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Pretreatments of chromatographic fingerprints for quality control of herbal medicines
Journal of Chromatography A, 1134 (2006) 253–259
Pretreatments of chromatographic fingerprints for quality control of herbal medicines Cheng-Jian Xu a,∗ , Yi-Zeng Liang b , Foo-Tim Chau c , Yvan Vander Heyden a a
Department of Analytical Chemistry and Pharmaceutical Technology, VICIM Partner, Vrije Universiteit Brussel-VUB, Laarbeeklaan 103, B-1090, Brussels, Belgium b Research Center of Modernization of Chinese Medicines, College of Chemistry and Chemical Engineering, Central South University, 410083 Changsha, China c Chemometrics and Herbal Medicine Laboratory, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China Received 6 March 2006; received in revised form 17 August 2006; accepted 21 August 2006 Available online 7 September 2006
Abstract Pretreatments of chromatographic fingerprints are important for quality control of herbal medicines and they include data correction and data transformation. The data correction can reduce the variations of experimental procedures, and data transformation can put different weights on the different parts of the fingerprints. In this paper, a new target peak alignment (TPA) procedure has been proposed to correct the retention time shifts, multiplicative scattering correction (MSC) has been introduced for response correction. Then the similarity of the fingerprints with mean and median fingerprints is used to evaluate the quality of herbal medicines (HMs). Furthermore, different data transformation methods with kernel principal component analysis (PCA) have been applied to the data and their effects were discussed. The proposed approaches have been demonstrated by the essential oils data set of a herbal medicine, named Houttuynia cordata (HC), containing samples from different geographic origins. The experimental results indicate that the proposed approaches may be helpful in the quality control of herbal medicines by fingerprints. © 2006 Elsevier B.V. All rights reserved. Keywords: Herbal medicine (HM); Peak alignment; GC–MS; Quality control; Data pretreatment; Chromatographic fingerprint
1. Introduction Herbal medicines (HMs) have played an important role in the clinical therapy in many oriental countries for thousands of years. During the past decades, they attracted more and more the attention in western countries because of their high pharmacological activity with low toxicity and rare complications. However, HMs have not been officially recognized world-wide yet because the quantity and quality of the safety and efficacy data are far from sufficient to meet the general quality criteria [1]. Therefore, the fingerprint analysis methods have been introduced and accepted recently by the World Health Organization ∗
Corresponding author. Present address: Chemometrics and Herbal Medicine Laboratory, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. Tel.: +852 2766 5585; fax: +852 2364 9932. E-mail address: [email protected] (C.-J. Xu). 0021-9673/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2006.08.060
(WHO) as a strategy for the assessment of the quality of HMs [2]. A fingerprint of a HM is a chromatogram (or electropherogram) here representing all the detectable chemical components present in the extract and being separated as much as possible so as to identify and characterize that HM. With the help of the fingerprint, the authentication and identification of a HM can be reliably conducted even if the quality and quantity of the constituents are unknown. Thus, for evaluating the quality of a HM, one can consider multiple constituents in the HM extracts, instead of only one or two marker components for evaluating the quality in the conventional approach. When one deals with chromatographic fingerprints of HMs from different sources, one can do similarity evaluation or pattern recognition for quality control purposes. Therefore, to meet the requisite that the same variable contains the same kind of information for multivariate analysis, the data corrections,
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including retention time and response correction, are usually necessary [3]. Many approaches have been proposed for retention time correction [3–8], such as correlation optimized warping (COW) [4], dynamic time warping (DTW) [5] and parametric time warping (PTW) [6]. Among them, the COW method is considered very often as the most effective one [7]. However, optimizing the input parameters of COW is time consuming and therefore applicability of this method for complex herbal medicine systems is rather tedious. Furthermore, all the above warping methods [3–8] do not make full use of the spectral information from the data sets with multi-channel detectors. The popular use of hyphenated chromatographic instruments, such as HPLC/DAD (high performance liquid chromatography/diode array detector) and GC–MS (gas chromatography/mass spectrometry) provide spectral information besides chromatograms. If such information can be handled properly and efficiently, the correction of retention time shift will be more reliable. A fingerprint of a HM is essentially representing relative concentrations of all the components which are detectable in the extract, and their relative concentrations are proportional to their chromatographic responses. However, a same response in different experiments does not stand for same concentration because experimental variations are present and unavoidable [3]. After retention time and response correction, the fingerprints can be used for quality control purpose by similarity evaluation with the correlation coefficient or the congruence coefficient [1]. On the other hand, one also can use principal component analysis (PCA) or other alternatives for unsupervised classification purpose. The principal component line depends upon the scaling of the data, and therefore a transformation procedure of the raw data is important for PCA analysis [9–13]. The examples of transformation are centering, normalization, standardization, selective normalization [10] and log centering. In this paper, firstly, the target peak alignment approach (TPA) is proposed as a new method to correct retention time shift present in hyphenated chromatographic data. Secondly multiplicative scattering correction (MSC) is introduced to reduce the variations in response. Finally, the effects of the different transformations in using principal component analysis on chromatographic fingerprints are discussed. The pretreatment of Houttuynia cordata (HC) data sets of three origins are utilized to demonstrate how to apply the proposed approach for the quality control of HMs. 2. Theory When the HM samples are measured by hyphenated instruments, such as GC/MS, HPLC/DAD, and CE/DAD, the twodimensional bilinear data obtained can be expressed as an absorbance matrix Xm×n . Here m is the number of measured spectra and n the number of variables (e.g., mass to charge ratios or wavelengths). The global/total fingerprint of the HM sample, by for instance GC/MS, can be obtained as the sum of the responses at each mass to charge ratio.
