparison procedure. In chromatographic fingerprints acquirement procedure, we are able to obtain multiple chromatograms, which demonstrate that they could chemically represent characteristics of the analyte, and validate the analytical methods. Moreover, chemical characteristics of crude drugs should present in chromatograms of the analyte, when the analyte is the final product of herbal medicines. In this paper, this procedure is called as “authentication of multiple chromatographic fingerprints”. After multiple chromatographic fingerprints of analyte were obtained, we compared the chromatographic fingerprints of analyte with the reference/standard fingerprints to quantitatively evaluate the quality of herbal medicines, for example, the lotto-lot consistency, using similarity measures or chemometrics approaches. 2.2. Reagents and materials Acetonitrile and methanol were HPLC grade from Tedia (Fairfield, USA). A.R. grade acetic acid and phosphoric acid for analysis were from Hangzhou Reagent Company (Hangzhou, China). Ammonia solution 25% extra pure was from Shanghai Reagent Company (Shanghai, China). Water was purified by a Milli-Q academic water purification system (Milford, MA, USA). Standards of danshensu, protocatechualdehyde, rosmarinic acid, and salvianolic acid B were purchased from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China) and standards of notoginsenoside R1 , ginsenoside Re, Rg1 , Rb1 , Rd, Rg2 , Rh1 were purchased from the College of Pharmaceutical Sciences, Jilin University (Changchun, China). “Danshen Dropping Pill”, S. miltiorrhiza Bunge, and P. notoginseng were supplied by TASLY Pharmaceutical Co. Ltd. (Tianjin, China). 2.3. Sample preparation Fingerprint I: 0.25 g DSDP was dissolved in 10 mL water with a supersonic process for 15 min. The solution was subjected to HPLC analysis after centrifugation at 10,000 rpm for 10 min. The sample injection volume was 10 ␮L. Fingerprint II: 1.0 g DSDP was dissolved in 10 mL 4% ammonia solution with a supersonic process for 15 min. The solution was filtrated through 0.45 ␮m nylon film (Shanghai Institute of Pharmaceutical Industry, Shanghai, China). Then 5 mL filtrate was applied on a C18 column (500 mg, SUPELCO, Park Bellefonte, USA) and successively washed with 15 mL 20%

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methanol–4% ammonia solution to elute the phenolic compounds entirely off. Finally, the cartridge was eluted with 5 mL methanol. The centrifuged methanol elute was collected for HPLC analysis. The sample injection volume was 20 ␮L. 2.4. Chromatographic procedure 2.4.1. HPLC analysis An Agilent 1100 series HPLC system (Agilent, USA) equipped with a quaternary pump, a vacuum degasser, an autosampler, a diode-array detector, a column heater–cooler, and ChemStation system were used for HPLC analysis. The column used was a Zorbax SB-C18 column (4.6 mm × 250 mm, 5 ␮m, Agilent Company, USA) coupled with Agilent C18 pre-column (4 mm × 5 mm). Fingerprint I: The mobile phase was solvent A (H3 PO4 :H2 O = 0.02:100) and solvent B (H3 PO4 :CH3 CN = 0.02:100) in the gradient mode at 30 ◦ C as follows: 8–18% B at 0–8 min, 18–21% B at 8–15 min, 21–34% B at 15–40 min. The flow rate was 1.0 mL/min. The effluent was monitored at 280 nm. Fingerprint II: The mobile phase was solvent A (CH3 COOH: H2 O = 0.01:100) and solvent B (CH3 COOH:CH3 CN = 0.01:100) in the gradient mode at 30 ◦ C as follows: 20–35% B at 0–15 min, 35% B isocratic for 10 min, 35–43% B at 25–40 min, 43% B isocratic for 10 min, 43–58% B at 50–65 min, 58–75% B at 65–70 min. The flow rate was 0.8 mL/min. The effluent was monitored at 203 nm. 2.4.2. Method validation The validation of the analytical method was carried out with sample solutions, i.e. Fingerprint I solutions and Fingerprint II solutions. The instrument/injection precision (repeatability) was obtained by analyzing the variations of retention time and peak area of six injections. The intra-day and inter-day precisions of the method were evaluated using multiple preparations of the same sample. Five replicate samples were prepared and analyzed in a single day and on three different days.

