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Art%3 a10.1007%2fs12161 013-9618-4
1. A Rapid UPLC-PAD Fingerprint Analysis of Chrysanthemum
morifolium Ramat Combined with Chemometrics Methods
Xianrui Liang & Hong Wu & Weike Su
Received: 9 January 2013 /Accepted: 2 April 2013
# Springer Science+Business Media New York 2013
Abstract A novel approach for fingerprint analysis of
Chrysanthemum morifolium Ramat was developed by com-
bining chemometrics methods such as similarity analysis,
hierarchical cluster analysis, and principal component anal-
ysis with ultra-performance liquid chromatography. The
chromatographic separation was accomplished by gradient
elution with mobile phase consisting of acetonitrile and
phosphate buffer in 10 min prior to conventional liquid
chromatography which usually takes over 1 h. Good repro-
ducibility, precision, and accuracy were obtained with rela-
tive standard deviations below 2.10 % of six typical
components. Consistent results were obtained in accordance
with their cultivated sources. The proposed method above
was confirmed to be more rapid and suitable, thus providing
important criteria for further quality control of C.
morifolium Ramat.
Keywords Chrysanthemum morifolium Ramat . UPLC .
Fingerprint . Chemometric
Introduction
Cultivated for over 3,000 years, the anthotaxy of
Chrysanthemum morifolium Ramat played an important role
in traditional Chinese medicine (TCM) and was widely used
for food supplement as well as herb tea (Pharmacopoeia of
People’s Republic of China 2010). Significant amounts of bio-
logically active components such as flavonoids and
hydroxycinnamoyl-quinic acids found in chrysanthemum
flower possessed heat-clearing, detoxication, antiviral, antioxi-
dant, antibacterial, and anti-inflammatory activities (Selected
Words of Chinese Bencao 1999). Recent studies also found that
it has crucial effects on anti-hyperlipidemia (Jiang et al. 2005b),
cardiovascular resistance (Jiang et al. 2004; Chen et al. 2007),
anti-tumor (Jin et al. 2005; Shi et al. 2005; Singh et al. 2005; Tsai
et al. 2011; King et al. 1999), and anti-HIV (Critchfield et al.
1996; Lee et al. 2003).
Ultra-performance liquid chromatography (UPLC),
equipped with sub-2 μm particle short column, is a newly
developed technique since 2004 (Nguyen et al. 2006). The
short retention time, good chromatographic resolution, high
sensitivity, and less solvent consumption make it more and
more popular, especially for studying natural products and
food (Li et al. 2012; Tang et al. 2012; Kong et al. 2011).
Though many studies about CM have been released, most of
them focused on its components study using time-consuming
conventional HPLC method (Lai et al. 2007; Lin and Harnly
2010; Clifford et al. 2007). Among these, chlorogenic acid
was designated as one of the most significant standards for
quality control of CM (Pharmacopoeia of People’s Republic
of China 2010; Cheng et al. 2007; Shen et al. 2010). In this
work, UPLC was applied for discriminating CM from differ-
ent cultivated regions by combining with chemometrics
methods.
Experimental
Materials and Reagents
Twenty batches of raw CM samples were collected from
five main cultivation areas located in China, which were
listed in Table 1. For the chemical fingerprint analysis, the
samples were dried at 50 °C in an oven until constant weight
was reached. Standard chlorogenic acid (purity >99.0 %;
X. Liang :H. Wu :W. Su (*)
Key Laboratory for Green Pharmaceutical Technologies and
Related Equipment of Ministry of Education, College of
Pharmaceutical Sciences, Zhejiang University of Technology,
Hangzhou 310014, China
e-mail: pharmlab@zjut.edu.cn
Food Anal. Methods
DOI 10.1007/s12161-013-9618-4
2. CA) was purchased from National Institute for the Control
of Pharmaceutical and Biological Products (Beijing, China).
HPLC grade acetonitrile and methanol were purchased from
Merck (Darmstadt, Germany). All other analytical grade
chemicals in this paper were purchased from Yongda
Chemical Reagent Co. (Tianjin, China). Ultrapure water
was obtained by Barnstead TII super Pure Water System
(MA, USA).
Ultra-performance Liquid Chromatography
UPLC was performed by using a Waters Acquity system
equipped with binary solvent delivery pump, auto sampler,
and photodiode array detector, which is connected to Waters
Empower software. The chromatographic separation was
performed using a Waters Acquity BEH C18 column, 50×
2.1 mm, 1.7 μm. The mobile phase was component A
(25 mM KH2PO4–H3PO4, pH 4.5) and B (acetonitrile) in
gradient mode as follows: 0–3 min, 8–15 % B; 3–6.5 min,
15–19 % B; 6.5–8 min, 19–20 % B; 8–8.5 min, 20–40 % B;
8.5–9 min, 40–60 % B; 9–10 min, 60 % B; the total flow
rate was 0.2 mL/min. The partial loop with needle overfill
mode was set up to inject 5 μL when the column was
maintained at 30 °C. The detector wavelength was set at
328 nm.