2.1. Retention time shift correction by target peak alignment (TPA) Retention time shift correction is an important requisite for quality control of HMs by multivariate analysis. Correcting retention time shifts in fingerprints would be easy if all peaks were identified in all of them. The main idea of the proposed TPA is to fit the unknown warp function [5] by identifying corresponding peaks in the two fingerprints. Recently, Gong et al. [14] proposed to use curve resolution methods [15–19] to resolve two-way data sets to identify the target components for retention time correction. However, resolution is often not necessary for identifying the target components and moreover it is not easy for a non-experienced user [20]. Meanwhile, Li et al. [21,22] proposed using the correlation coefficient of spectra to identify the target components for alignment. However, the correlation coefficient is often not good to evaluate the similarity of overlapped components. Therefore, we resort to use window target-testing factor analysis (WTTFA) [23,24] to identify the target components in the fingerprints. WTTFA is essentially a local target factor analysis (TFA) [25], and it tests where a specified component is present in the mixture. Suppose st is the obtained pure spectrum of the target component, X is the matrix of the fingerprint to align and Span (Xi ) represents the hyperplane spanned by the column vectors in the moving window matrix Xi (i = 1, 2, . . . , m − w + 1, where w is the size of the moving window and m is number of measured spectra). If the target component belongs to the subsystem in the moving window matrix Xi , the column vector st will exist in Span (Xi ). Therefore, one can implement TFA on local window matrices and then the length of the residue vector obtained by TFA is plotted in the time direction. +
rei = ||(I − XTi (XTi ) )st ||22
i = 1, 2, . . . , m − w + 1
(1)
here rei is defined as the length of the residue vector in the time direction, the || ||2 represents the 2-norm of the vector, the superscript + denotes the Moore-Penrose inverse of the matrix [26] and I designates the identity matrix. If the length of the residue vector nearly equals zero, the target component is present in the time window. After finding the target components and their retention times by WTTFA, the whole fingerprint can be divided into many sections. In each section, the time points in which target peaks achieve the highest intensity will be used for linear interpolation. Suppose a section having starting point at position xs and end point at position xe is warped to starting position xs and end position xe by calculating yj =
j (xe − xs ) + xs ; j = 0, . . . , xe − xs xe − xs
(2)
and then calculating the value of y (xs + j) by linear interpolation between the points in y adjacent to yj . Here y and y are the referenced fingerprint and the fingerprint under alignment, respectively. In this work, linear interpolation was used, but it is also possible to use non-linear interpolation (e.g. splines).
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num
2.2. Response correction by multiplicative signal correction (MSC)[27] In quality control of HMs by fingerprints, the fingerprints may be produced from different companies, persons, instruments and at different time etc. Therefore, a same GC response does not essentially represent same concentration of the constituent. The aim of response correction is to put the responses of different fingerprints into a same average zero-level, and that is the signals should have a same zero component response. It should be noted that response correction is a little different from conventional background correction. In background correction, one use baseline of that chromatogram to remove shift [28], and in response correction, one use a average baseline of different chromatogram to remove shift. Malmquist and Danielsson proposed a method [3] for response correction which is similar as multiplicative signal correction, but they use an iterative procedure to select data points which represent baseline regions. In this paper, we use MSC directly since an apparent zero-component (baseline) region can be determined easily for our cases. Here, MSC is used to separate the chemical responses from the baseline shift. The baseline shift can be estimated from zero-component region which means a part of the chromatogram which does not contain any elution components. The chromatogram in zero-component region is corrected to have the same level of baseline shift. The fit is achieved for each chromatogram (fingerprint) as follows. xzero,i = a + b¯xzero + ei
(3)
here xzero,i is the fingerprint in zero-component region of ith HM sample, x¯ zero and ei the mean fingerprint and the residual fingerprint in zero-component region, respectively. For each sample, the fitted constants a and b are estimated by ordinary least-squares regression. Then value of each fingerprint of the corrected one is calculated by: xj (MSC) =
xj − a , b
j = 1, 2, . . . , m
(4)
2.3. Similarity evaluation and principal component analysis (PCA) After retention time and response correction, the fingerprints of HMs can be used for quality control. The intuitive evaluation method is to compare the similarities and/or differences of the chromatographic fingerprints’ shapes [1]. The most commonly used methods to evaluate the similarity of the multivariate systems are the correlation coefficient, rcor , and the congruence coefficient, rcon, which are formulated respectively as follows: num ¯ )(yi − y¯ ) i=1 (xi − x rcor = (5) num 2 1/2 num 2 1/2 (x − x ¯ ) (y − y ¯ ) i i i=1 i=1 num i=1 xi yi (6) rcon = num 2 1/2 num 2 1/2 i=1 (xi ) i=1 (yi )
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num
¯= here x¯ = i=1 xi /n , y i=1 yi /n and num is the number of chromatogram studied. The two indices rcor and rcon will produce exactly the same result only when the fingerprints have been standardized. However, neither was shown to be always better than the other. To avoid the effect of outliers, one also can use a median fingerprint instead of a mean fingerprint for quality control purposes. Principal component analysis [30] is a conventionally used method for unsupervised classification of multivariate data. The selection of a transformation procedure of the raw data is important since the variable will determine its importance in the model [8]. In our opinions, that classification based on raw data often give too much weight to major variables, while those based on log centered data to minor variables. In view of this situation, we use different procedures to do transformation to observe the trends of clustering in PCA plot. 3. Experimental 3.1. Instrument GC-17A Gas Chromatograph, QP-5000 Mass Spectrometer (Shimadzu, Kyoto, Japan). 3.2. Materials Eighteen Houttuynia cordata samples were analyzed, five come from the Jiangxi province, seven from the Yunnan province and six from the Sichuan province. 3.3. Extraction of volatile oil The essential oil of the herb was prepared according to the standard extracting method described in the Chinese Pharmacopoeia [29]. 3.4. Separation of the volatile oil An OV-1 capillary column (30 m × 0.25 mm I.D., 0.25 m film thickness, J & W Scientific, California, USA) was used. Column temperature was maintained at 50 ◦ C for 6 min and programmed from 50–230 ◦ C at 25 ◦ C/min. Split injection was conducted with a split ratio of 1:10 and injection volume was 1 l. Inlet temperature is kept at 280 ◦ C. Helium carrier gas was used at a constant flow-rate of 0.7 ml/min. Detection was performed with a mass spectrometer (Shimadzu, Kyoto, Japan): electron impact (EI+ ) mass spectra were recorded at 70 eV ionization energy in full scan mode in the 30–350 amu mass range with 0.2 s/scan velocity. The ionization source temperature was set at 280 ◦ C. 3.5. Data analysis Data analysis was performed on a Pentium 4 personal computer. All programs for calculation were coded in MATLAB 6.5 for Windows (The Mathworks, Natick, MA).
