Vol.1, No.3, 134-138 (2009)
doi:10.4236/health.2009.13022
SciRes Copyright © 2009 http://www.scirp.org/journal/HEALTH/
Health
Openly accessible at
Simultaneous determination of chlorogenic acid and
baicalin in heat-clearing and detoxicating oral liquid by
NIRS
Zhen-Yao Liu1, Bing Liu1, Ji-Dong Yang1,2*
1College of Chemistry and Chemical Engineering, Southwest China University, Chongqing, China
2College of Chemistry and Chemical Engineering, Yangtze Normal University, Chongqing, China; Corresponding author:
Received 24 August 2009; revised 9 September 2009; accepted 11 September 2009.
ABSTRACT
The calibration model for simultaneous deter-
mination of chlorogenic acid and baicalin in
heat-clearing and detoxicating oral liquid was
built by partial least squares and near infrared
spectroscopy, and the method of spectral pre-
treatment was discussed. Building model from
calibration set obtained good results, and vali-
dated by prediction. According to heat-clearing
and detoxicating oral liquid from 30 batches of 6
factories, the correlation coefficient of chloro-
genic acid and baicalin model are 0.9993 and
0.9923, The root mean square error of cross
validation (RMSECV) are 0.467 and 0.480, and
the standard Error of prediction (SEP) of chloro-
genic acid and baicalin are 0.356 and 0.370 re-
spectively. The correlation coefficients in pre-
diction set are 0.9997 and 0.9969, prediction
results are accurate and reliable. This method
can be applied in rapid analysis of heat- clearing
and detoxicating oral liquid, and it is fit for
on-line detection and has a wide application
prospect.
Keywords: Near Infrared Spectroscopy; Partial
Least Square Method; Heat-Clearing and
Detoxicating Oral Liquid; Chlorogenic Acid; Baicalin
1. INTRODUCTION
Heat-clearing and detoxicating oral liquid is very com-
monly used nowadays in china. Heat-clearing and de-
toxicating oral liquid has efficacies of heat-clearing and
detoxifying, clearing lung and moisturizing dryness,
smoothing throat to stop cough. It is used in therapy of
exogenous fever, swollen sore throat, headache and gen-
eral aching. Heat-clearing and detoxicating oral liquid is
made of honeysuckle, scutellaria, weeping forsythia,
dyers woad leaf, heartleaf houtluynia and gypsum. Main
drug of heat-clearing and detoxicating oral liquid are
honeysuckle and scutellaria, active component of hon-
eysuckle and scutellaria are chlorogenic acid and baicalin
separately common method that it determines the main
component of heat-clearing and detoxicating oral liquid
quantitatively was high performance liquid chromato-
graphy[1]. However, the high performance liquid chro-
matography required tedious and complex processing for
samples. This method was time-consuming and destruc-
tive. And the use of chemical reagents was also a factor if
the economic benefit and safety was considered in the
experiment [2]. All of these factors have underlined the
need for a reliable technique to quickly and nondestruc-
tively detect the quality of heat-clearing and detoxicating
oral liquid.
In spectral analysis, the region that the wavelength is
between 780nm and 2500nm is called NIR region. Most
organic compounds and some inorganic matter’s funda-
mental frequency of chemical bond vibration are in this
region. The chemical bonds of C-H, N-H, O-H and S-H
have stretching vibration in NIR region. the analyte that
has this chemical bonds could be determined by near
infrared method.
From 1990s, NIR spectrum was developed fastest and
most striking in the spectral analytical technique.
Near-infrared spectroscopy was a powerful analytical tool
used in various industrial sectors, e.g. the agricultural,
petrochemical, textile and pharmaceutical [3-6]. NIR
spectroscopy was a fast, accurate and non-destructive
analytical tool that can be considered as a replacement of
the traditional chemical analysis. The NIRS did not need
pretreatment and destroy the sample, had no pollution,
convenient and fast, could determine online, simultaneity
detect multi-component, reproducibility was good, all of
Contract/grant sponsor: Science & Technical Committee of Chong-
qing, P. R. China. Contract/grant number: CSTC,2008EA5008.