Standard Preparation
The standard stock solution of CA (400.0 μg/mL) was pre-
pared by dissolving appropriate amount in methanol/water
(50/50, v/v). It was stored in dark volumetric flask at 4 °C.
For the standard calibration curve, it was diluted by water to
different concentrations from 0.1 to 20 μg/mL.
Sample Solution Preparation
Powdered dried material (2.0 g) was mixed with 50 mL of
solution ethanol/water (50/50, v/v) using a 100 mL conical
flask. After settling for 12 h, it was extracted by ultrasonication
at 25 °C for 30 min. The extracted solution was prepared by the
method of weight relief by which the weight lost in the extrac-
tion procedure was compensated. One milliliter of supernatant
fluid was diluted into 10 mL volumetric flask by water. They
were all filtrated through 0.22 μm PTFE filter before injection.
Validation of UPLC Method
To evaluate validation of the method, the precision test (pt),
reproducibility test (rt), stability test (st), and linearity exper-
iments were performed on S6. The precision was evaluated by
six repeated runs within a single day. The reproducibility was
measured by running six replicate samples S6 prepared inde-
pendently in a single day. Solution stability was determined by
running five times of the same sample in the same day (every
2 h). The relative standard deviations (RSD) of relative reten-
tion time (RRT) and relative peak area (RPA) of six typical
components of each test were calculated. Linearity test was
analyzed by injecting 10 concentrations and the regression
equation was deduced. The limit of detection (LOD) and limit
of quantity (LOQ) for CAwere estimated by injecting a series
of diluted solution at known concentration to meet the signal-
to-noise (S/N) equal to 3 and 10, respectively.
Data Analysis
Data analysis was accomplished through three steps. SA
was performed by Similarity Evaluation System for
Chromatographic Fingerprint of Traditional Chinese Medicine
composed by Chinese Pharmacopoeia Committee (Version
2004A), which was recommended by State Food and Drug
Administration (SFDA) of China (Kong et al. 2011). Ten batches
of typical CM were picked out to create the mean chromatogra-
phy as a representative standard fingerprint chromatogram. Then
the similarity among 20 samples and the simulative mean chro-
matography were calculated by this software.
Based on the RPA of common peaks, hierarchical clus-
tering analysis (HCA) was performed using SPSS17.0 soft-
ware (USA) (Li et al. 2010). In this paper the squared
Table 1 Raw samples of Chry-
santhemum morifolium Ramat in
this study
Sample no. Sources Batch no. Sample no. Sources Batch no.
S1 Tongxiang, Zhejiang 120209 S11 Ruicheng, Shanxi 120216
S2 Tongxiang, Zhejiang 120313 S12 Huangshan, Anhui 111128
S3 Tongxiang, Zhejiang 20111224 S13 Huangshan, Anhui 110427
S4 Tongxiang, Zhejiang 20120307 S14 Futian, Hubei 120104
S5 Tongxiang, Zhejiang 120618 S15 Futian, Hubei 120323
S6 Tongxiang, Zhejiang 111125 S16 Yancheng, Jiangsu 20111208
S7 Tongxiang, Zhejiang 20111202 S17 Yancheng, Jiangsu 20111101
S8 Tongxiang, Zhejiang 20111217 S18 Yancheng, Jiangsu 20101124
S9 Tongxiang, Zhejiang 20120413 S19 Bozhou, Anhui 120521
S10 Tongxiang, Zhejiang 101116 S20 Chuzhou, Anhui 120408
Food Anal. Methods
3. Euclidean distance was chosen as the measure of similarity,
and the Between-groups Linkage method was applied for
the clustering algorithm.
Principle component analysis (PCA) was also performed
based on the RPA of common characteristic peaks by SPSS
17.0 software.
Results and Discussion
Optimization of UPLC Condition
To achieve the baseline separation of main components of
CM, chromatographic conditions such as the mobile phase,
gradient elution condition, flow rate, column temperature, and
detection wavelength were investigated. In this paper, aceto-
nitrile was chosen as the organic elution phase because of its
greater elution effect than methanol. Considering that buffer
solution was the most important factor on the separation, a
range of pH from 2 to 8 were investigated. Typical buffer
solution used to reach the target pH such as K2HPO4–H3PO4,
KH2PO4–H3PO4, CH3COONH4–CH3COOH, H3PO4, and
CH3COOH were prepared. Ultimately 25 mM KH2PO4 solu-
tion (adjusted to pH 4.5 using H3PO4) was demonstrated to be
the most appropriate buffer system according to the peak
profile and resolution. The gradient composition and run time
were also evaluated and gradient program of 8–15 % B in 0–
3 min, 15–19 % B in 3–6.5 min, 19–20 % B in 6.5–8 min, 20–
40 % B in 8–8.5 min, 40–60 % B in 8.5–9 min, and 60 % B in
9–10 min were applied. To improve the chromatographic per-
formance, the influence of column temperature was investigat-
ed from 25–35 °C at the flow rate of 0.2 mL/min, and the flow
rate from 0.2 to 0.4 mL/min was examined with the column
temperature maintained at 30 °C. Changing column tempera-
ture made no obvious difference to the separation while higher
flow rate led to co-elution between 6.8 and 8.0 min. Taking the
system backpressure into account the column temperature at 30
°C and the flow rate at 0.2 mL/min were preferred.