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Fig. 1. The 18 fingerprints of HC from three origins.
4. Results and discussion 4.1. Retention time shift and response correction Fig. 1 illustrates the fingerprints of 18 the HC samples, five come from the Jiangxi, seven from the Yunnan and six from the Sichuan province. The fingerprints are constructed from their total ion current chromatogram. The HC samples were extracted immediately after collection by different researchers almost at the same time. With the presence of a large number of peaks, Fig. 1 tells that the volatile oil system to be studied is a complex analytical system. The fingerprint obtained from the third sample has been selected as the reference for later peak alignment procedure because some target peaks are missing in some fingerprints and the third one contains the most. Now, we will illustrate how to select a target component. Firstly, we select the peak between points 740–840 (2.47–2.80 min) as a possible target peak. The orthogonal projection approach (OPA) [19] has been used here for a possible peak purity detection approach. It should be noted that a pure peak is preferred but not necessary, since a small impurity will not affect our later treatment. The first two dissimilarity plots for OPA were shown in Fig. 2, and they are the plots showing the dissimilarity of the newly selected spectrum to the already selected ones versus measurements points obtained by OPA for the selected peak. The plot in Fig. 2a shows a profile seems there is at least one component in the system since significant peaks appeared cannot be regarded as a random profile. An unexpected concave curve on the top in Fig. 2a is coming from the scan effect [31] in GC–MS. The second plot in Fig. 2b shows a profile that can be considered as random, indicating that only one big component is present in the peak, and thus the peak is almost pure and can be selected as target peak. In our opinion, if one big peak is overlapped by a small impurity, one also can select a major part of the big peak as a possible target peak. After obtaining the pure mass spectrum from the pure region of the target component, one can implement WTTFA on the
Fig. 2. OPA dissimilarity plots for the target peak: (a) the first and (b) the second.
other fingerprints under alignment. WTTFA scans a series of moving time windows to find the target component in other fingerprints. To reduce computation time, it is not necessary to scan all time points to find the target component, and the scan time points are set as 740–840 for the first target mass spectrum. One can find whether the first target component is really present in this sample and determines when it elutes by observing the norm of the residue vector shown in Fig. 3. In implementing WTTFA, the window size should be defined firstly. Generally, the window size cannot be smaller than the number of maximal overlapped components in time direction. Otherwise, it may not adequately construct the subspace of all components in the time-window, which could yield a false negative result in target testing. On the other hand, too large a window size is also not preferred, since it will lose its accuracy in setting the boundaries of the concentration window because each window takes a large region of time into account. In our case, the window size is often selected as three or four. If the window size is three, one can scan the fingerprint in every three measure points. The window matrix is built as follows. Column 1–3
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Fig. 3. (a) Total ion current chromatogram of identified peak and (b) WTTFA plot of pure target component with window size three.
for window matrix 1, column 2–4 for window matrix 2, and so on. Having obtained the above elution information concerning the target component, the later alignment based on linear interpolation will be much easier. On the other hand, if the target component is absent in a sample, it will not be used for later alignment of that sample. After all possible target peaks are identified according to the above procedure, the chromatographic peak shifts can be corrected by performing the piecewise linear interpolation. The question how many targets are necessary arises when using TPA in practice. For linear shifting among chromatograms, two target components are usually enough for a good alignment. However, more target peaks are usually necessary to better fit a non-linear shift. Nevertheless, too many target peaks will cause overfitting, and also increase the computation efforts. In practice, one can use the major peak in each peak cluster as the target peaks, and later can use targets not included in the former alignment process to test whether the alignment is good enough until all possible target peaks have been adopted. In our opinions, we prefer to select fewer target peaks instead of more to avoid overfitting. In the implemented TPA, if different constituents have very similar spectra, TFA cannot discriminate between them. Thus, misalignment or peak mismatch may occur in such a collinearity situation. In such a case, one can use the similarity of chromatographic peaks as another criterion to ascertain the target peaks. It should be noted that the TPA method have been applied to two-way multi-channel data in our cases; however, it also can used in one-way mono-channel data if one use the similarity of chromatographic peaks instead of similarity of mass spectra as a criterion. It has been shown that the performance of TPA on PCA plot is similar as that of COW [32]. The next step after peak alignment is to do response correction. The region between measurement points 1200 to 2050 (time 4.00–6.83) is selected as zero component region with no component eluting out so as to determine the fitted constants a and b in
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Fig. 4. The 18 fingerprints of HC from three origins after retention time and response correction.