Z. Y. Liu et al. / HEALTH 1 (2009) 134-138
SciRes Copyright © 2009 http://www.scirp.org/journal/HEALTH/
135
135
Openly accessible at
Figure 1. Structural formula of chlorogenic acid and baicalin.
In Figure 1, there are plenty of C-H bonds and O-H bonds in chloro-
genic acid and baicalin, so chlorogenic acid and baicalin have signifi-
cant absorption in NIR region. Therefore, chlorogenic acid and baica-
lin in Heat-clearing and detoxicating oral liquid could be detected fast
and nondestructively by near infrared method.
8009001000 110012001300 1400 1500nm
2
, 3
0
,
4
,
6
,
8
, 1
0
, 1
2
, 1
4
, 1
6
, 1
8
, 2
0
, 2
2
, 2
4
, 2
6
, 2
8
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Abs
Figure 2. Near infrared spectra of heat-clearing and detoxicating
oral liquid sample.
this advantage indicates that NIR spectrum method was
fit for detecting active component of the traditional chi-
nese medicine, and NIR spectrum method had been used
in analysis of Chinese medicine successfully recent years
[7-11].
2. MATERIALS AND METHODS
2.1. Sample and Reagents
In the experiment, 30 batches of heat-clearing and de-
toxicating oral liquid samples from six manufacturers
(listed in Table 2) were investigated. All the samples
were placed in the same temperature-controlled room
where the spectrometer was located before performing
the analysis. The standard agent of chlorogenic acid
(batch number: 1107532200413) and baicalin (batch
number: 1107152200514) were provided by national
institute for the control of pharmaceutical and biological
products.
2.2. NIR Spectra Collection
The NIR spectra were collected using the near-infrared
spectrophotometer (U-4100, Hitachi, Japan) with 1cm
quartz sample cell. Air was reference, wavelength range
was 800~1500 nm, wavelength spacing was 2 nm, slit
width is 2 nm. Scanning speed was 1500 nm/s, every
sample was scanned 3 times and got average value. NIR
Spectrum of oral liquid was Figure 2.
2.3. High Performance Liquid
Chromatography Analysis
Prior to the NIR spectral analysis, all samples were ana-
lyzed by high performance liquid chromatography
(LC-2010A, Shimazu, Japan). 1 ml heat-clearing and
detoxicating oral liquid was diluted by adding 1 ml of
distilled water, The mixed solutions were centrifuged at
12000 rpm for 40 min using a temperature-controlled
centrifuge (Z323K, Hermle, Germany), then filtered
through a 0.45 μm Millex membrane (Millipore, Mol-
sheim, France) in order to separate the dispersed solid
particles. The injected sample volume was 2μm. The
HPLC settings were as follows: mobile phase of 50%
methanol and 0.3% phosphoric acid in water, flow rate of
1.0 mL min-1, run time of 10 min, column temperature of
40˚C. To check the reproducibility of the HPLC meas-
urements, each sample was measured twice. Quantifica-
tion was performed by integrating the peak areas of the
HPLC results using computer-assisted software matched
with the apparatus.
2.4. Method (Multivariate Analysis of Partial
Least Square)
Multivariate analysis was used for quantitative and quali-
tative analysis. Partial Least Square (PLS) algorithm,
which was proven to be effective in many quantitative
applications, was used in this experiment too. These
methods with original and vector normalised spectra were
used to develop calibration models. The performance of
the final PLS model was evaluated in terms of root mean
square error of cross validation (RMSECV) for cross
validation and root mean square error of prediction
(RMSEP) during test validation, and the coefficient of
determination (R2).
The residual (Res) is the difference between the true
and fitted value. Thus the sum of squared errors (SEE) is
the quadratic summation of these values (Eq. 1).
2
Re }{i
s
SSE  (1)
The root mean square error of estimation (RMSEE) is
calculated from this sum, with “n” being the number of
samples and “r” the rank (Eq.2).
1
n-r-1
RMSEE SSE (2)
The determination coefficient, R2 (Eq.3) gives the
percentage of variance present in the true component
values, which is reproduced in the regression.