Additionally, in order to acquire the optimal absorption of all
the detected peaks and the lowest baseline noise, a wavelength
ranging from 190 to 400 nm was scanned by PDA detector and
the wavelength of 328 nm was chosen at last. With above
optimized chromatographic conditions a satisfactory separation
was accomplished in 10 min, which was much shorter than 1 h
reported in literatures (Cheng et al. 2007; Jiang et al. 2005a).
Optimization of Extraction Conditions
In order to obtain the greatest extraction efficiency, factors
such as extraction solvent, temperature, extracting time, and
processing were investigated. The peak area of peaks 1–6
with R>1 (R, resolution) of S6 were chosen as references for
optimizing the extraction efficiency and the maximum UV
wavelengths of them were labeled in Table 2. Methanol,
ethanol, acetonitrile, acetone, ethyl acetate, chloroform, and
water were used as extraction solvents. Almost nothing was
obtained when 100 % ethyl acetate or 100 % chloroform
was applied. Comparing to methanol–water, acetonitrile–
water, and acetone–water systems, ethanol–water system
suggested better results. Different ratios of ethanol–water
Table 2 The maximum
UV wavelengths of the
peak 1–6 to assess peak
similarity
Peak no. UV (λmax)/nm
1 218,326
2 218,328
3 218,241,328
4 205,255,349
5 198,267,338
6 206,268,328
Table 3 Results of RRT and RPA of reproducibility test, precision test, stability test on UPLC fingerprint of the Chrysanthemum morifolium Ramat
from different sources of China
Peak Relative retention time Relative peak area
No. Mean (RSD%) Mean (RSD%)
Rpt Rrt Rst Rpt Rrt Rst
1 1.000 (0.54) 1.000 (0.47) 1.000 (0.91) 1.000 (0.22) 1.000 (0.87) 1.000 (0.58)
2 2.163 (0.95) 2.163 (0.47) 2.152 (0.96) 0.295 (0.27) 0.310 (1.29) 0.294 (0.68)
3 3.334 (0.37) 3.323 (0.16) 3.312 (0.35) 1.367 (0.15) 1.360 (1.39) 1.365 (0.64)
4 4.561 (0.30) 4.539 (0.26) 4.546 (1.25) 0.813 (0.33) 0.816 (1.00) 0.808 (1.17)
5 5.272 (0.21) 5.254 (0.16) 5.246 (0.81) 1.998 (0.28) 2.010 (1.29) 1.991 (1.02)
6 6.746 (0.13) 6.725 (0.09) 6.691 (0.16) 0.036 (0.83) 0.035 (2.10) 0.036 (1.71)
Rpt (n=6), Rrt (n=6), and Rst (n=5: determine the same sample solution at 0, second, fourth, sixth, and eighth hour after it was prepared) represent
the results of relative retention time and relative peak area of precision test, reproducibility test, stability test on UPLC fingerprint of the
Chrysanthemum morifolium Ramat from different sources of China, respectively
Food Anal. Methods
4. at 0, 30, 40, 50, 60, 70, and 100 % as extraction solution
were applied and 50 % ethanol was employed for extraction
in all the subsequent studies. Temperatures, extracting time,
and heating mode were also evaluated and a proper temper-
ature at 25 °C and an extraction time of 30 min by ultrasonic
extraction were applied.
UPLC Method Validation
The results of UPLC method validation were summa-
rized in Table 3. It was indicated that the RSD of RRT
and RPA of precision test were below 0.95 and 0.83 %,
respectively.
The RSD of RRT and RPA of reproducibility test were
not exceeding 0.47 and 2.10 %, respectively. And the RSD
of RRT and RPA of stability test were no more than 1.25 and
1.71 %, respectively.
The regression equation R2
of linear calibration curve
was 0.9993, which showed excellent correlation between
the peak area and concentration. And the LOD for CA was
found to be 0.02 μg/mL, with RSD 3.78 %. The LOQ was
0.1 μg/mL, with RSD 2.02 %.