Eq. (3). The fingerprints after all corrections have been shown in Fig. 4. The assumption of response correction in this case is that all fingerprints have a same zero component response between points 1200 to 2050. It should be noted that MSC method can only be applied for linear baseline drift. For non-linear drift, one can try to use piecewise linear or other polynomial correction methods. 4.2. Similarity evaluation and unsupervised classification by principal component analysis (PCA) Fig. 4 shows the fingerprints after time and response correction. There are often two sources of variation contained in HMs data sets, one coming from the experimental process, and the other from the diversity of HMs. After correction of the time and response shifts, the variation arising from experimental process usually can be reduced. Table 1 lists the congruence coefficients rcon and correlation coefficients rcor of the samples compared with the mean spectrum of 18 samples before and after correction. After time and response correction, similarity among most fingerprints increases, tells us that the variation of experimental process has been reduced. In practice, the herbs which have the smallest similarity values, or are below than a defined value for example 0.7 can be regarded as not qualified. Principal component analysis of the kernel version [30,33] has been used to explore the clustering of the HC fingerprints. PCA is a popular method in applied statistical work and data analysis, and it has a good ability to summarize multivariate variation. The fingerprints are usually wide data matrices, characterized by a relatively small number of samples (a few ten to a few hundreds) and a very large number of variables (many hundreds, at least). Therefore, the kernel PCA is recommended to reduce computation burden. The kernel PCA yields the same principal components but, when facing with wide data, they perform much faster than the classic algorithm. The details of kernel PCA can be found in reference [33].
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Table 1 The Congruence coefficients, rcon , and correlation coefficients, rcor , of the samples compared with the mean spectrum of 18 samples before and after correction as mentioned in the text Sample no.
rcon Before
rcon After
rcor Before
rcor After
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0.6017 0.4011 0.4665 0.4264 0.3886 0.6513 0.6239 0.5798 0.3332 0.5044 0.5097 0.4596 0.5379 0.4012 0.6195 0.3740 0.6195 0.5598
0.7654 0.5559 0.7725 0.7425 0.5322 0.9373 0.8581 0.8461 0.6659 0.8527 0.7778 0.7466 0.3913 0.4318 0.8704 0.4920 0.5021 0.4604
0.5768 0.3834 0.4484 0.3896 0.3701 0.6300 0.5956 0.5526 0.2973 0.4788 0.4829 0.4341 0.5265 0.3806 0.5967 0.3539 0.6007 0.5517
0.7530 0.5462 0.7684 0.7302 0.5217 0.9345 0.8497 0.8382 0.6470 0.8477 0.7686 0.7377 0.3757 0.4165 0.8642 0.4786 0.4814 0.4484
By observing Fig. 5, it can be seen that it is difficult to discriminate the different groups. After correction in time and response, Fig. 6 gives us marginally better classification plot. Clearly, it is still difficult to divide the HC samples into three groups. The reason is that PCA based on raw data give too much weight to major peaks, and HC sample is difficult to separate mainly based on the major peaks. On the other hand, one can also use log column centered data which pay more attention to minor peaks, and then the clustering tendency by PCA can be improved. The PC plots shown in Figs. 7 and 8 reveal four clusters to the geographic origin of the samples. Only one sample of the Sichuan province is misclassified, and the samples from Yunnan have been divided into two subgroups. It should be noted that we do not have a sufficiently large data set in the current research, the obtained clustering tendency may have an overfitting problem and its prediction ability would be
Fig. 5. PCA plot (PC1-PC2) from original HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
Fig. 6. PCA plots (PC1-PC2) from the corrected HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
Fig. 7. PCA plots (PC1-PC2) from the log centered HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
Fig. 8. PCA plots (PC1-PC3) from the log centered HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
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limited. If many new samples are available in future, they will be pretreated in the same way as the old ones. Then one can do supervised discriminate analysis with testing it prediction performance. Moreover, other transformations such as centering, standardization, selective normalization and log double centering etc may be applicable for different cases. One can try to use them in the different situations. 5. Conclusion In this paper, a systematical pretreatment and evaluation procedure has been proposed, and it includes data correction, similarity evaluation, data transformation and unsupervised classification. The data corrections include retention time and response corrections can reduce the variations from the experimental procedure, are necessary for quality control of HMs. The data transformation can change the variations of the data sets, and it was combined with PCA could be helpful in the quality control of HM by fingerprints. The data transformation is a weighting scheme for model training and need more other samples to test prediction ability later to avoid overfitting. Acknowledgements The first author wishes to express his sincere thanks to Prof. D.L. Massart, Vrije Universiteit Brussels, for his valuable discussions. This research work is financially supported by a joint project of bilateral scientific and technological cooperation between with Flanders and China (BWS’ 02/06), the Area of Excellence grant by University Grants Council (UGC) of Hong Kong SAR (AoE/B-10/01) and the National natural science foundation of the People’s Republic of China (Grants No. 20235020). References [1] Y.Z. Liang, P.S. Xie, K. Chan, J. Chromatogr. B 812 (2004) 53. [2] WHO, General Guidelines for Methodologies on Research and Evaluation of Traditional Medicines, Geneva, 2000, p. 1.