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the reference/standard fingerprints to quantitatively test the lotto-lot consistency of herbal medicines. Up to now, varieties of methods were employed to quantitatively test the lot-tolot consistency of herbal medicines [10,19,20]. However, all the reported methods are based on a single chromatogram. Therefore, the information contained in the multiple fingerprints should be combined before employing the lot-to-lot consistency testing methods. In this paper, a data fusion-based method [33] was employed to evaluate the similarity of multiple chromatographic fingerprints. To accurately capture the information encoded in a chromatogram, a chromatographic fingerprint was usually mathematically represented by a vector of their peak areas. Thus, taking binary chromatographic fingerprints as an example, assume that vector α = [α1 , . . ., αi , . . ., αm ]T and vector β = [β1 , . . ., βj , . . ., βn ]T represent Fingerprint I and Fingerprint II, respectively, where αi denotes absolute area of the ith peak of Fingerprint I, βj denotes absolute area of the jth peak of Fingerprint II, and the superscript T indicates the transpose of matrix. The new fused vector φ representing integral chemical characteristics of binary fingerprints is formed by φ = [α, θβ], where θ is the combined coefficient. This data fusion-based method is easily generalized to higher-dimensional chromatographic fingerprinting. Consequently, the similarity between multiple chromatographic fingerprints can be easily determined by comparing their fusion vector using commonly used similarity measures, e.g. cosine, correlation coefficient, etc. [20]. To minimize the variability in different experimental conditions, every sub-fingerprint of multiple chromatographic fingerprints is normalized using the following equation: αi αi =
n (1) i=1 αi where αi is the normalized peak area, and αi is the absolute area of peak i in the sub-fingerprint. 3. Results and discussion 3.1. Authentication of binary chromatographic fingerprints

2.4.3. Identification of constituents of herbal medicines with LC/MSD In order to identify the peaks of the binary fingerprints, an Agilent 1100 HPLC/MSD (Agilent Company, USA) equipped with an electrospray ionization source was used. The MS spectra were acquired in negative ion mode for the detection of depsides and saponins. N2 was used both as drying gas with a flow rate of 12 L/min and as nebulizing gas with a pressure of 45 psi. The nebulizer temperature was set at 350 ◦ C and the capillary voltage was set at 3500 V. The mass spectra were recorded in the range of 100–1200 u for the detection of depsides, and in the range of 400–1500 u for the detection of saponins. 2.5. Similarity of multiple chromatographic fingerprints It is well established that the samples with similar chromatographic fingerprint likely have similar properties. Therefore, we can compare the chromatographic fingerprints of samples with

The resulting chromatograms are shown in Fig. 1. The main constituents of DSDP samples were identified by UV spectra and LC/MS as described in Section 2.4.2. On the basis of these UV spectra, MS spectra of [M − H]− ions and previous results of our lab [34] and others [27,32], the main peaks of two chromatograms were identified in Tables 1 and 2. Some of the results were confirmed by comparison with the standards. Obviously, the main constituents of DSDP in chromatogram I are depsides, including those well-known pharmacologically active and mark compounds, e.g. danshensu, salvianolic acid B and so on. Chromatogram II consists of main characteristic components of saponins in DSDP. These two chromatograms collectively represent the main chemical characteristic components of DSDP. The relationship between the chromatograms of the product and those of the corresponding raw materials is also considered. According to Council [23], raw materials S. miltiorrhiza

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Fig. 1. HPLC chromatograms of DSDP: (A) Fingerprint I; (B) Fingerprint II.

Bunge and P. notoginseng were extracted with water. The centrifuged solutions were analyzed by HPLC as described in Section 2.4.1. The resulting chromatograms are shown in Fig. 2. Chromatogram I is very similar to that of S. miltiorrhiza Bunge (Fig. 2A), and chromatogram II is very similar to that of P. notoginseng (Fig. 2B). That is, it is demonstrated that the chemical characteristics of raw materials present in chromatograms of product met the demands of the national standard. So far a conclusion can be generalized that the resulting chromatograms can be used as binary chromatographic fingerprints of DSDP for quality control. 3.2. Validation of binary chromatographic fingerprints The analytical methods of binary fingerprinting have been validated based on the retention time and the peak area. The results, which clearly demonstrated the reproducibility of the sample preparation, were listed in Tables 3 and 4. Table 1 Identification of constituents of Fingerprint I of DSDP Peak no.

Retention time

Identity

UV-λmax

1 2 3 4 5 6 7 8 9 10

6.16 9.94 16.71 17.65 20.27 22.68 23.72 24.76 27.64 30.99

Danshensu Protocatechualdehyde – – Salvianolic acid D Salvianolic acid E Rosmarinic acid – Salvianolic acid B Salvianolic acid A

280 231, 280, 310 327 327 247, 321 330 329 250, 290 254, 286, 309 288

The instrument/injection precision (repeatability) of Fingerprint I, represented by the relative standard deviation (R.S.D.), were below 0.30% (n = 6) for retention times and within 0.15–1.74% (n = 6) for peak areas. The intra-day precisions (R.S.D.) of Fingerprint I were 0.11–0.32% (n = 5) for retention times and 0.45–3.03% (n = 5) for peak areas, while the interday precisions (R.S.D.) were 0.17–0.36% (n = 15) for retention times and 1.18–3.96% (n = 15) for peak areas. The injection precision of Fingerprint II was in the range of 0.07–0.27% (n = 6) for retention times and 0.63–1.67% Table 2 Identification of constituents of Fingerprint II of DSDP Peak no.