Fig. 1 Chromatograms of 20 batches of C. morifolium Ramat samples
Fig. 2 Mean chromatography of S1–S10 established by similarity evaluation system for chromatographic fingerprint of TCM and six typical
components of it
Food Anal. Methods
6. UPLC Fingerprint Analysis of C. morifolium Ramat
Similarity Analysis
The similarity of 20 batches of CM samples from various
sources was reliably evaluated by calculating the correlative
coefficient of original data. Chromatograms of 20 batches of
CM samples were shown in Fig. 1. S1–S10 from Tongxiang,
Zhejiang, 10 of qualified samples, were chosen to establish
a mean chromatogram shown in Fig. 2, and 25 common
peaks were signed out. All the samples show high similarity
in retention time while their peak abundances are different.
The correlative coefficient (r) between each sample and the
corresponding mean chromatogram was listed in Table 4.
It suggested that samples from Tongxiang, Zhejiang, and
Ruicheng, Shanxi had high similarity with r>0.9, while S12
(0.888) and S13 (0.878) from Huangshan, Anhui harvested in
different years showed less differences. It is considered that two
samples are much different when r<0.8 (Xu et al. 2006; Lu et
al. 2006). The correlative coefficient of the rest of samples was
less than 0.8, indicating the different internal quality from S1–
S13. S14 was less alike (r<0.8) with S1–S13, but more similar
to S15–S20 (r>0.8). The results were in accordance with the
actual patterns and peak shapes of the chromatograms.
Hierarchical Clustering Analysis
To further assess the quality characteristics including the
resemblance and differences of these samples, a hierarchical
agglomerative clustering analysis of 20 batches of CM was
performed based on the relative peak areas of all the 25
common chromatographic peaks, in which CA (peak 1) was
assigned as the reference peak. A 25×20 matrix was formed
as input data to SPSS 17.0. After determining an appropriate
distance method, the results were shown in Fig. 3, from
which a clear classification was revealed as cluster Ι, cluster
ΙΙ, and cluster ΙΙΙ. S1–S11 were classified into cluster Ι and
S12–S13 formed cluster ΙΙ, while S14–S20 formed cluster
ΙΙΙ. The shorter distance between two samples demonstrated
their higher similarity in quality. Cluster Ι was more similar
to cluster ΙΙ than cluster ΙΙΙ, which was consistent with the
results of SA. According to the dendrogram shown in Fig. 3,
another classification combining cluster ΙΙ and cluster Ι
could also be made if a higher distance level was adopted.
Additionally the distance between S14 and S1–S13 was
farther with the low similarity (r<0.8) among them, which
was in conformity to the results of SA to some extent.
Principle Component Analysis
PCA is a sophisticated technique widely used for reducing
the dimensions of multivariate problems without losing
much information (Sun and Chen 2012). The resulted
PCA loading plot could be used to classify samples objec-
tively. The same values of RPA of all common peaks used to
HCA were also applied to it. On the basis of eigenvalues>1,
the first two principal components PC1 and PC2 are often
used to provide a convenient visual aid for identifying
Fig. 3 Dendrogram of S1–S20
samples on HCA
Food Anal. Methods
7. inhomogeneity in the data set (Clifford et al. 2007). The
result was shown in Fig. 4. The scattered small dots indi-
cated that all the samples could be easily classified into two
groups. S1–S13 constituted group Ι. As the distance be-
tween S14 and S15–S20 was shorter than that with group
Ι, S14–S20 formed group ΙI. The classification above was
similar to the results of SA and HCA.
Conclusions
In this paper, a novel approach based on ultra-performance
liquid chromatography coupled with photo-diode array detec-
tor fingerprint analysis with chemometrics method was ap-
plied to discriminate C. morifolium Ramat from different
regions. The UPLC conditions and extraction conditions were
optimized respectively. SA, HCA, and PCA were used to
classify 20 batches of samples. The SA result showed abun-
dant diversity of chemical constituents qualitatively in the CM
from different sources. Moreover the HCA and PCA method
clustered the samples into three or two classes, showing the
close and/or distant relations among the 20 samples by the
dendrogram and PCA plot, respectively. The results derived
from three chemometrics methods above were basically con-
sistent with each other. The results also indicated that UPLC
played a great role in the quality control of TCM due to its
faster detection and less solvent consumption. As a key tech-
nique for Chinese herbal quality control and a powerful sup-
port for the progress of Chinese herbal medical prescription,
the investigation described in this work may provide useful
reference to establish further quality control system for other
related TCM or preparations.
Acknowledgments We thank the National Natural Science Founda-
tion of China [21206148] for financial support.
Conflict of Interest Xianrui Liang declares that she has no conflict
of interest. Hong Wu declares that she has no conflict of interest. Weike
Su declares that he has no conflict of interest. This article does not
contain any studies with human or animal subjects.
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