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Pretreatments of chromatographic fingerprints for quality control of herbal medicines Cheng-Jian Xu a,∗ , Yi-Zeng Liang b , Foo-Tim Chau c , Yvan Vander Heyden a a
Department of Analytical Chemistry and Pharmaceutical Technology, VICIM Partner, Vrije Universiteit Brussel-VUB, Laarbeeklaan 103, B-1090, Brussels, Belgium b Research Center of Modernization of Chinese Medicines, College of Chemistry and Chemical Engineering, Central South University, 410083 Changsha, China c Chemometrics and Herbal Medicine Laboratory, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China Received 6 March 2006; received in revised form 17 August 2006; accepted 21 August 2006 Available online 7 September 2006
Abstract Pretreatments of chromatographic fingerprints are important for quality control of herbal medicines and they include data correction and data transformation. The data correction can reduce the variations of experimental procedures, and data transformation can put different weights on the different parts of the fingerprints. In this paper, a new target peak alignment (TPA) procedure has been proposed to correct the retention time shifts, multiplicative scattering correction (MSC) has been introduced for response correction. Then the similarity of the fingerprints with mean and median fingerprints is used to evaluate the quality of herbal medicines (HMs). Furthermore, different data transformation methods with kernel principal component analysis (PCA) have been applied to the data and their effects were discussed. The proposed approaches have been demonstrated by the essential oils data set of a herbal medicine, named Houttuynia cordata (HC), containing samples from different geographic origins. The experimental results indicate that the proposed approaches may be helpful in the quality control of herbal medicines by fingerprints. © 2006 Elsevier B.V. All rights reserved. Keywords: Herbal medicine (HM); Peak alignment; GC–MS; Quality control; Data pretreatment; Chromatographic fingerprint
1. Introduction Herbal medicines (HMs) have played an important role in the clinical therapy in many oriental countries for thousands of years. During the past decades, they attracted more and more the attention in western countries because of their high pharmacological activity with low toxicity and rare complications. However, HMs have not been officially recognized world-wide yet because the quantity and quality of the safety and efficacy data are far from sufficient to meet the general quality criteria [1]. Therefore, the fingerprint analysis methods have been introduced and accepted recently by the World Health Organization ∗
Corresponding author. Present address: Chemometrics and Herbal Medicine Laboratory, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. Tel.: +852 2766 5585; fax: +852 2364 9932. E-mail address: [email protected] (C.-J. Xu). 0021-9673/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2006.08.060
(WHO) as a strategy for the assessment of the quality of HMs [2]. A fingerprint of a HM is a chromatogram (or electropherogram) here representing all the detectable chemical components present in the extract and being separated as much as possible so as to identify and characterize that HM. With the help of the fingerprint, the authentication and identification of a HM can be reliably conducted even if the quality and quantity of the constituents are unknown. Thus, for evaluating the quality of a HM, one can consider multiple constituents in the HM extracts, instead of only one or two marker components for evaluating the quality in the conventional approach. When one deals with chromatographic fingerprints of HMs from different sources, one can do similarity evaluation or pattern recognition for quality control purposes. Therefore, to meet the requisite that the same variable contains the same kind of information for multivariate analysis, the data corrections,
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including retention time and response correction, are usually necessary [3]. Many approaches have been proposed for retention time correction [3–8], such as correlation optimized warping (COW) [4], dynamic time warping (DTW) [5] and parametric time warping (PTW) [6]. Among them, the COW method is considered very often as the most effective one [7]. However, optimizing the input parameters of COW is time consuming and therefore applicability of this method for complex herbal medicine systems is rather tedious. Furthermore, all the above warping methods [3–8] do not make full use of the spectral information from the data sets with multi-channel detectors. The popular use of hyphenated chromatographic instruments, such as HPLC/DAD (high performance liquid chromatography/diode array detector) and GC–MS (gas chromatography/mass spectrometry) provide spectral information besides chromatograms. If such information can be handled properly and efficiently, the correction of retention time shift will be more reliable. A fingerprint of a HM is essentially representing relative concentrations of all the components which are detectable in the extract, and their relative concentrations are proportional to their chromatographic responses. However, a same response in different experiments does not stand for same concentration because experimental variations are present and unavoidable [3]. After retention time and response correction, the fingerprints can be used for quality control purpose by similarity evaluation with the correlation coefficient or the congruence coefficient [1]. On the other hand, one also can use principal component analysis (PCA) or other alternatives for unsupervised classification purpose. The principal component line depends upon the scaling of the data, and therefore a transformation procedure of the raw data is important for PCA analysis [9–13]. The examples of transformation are centering, normalization, standardization, selective normalization [10] and log centering. In this paper, firstly, the target peak alignment approach (TPA) is proposed as a new method to correct retention time shift present in hyphenated chromatographic data. Secondly multiplicative scattering correction (MSC) is introduced to reduce the variations in response. Finally, the effects of the different transformations in using principal component analysis on chromatographic fingerprints are discussed. The pretreatment of Houttuynia cordata (HC) data sets of three origins are utilized to demonstrate how to apply the proposed approach for the quality control of HMs. 2. Theory When the HM samples are measured by hyphenated instruments, such as GC/MS, HPLC/DAD, and CE/DAD, the twodimensional bilinear data obtained can be expressed as an absorbance matrix Xm×n . Here m is the number of measured spectra and n the number of variables (e.g., mass to charge ratios or wavelengths). The global/total fingerprint of the HM sample, by for instance GC/MS, can be obtained as the sum of the responses at each mass to charge ratio.