Retention time

Identity

[M − H]−

1 2 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

11.39 12.66 12.72 21.29 21.73 23.54 24.50 25.89 31.82 36.37 43.09 44.19 46.07 47.76 50.26 56.05 56.92 69.65 70.77

R1 Re Rg1 Rb1 R2 Rg2 Rh1 Rh1 iso. Rd Rd iso. – – Rg6 or F4 Rk3 or Rh4 Rk3 or Rh4 20(R) Rg3 20(S) Rg3 Rk1 or Rg5 Rk1 or Rg5

931 945 799 1107 769 783 637 637 945 945 751 751 765 619 619 783 783 765 765

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Fig. 2. Binary chromatographic fingerprints of DSDP and the chromatograms of the corresponding raw materials: (A) Fingerprint I vs. S. miltiorrhiza Bunge; (B) Fingerprint II vs. P. notoginseng.

(n = 6) for peak areas. The intra-day precisions (R.S.D.) of Fingerprint II were 0.08–0.56% (n = 5) for retention times and 0.68–3.82% (n = 5) for peak areas, while the inter-day precisions (R.S.D.) were within the range of 0.12–0.50% (n = 15) for retention times and 1.49–4.43% (n = 15) for peak areas, respectively.

3.3. Assessing the quality using binary chromatographic fingerprints The developed binary fingerprinting was used to assess lotto-lot consistency and detect frauds. In this study, 34 samples, 14 normal samples, 10 special samples with different ratios of

Table 3 Analytical method validation results of Fingerprint I Peak no.

1 2 3 4 5 6 7 8 9 10

R.S.D. of retention time (%)

R.S.D. of peak area (%)

Injection (n = 6)

Intra-day (n = 5)

Inter-day (n = 15)

Injection (n = 6)

Intra-day (n = 5)

Inter-day (n = 15)

0.30 0.26 0.29 0.27 0.24 0.25 0.23 0.24 0.23 0.18

0.32 0.30 0.11 0.12 0.18 0.20 0.18 0.20 0.22 0.23

0.36 0.28 0.26 0.27 0.28 0.26 0.25 0.27 0.25 0.17

0.19 0.17 0.20 0.34 0.33 1.74 0.38 0.42 0.15 0.25

1.82 1.96 2.81 0.45 1.64 2.10 2.15 2.47 2.00 3.03

1.86 2.29 1.18 1.88 1.57 3.66 2.57 3.96 2.50 2.28

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Table 4 Analytical method validation results of Fingerprint II Peak no.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

R.S.D. of retention time (%)

R.S.D. of peak area (%)

Injection (n = 6)

Intra-day (n = 5)

Inter-day (n = 15)

Injection (n = 6)

Intra-day (n = 5)

Inter-day (n = 15)

0.20 0.18 0.09 0.09 0.12 0.16 0.19 0.27 0.16 0.14 0.15 0.18 0.18 0.21 0.08 0.07 0.20 0.17

0.20 0.21 0.29 0.21 0.23 0.28 0.33 0.56 0.37 0.22 0.22 0.24 0.26 0.29 0.08 0.30 0.13 0.12

0.28 0.22 0.24 0.18 0.21 0.25 0.30 0.50 0.33 0.22 0.22 0.24 0.25 0.28 0.14 0.28 0.13 0.12

0.95 0.86 1.15 0.65 1.58 0.49 1.37 0.75 1.61 1.35 1.07 1.14 0.63 0.82 1.45 0.96 1.67 1.44

3.35 2.38 3.20 1.60 1.02 1.30 1.27 3.14 3.37 0.68 2.09 3.82 1.11 3.15 1.31 1.68 2.21 2.17

2.85 2.05 2.26 1.96 2.53 1.55 2.74 2.49 3.73 2.85 3.17 3.61 2.31 4.43 1.93 2.89 1.49 1.60