2.1. Retention time shift correction by target peak alignment (TPA) Retention time shift correction is an important requisite for quality control of HMs by multivariate analysis. Correcting retention time shifts in fingerprints would be easy if all peaks were identified in all of them. The main idea of the proposed TPA is to fit the unknown warp function [5] by identifying corresponding peaks in the two fingerprints. Recently, Gong et al. [14] proposed to use curve resolution methods [15–19] to resolve two-way data sets to identify the target components for retention time correction. However, resolution is often not necessary for identifying the target components and moreover it is not easy for a non-experienced user [20]. Meanwhile, Li et al. [21,22] proposed using the correlation coefficient of spectra to identify the target components for alignment. However, the correlation coefficient is often not good to evaluate the similarity of overlapped components. Therefore, we resort to use window target-testing factor analysis (WTTFA) [23,24] to identify the target components in the fingerprints. WTTFA is essentially a local target factor analysis (TFA) [25], and it tests where a specified component is present in the mixture. Suppose st is the obtained pure spectrum of the target component, X is the matrix of the fingerprint to align and Span (Xi ) represents the hyperplane spanned by the column vectors in the moving window matrix Xi (i = 1, 2, . . . , m − w + 1, where w is the size of the moving window and m is number of measured spectra). If the target component belongs to the subsystem in the moving window matrix Xi , the column vector st will exist in Span (Xi ). Therefore, one can implement TFA on local window matrices and then the length of the residue vector obtained by TFA is plotted in the time direction. +
rei = ||(I − XTi (XTi ) )st ||22
i = 1, 2, . . . , m − w + 1
(1)
here rei is defined as the length of the residue vector in the time direction, the || ||2 represents the 2-norm of the vector, the superscript + denotes the Moore-Penrose inverse of the matrix [26] and I designates the identity matrix. If the length of the residue vector nearly equals zero, the target component is present in the time window. After finding the target components and their retention times by WTTFA, the whole fingerprint can be divided into many sections. In each section, the time points in which target peaks achieve the highest intensity will be used for linear interpolation. Suppose a section having starting point at position xs and end point at position xe is warped to starting position xs and end position xe by calculating yj =
j (xe − xs ) + xs ; j = 0, . . . , xe − xs xe − xs
(2)
and then calculating the value of y (xs + j) by linear interpolation between the points in y adjacent to yj . Here y and y are the referenced fingerprint and the fingerprint under alignment, respectively. In this work, linear interpolation was used, but it is also possible to use non-linear interpolation (e.g. splines).
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num
2.2. Response correction by multiplicative signal correction (MSC)[27] In quality control of HMs by fingerprints, the fingerprints may be produced from different companies, persons, instruments and at different time etc. Therefore, a same GC response does not essentially represent same concentration of the constituent. The aim of response correction is to put the responses of different fingerprints into a same average zero-level, and that is the signals should have a same zero component response. It should be noted that response correction is a little different from conventional background correction. In background correction, one use baseline of that chromatogram to remove shift [28], and in response correction, one use a average baseline of different chromatogram to remove shift. Malmquist and Danielsson proposed a method [3] for response correction which is similar as multiplicative signal correction, but they use an iterative procedure to select data points which represent baseline regions. In this paper, we use MSC directly since an apparent zero-component (baseline) region can be determined easily for our cases. Here, MSC is used to separate the chemical responses from the baseline shift. The baseline shift can be estimated from zero-component region which means a part of the chromatogram which does not contain any elution components. The chromatogram in zero-component region is corrected to have the same level of baseline shift. The fit is achieved for each chromatogram (fingerprint) as follows. xzero,i = a + b¯xzero + ei
(3)
here xzero,i is the fingerprint in zero-component region of ith HM sample, x¯ zero and ei the mean fingerprint and the residual fingerprint in zero-component region, respectively. For each sample, the fitted constants a and b are estimated by ordinary least-squares regression. Then value of each fingerprint of the corrected one is calculated by: xj (MSC) =
xj − a , b
j = 1, 2, . . . , m
(4)
2.3. Similarity evaluation and principal component analysis (PCA) After retention time and response correction, the fingerprints of HMs can be used for quality control. The intuitive evaluation method is to compare the similarities and/or differences of the chromatographic fingerprints’ shapes [1]. The most commonly used methods to evaluate the similarity of the multivariate systems are the correlation coefficient, rcor , and the congruence coefficient, rcon, which are formulated respectively as follows: num ¯ )(yi − y¯ ) i=1 (xi − x rcor = (5) num 2 1/2 num 2 1/2 (x − x ¯ ) (y − y ¯ ) i i i=1 i=1 num i=1 xi yi (6) rcon = num 2 1/2 num 2 1/2 i=1 (xi ) i=1 (yi )
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num
¯= here x¯ = i=1 xi /n , y i=1 yi /n and num is the number of chromatogram studied. The two indices rcor and rcon will produce exactly the same result only when the fingerprints have been standardized. However, neither was shown to be always better than the other. To avoid the effect of outliers, one also can use a median fingerprint instead of a mean fingerprint for quality control purposes. Principal component analysis [30] is a conventionally used method for unsupervised classification of multivariate data. The selection of a transformation procedure of the raw data is important since the variable will determine its importance in the model [8]. In our opinions, that classification based on raw data often give too much weight to major variables, while those based on log centered data to minor variables. In view of this situation, we use different procedures to do transformation to observe the trends of clustering in PCA plot. 3. Experimental 3.1. Instrument GC-17A Gas Chromatograph, QP-5000 Mass Spectrometer (Shimadzu, Kyoto, Japan). 3.2. Materials Eighteen Houttuynia cordata samples were analyzed, five come from the Jiangxi province, seven from the Yunnan province and six from the Sichuan province. 3.3. Extraction of volatile oil The essential oil of the herb was prepared according to the standard extracting method described in the Chinese Pharmacopoeia [29]. 3.4. Separation of the volatile oil An OV-1 capillary column (30 m × 0.25 mm I.D., 0.25 m film thickness, J & W Scientific, California, USA) was used. Column temperature was maintained at 50 ◦ C for 6 min and programmed from 50–230 ◦ C at 25 ◦ C/min. Split injection was conducted with a split ratio of 1:10 and injection volume was 1 l. Inlet temperature is kept at 280 ◦ C. Helium carrier gas was used at a constant flow-rate of 0.7 ml/min. Detection was performed with a mass spectrometer (Shimadzu, Kyoto, Japan): electron impact (EI+ ) mass spectra were recorded at 70 eV ionization energy in full scan mode in the 30–350 amu mass range with 0.2 s/scan velocity. The ionization source temperature was set at 280 ◦ C. 3.5. Data analysis Data analysis was performed on a Pentium 4 personal computer. All programs for calculation were coded in MATLAB 6.5 for Windows (The Mathworks, Natick, MA).
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Fig. 1. The 18 fingerprints of HC from three origins.
4. Results and discussion 4.1. Retention time shift and response correction Fig. 1 illustrates the fingerprints of 18 the HC samples, five come from the Jiangxi, seven from the Yunnan and six from the Sichuan province. The fingerprints are constructed from their total ion current chromatogram. The HC samples were extracted immediately after collection by different researchers almost at the same time. With the presence of a large number of peaks, Fig. 1 tells that the volatile oil system to be studied is a complex analytical system. The fingerprint obtained from the third sample has been selected as the reference for later peak alignment procedure because some target peaks are missing in some fingerprints and the third one contains the most. Now, we will illustrate how to select a target component. Firstly, we select the peak between points 740–840 (2.47–2.80 min) as a possible target peak. The orthogonal projection approach (OPA) [19] has been used here for a possible peak purity detection approach. It should be noted that a pure peak is preferred but not necessary, since a small impurity will not affect our later treatment. The first two dissimilarity plots for OPA were shown in Fig. 2, and they are the plots showing the dissimilarity of the newly selected spectrum to the already selected ones versus measurements points obtained by OPA for the selected peak. The plot in Fig. 2a shows a profile seems there is at least one component in the system since significant peaks appeared cannot be regarded as a random profile. An unexpected concave curve on the top in Fig. 2a is coming from the scan effect [31] in GC–MS. The second plot in Fig. 2b shows a profile that can be considered as random, indicating that only one big component is present in the peak, and thus the peak is almost pure and can be selected as target peak. In our opinion, if one big peak is overlapped by a small impurity, one also can select a major part of the big peak as a possible target peak. After obtaining the pure mass spectrum from the pure region of the target component, one can implement WTTFA on the
Fig. 2. OPA dissimilarity plots for the target peak: (a) the first and (b) the second.
other fingerprints under alignment. WTTFA scans a series of moving time windows to find the target component in other fingerprints. To reduce computation time, it is not necessary to scan all time points to find the target component, and the scan time points are set as 740–840 for the first target mass spectrum. One can find whether the first target component is really present in this sample and determines when it elutes by observing the norm of the residue vector shown in Fig. 3. In implementing WTTFA, the window size should be defined firstly. Generally, the window size cannot be smaller than the number of maximal overlapped components in time direction. Otherwise, it may not adequately construct the subspace of all components in the time-window, which could yield a false negative result in target testing. On the other hand, too large a window size is also not preferred, since it will lose its accuracy in setting the boundaries of the concentration window because each window takes a large region of time into account. In our case, the window size is often selected as three or four. If the window size is three, one can scan the fingerprint in every three measure points. The window matrix is built as follows. Column 1–3
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Fig. 3. (a) Total ion current chromatogram of identified peak and (b) WTTFA plot of pure target component with window size three.
for window matrix 1, column 2–4 for window matrix 2, and so on. Having obtained the above elution information concerning the target component, the later alignment based on linear interpolation will be much easier. On the other hand, if the target component is absent in a sample, it will not be used for later alignment of that sample. After all possible target peaks are identified according to the above procedure, the chromatographic peak shifts can be corrected by performing the piecewise linear interpolation. The question how many targets are necessary arises when using TPA in practice. For linear shifting among chromatograms, two target components are usually enough for a good alignment. However, more target peaks are usually necessary to better fit a non-linear shift. Nevertheless, too many target peaks will cause overfitting, and also increase the computation efforts. In practice, one can use the major peak in each peak cluster as the target peaks, and later can use targets not included in the former alignment process to test whether the alignment is good enough until all possible target peaks have been adopted. In our opinions, we prefer to select fewer target peaks instead of more to avoid overfitting. In the implemented TPA, if different constituents have very similar spectra, TFA cannot discriminate between them. Thus, misalignment or peak mismatch may occur in such a collinearity situation. In such a case, one can use the similarity of chromatographic peaks as another criterion to ascertain the target peaks. It should be noted that the TPA method have been applied to two-way multi-channel data in our cases; however, it also can used in one-way mono-channel data if one use the similarity of chromatographic peaks instead of similarity of mass spectra as a criterion. It has been shown that the performance of TPA on PCA plot is similar as that of COW [32]. The next step after peak alignment is to do response correction. The region between measurement points 1200 to 2050 (time 4.00–6.83) is selected as zero component region with no component eluting out so as to determine the fitted constants a and b in
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Fig. 4. The 18 fingerprints of HC from three origins after retention time and response correction.