S. miltiorrhiza Bunge to P. notoginseng and 10 frauds, were investigated. 3.3.1. Testing the lot-to-lot consistency of DSDP Fingerprint I and Fingerprint II were represented by a vector of 10- and 18-dimensions, respectively, as described in Section 2.5. Correlation coefficient was employed to evaluate the similarity of fingerprints. In addition, according to the quantitative results of depsides and saponins, we can conclude that contents of depsides represented in Fingerprint I was nearly half of saponins represented in Fingerprint II. As a consequence, the combined coefficient θ is 2.2. Thus, using the data fusion method described in Section 2.5, all the binary chromatographic fingerprints (i.e. samples) were represented by fusion vectors. To test lot-to-lot consistency, a reference/standard fingerprint representing the mean of the fusion vectors of all the normal samples was constructed as suggested by SFDA [9]. Subsequently, lot-to-lot consistency was quantitatively determined, based on the reference/standard fingerprints, using the correlation coefficient measure. The similarity between a tested sample and the reference/standard sample could be easily inspected

(Fig. 3). The relative peak area of danshensu (the ratio of danshensu’s peak area of tested sample to the mean peak area of danshensu of all the normal samples) is also displayed in Fig. 3. As shown in Fig. 3, variation between danshensu’s relative peak areas of two kinds of samples, normal samples and special samples, are indistinct. For example, the relative peak areas of sample #3 and #13 are far lower than that of other normal samples, while the relative peak areas of some special samples, i.e. #16, #17 and #18, are even greater than the mean value of all the normal samples. This indicates that it is impossible to identify the consistency of DSDP by current unique-mark-compoundbased quality control method. On the contrary, similarity values of two kinds of samples, normal samples and special samples, have different patterns. Here, if we set an appropriate threshold [9], such as 0.85, it is easy to find that all the normal samples have a similarity value greater than it, while special samples with a different ratio of S. miltiorrhiza Bunge to P. notoginseng have low values of the similarity which is less than the threshold. Therefore, we can discriminate normal samples from the frauds according to their similarity values. As

Fig. 3. Histogram maps of normal samples and special samples. The first 14 are normal samples, and the rest represent special samples with different ratios of S. miltiorrhiza Bunge to P. notoginseng.

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Fig. 4. Histogram maps of normal samples and frauds. The first 14 are normal samples, and samples 15–21 and 22–24 are fraud samples produced from inferior S. miltiorrhiza Bunge and P. notoginseng, respectively.

a consequence, it was demonstrated that the presented binary chromatographic fingerprinting is more powerful and capable of identifying the lot-to-lot consistency of samples than current one-mark-compound-based quality control method. 3.3.2. Detection of frauds Ten frauds of DSDP that were produced from inappropriate materials, front 7 (nos. 15–21, Fig. 4) from inferior P. notoginseng and the rest (nos. 22–24, Fig. 4) from inferior S. miltiorrhiza Bunge, were used to verify the detection capability on the frauds. Similarly, we quantitatively evaluated their relative peak areas of danshensu and correlation coefficient values according to the previous description (Fig. 4). As revealed by Fig. 4, all the normal samples have high similarity values (greater than 0.88), while frauds have low values of the similarity (less than 0.77). Therefore, if we set an appropriate threshold, such as 0.85, the frauds can be distinguished from normal samples according to similarity values of their binary chromatographic fingerprints, although danshensu’s relative peak areas of two kinds of samples are out of order. However, it is still difficult to distinguish the front seven frauds from the rest frauds directly by their similarity values. In this regard, a well-known chemometrics approach, principal components analysis [35], was further employed as a potential tool to detect the frauds based on their binary chromatographic fingerprints.

The score plots derived from the first two PCs are shown in Fig. 5, where each sample is represented as a marker. It is noticeable that the samples were clearly clustered in three domains (i.e. normal samples, front seven frauds, and the rest frauds). Therefore, the presented binary chromatographic fingerprints conjunction with chemometrics approach offers a powerful way for frauds detection of herbal medicines. 4. Conclusion In this study, multiple chromatographic fingerprinting, which consists of more than one chromatographic fingerprint and represent the whole chemical characteristics of the analyte, is proposed as a strategy for quality control of complex herbal medicines instead of reported single chromatographic fingerprinting. As a typical example, binary chromatographic fingerprinting of DSDP was developed for consistency assessment and frauds detection. The results indicate that multiple chromatographic fingerprinting could show the promising prospect for analysts to directly address the difficult problems in quality control of complex herbal medicines. Acknowledgements This study was financially supported by the Chinese Key Technologies R&D Program (Grant No. 2001BA701A01) and a key grant from the National Natural Science Foundation of China (Grant No. 90209005). The authors would like to thank Dr. Leming Shi of the National Center for Toxicological Research of the US Food and Drug Administration for constructive suggestions and helpful discussions. References

Fig. 5. Distribution of samples on the plan of the first two principal components: () normal samples; () frauds produced from inferior P. notoginseng; (♦) frauds produced from inferior S. miltiorrhiza Bunge.

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