Eq. (3). The fingerprints after all corrections have been shown in Fig. 4. The assumption of response correction in this case is that all fingerprints have a same zero component response between points 1200 to 2050. It should be noted that MSC method can only be applied for linear baseline drift. For non-linear drift, one can try to use piecewise linear or other polynomial correction methods. 4.2. Similarity evaluation and unsupervised classification by principal component analysis (PCA) Fig. 4 shows the fingerprints after time and response correction. There are often two sources of variation contained in HMs data sets, one coming from the experimental process, and the other from the diversity of HMs. After correction of the time and response shifts, the variation arising from experimental process usually can be reduced. Table 1 lists the congruence coefficients rcon and correlation coefficients rcor of the samples compared with the mean spectrum of 18 samples before and after correction. After time and response correction, similarity among most fingerprints increases, tells us that the variation of experimental process has been reduced. In practice, the herbs which have the smallest similarity values, or are below than a defined value for example 0.7 can be regarded as not qualified. Principal component analysis of the kernel version [30,33] has been used to explore the clustering of the HC fingerprints. PCA is a popular method in applied statistical work and data analysis, and it has a good ability to summarize multivariate variation. The fingerprints are usually wide data matrices, characterized by a relatively small number of samples (a few ten to a few hundreds) and a very large number of variables (many hundreds, at least). Therefore, the kernel PCA is recommended to reduce computation burden. The kernel PCA yields the same principal components but, when facing with wide data, they perform much faster than the classic algorithm. The details of kernel PCA can be found in reference [33].
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Table 1 The Congruence coefficients, rcon , and correlation coefficients, rcor , of the samples compared with the mean spectrum of 18 samples before and after correction as mentioned in the text Sample no.
rcon Before
rcon After
rcor Before
rcor After
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0.6017 0.4011 0.4665 0.4264 0.3886 0.6513 0.6239 0.5798 0.3332 0.5044 0.5097 0.4596 0.5379 0.4012 0.6195 0.3740 0.6195 0.5598
0.7654 0.5559 0.7725 0.7425 0.5322 0.9373 0.8581 0.8461 0.6659 0.8527 0.7778 0.7466 0.3913 0.4318 0.8704 0.4920 0.5021 0.4604
0.5768 0.3834 0.4484 0.3896 0.3701 0.6300 0.5956 0.5526 0.2973 0.4788 0.4829 0.4341 0.5265 0.3806 0.5967 0.3539 0.6007 0.5517
0.7530 0.5462 0.7684 0.7302 0.5217 0.9345 0.8497 0.8382 0.6470 0.8477 0.7686 0.7377 0.3757 0.4165 0.8642 0.4786 0.4814 0.4484
By observing Fig. 5, it can be seen that it is difficult to discriminate the different groups. After correction in time and response, Fig. 6 gives us marginally better classification plot. Clearly, it is still difficult to divide the HC samples into three groups. The reason is that PCA based on raw data give too much weight to major peaks, and HC sample is difficult to separate mainly based on the major peaks. On the other hand, one can also use log column centered data which pay more attention to minor peaks, and then the clustering tendency by PCA can be improved. The PC plots shown in Figs. 7 and 8 reveal four clusters to the geographic origin of the samples. Only one sample of the Sichuan province is misclassified, and the samples from Yunnan have been divided into two subgroups. It should be noted that we do not have a sufficiently large data set in the current research, the obtained clustering tendency may have an overfitting problem and its prediction ability would be
Fig. 5. PCA plot (PC1-PC2) from original HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
Fig. 6. PCA plots (PC1-PC2) from the corrected HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
Fig. 7. PCA plots (PC1-PC2) from the log centered HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
Fig. 8. PCA plots (PC1-PC3) from the log centered HC fingerprints: “O” Jiangxi, “*” Yunnan and “+” Sichuan.
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limited. If many new samples are available in future, they will be pretreated in the same way as the old ones. Then one can do supervised discriminate analysis with testing it prediction performance. Moreover, other transformations such as centering, standardization, selective normalization and log double centering etc may be applicable for different cases. One can try to use them in the different situations. 5. Conclusion In this paper, a systematical pretreatment and evaluation procedure has been proposed, and it includes data correction, similarity evaluation, data transformation and unsupervised classification. The data corrections include retention time and response corrections can reduce the variations from the experimental procedure, are necessary for quality control of HMs. The data transformation can change the variations of the data sets, and it was combined with PCA could be helpful in the quality control of HM by fingerprints. The data transformation is a weighting scheme for model training and need more other samples to test prediction ability later to avoid overfitting. Acknowledgements The first author wishes to express his sincere thanks to Prof. D.L. Massart, Vrije Universiteit Brussels, for his valuable discussions. This research work is financially supported by a joint project of bilateral scientific and technological cooperation between with Flanders and China (BWS’ 02/06), the Area of Excellence grant by University Grants Council (UGC) of Hong Kong SAR (AoE/B-10/01) and the National natural science foundation of the People’s Republic of China (Grants No. 20235020). References [1] Y.Z. Liang, P.S. Xie, K. Chan, J. Chromatogr. B 812 (2004) 53. [2] WHO, General Guidelines for Methodologies on Research and Evaluation of Traditional Medicines, Geneva, 2000, p. 1.
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