Z. Y. Liu et al. / HEALTH 1 (2009) 134-138
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2
2
(1) 100
()
im
SSE
R
yy
 


(3)
R2 can be negative for low ranks, when the residual are
larger than the variance in the true values (yi). In case of
cross validation, the RMSECV is calculated using Eq.4.
2
1
()
n
ii
i
yy
RMSECV n




(4)
For the prediction set, the root mean square error of predic-
tion (RMSEP) is calculated as follows (Eq.5) [13].

2
1
()
n
ii
i
yy
RMSEP n




(5)
3. RESULTS AND DISCUSSION
3.1. Choice of Model Algorithm
In the analyze of NIR spectrum, model algorithm in-
cluded MLR、PLS、PCA、AN N、TP and etc. Basic idea
of model algorithm was made use of total spectrum in-
formation of NIR spectrum, in order to eliminate effects
of spectrum peak’s overlap and complex background. The
absorption peaks of NIR spectra were broad and overlap,
making single wavelength calibration impossible due to
large hidden information in spectral data. Useful multi-
variate calibration tools such as partial least-squares (PLS)
were available. PLS was an analysis method that relates
changes in spectral data. It extended and improved the
potential application of spectroscopy technique in phar-
maceutical industry by extracting features from spectra.
As a form of principal component analysis (PCA), PLS
made use of the information of the NIR spectrum and the
established analyte values associated with the spectrum.
The calibration equation could be applied to unknown
samples once the equation was established by PLS from
samples where the analyte was determined by another
acceptable method. PLS regression was a multivariate
method. It had no restriction in using the number of
wavelengths that could be selected for the calibration to
make the model suitable to extract the maximum infor-
mation from the spectra. The information extracted could
be condensed in the latent variables or factors which were
used in the calibration and prediction steps [12].
3.2. Choice of Spectrum Pretreatment
Method
Spectral signal that was determined by detector contains
not only spectral information but also several of noises.
Smoothing processing of signal was a common method to
Table 1. Mathematical statistics results for calibration models
of chlorogenic acid and baicalin.
chlorogenic acid baicalin
R2RMS
ECV PCs R2 RMS
ECV PCs
Smoothing
processing
0.964
6 1.3798 0.978 5 0.8823
First deriva-
tive
0.998
7 0.4675 0.984 7 0.4804
Second
derivative
0.990
9 1.0425 0.983 2 0.9082
reject noise. It was effective to smooth the high frequency
noise and increase the signal-to-noise ratio. Origin spec-
trum that processed by derivative protected against the
influence of baseline drift. It was effective to discriminate
overlapping peaks, increased the resolution and sensi-
tivity. But derivative spectrophotometry amplified differ-
ence of adjacent wavelength point, it lead to amplification
of noise and decrease of signal-to-noise ratio. So the
pretreatment method should be select to different spec-
trum of samples. Smoothing processing, first derivative
and second derivative was compared in this experiment.
Parameters of model are in the Table 1. From Table 1, the
model effect that pretreated by first derivative was best to
chlorogenic acid and baicalin.
3.3. Optimization of Model and Determination
of PCs in Model Development
The number of PCs to use in the PLS model was very
important because too few components will generate an
underfitted model, fitted loosely the data structure. Using
too many, on the other hand, generates an overfitted
model, one which fitted parts of the noise of the calibra-
tion set, thus generating a low RMSEC but performing
poorly in the validation set. The optimum number of PCs
would then decompose the spectral data matrix between
the structure and the noise. For this reason, evaluation of
the variance plots was needed to determine which PCs
describe most of the residual variance of the expected
value matrix in order to determine the optimal number of
PCs used in the regression model. Optimum number of
PCs was usually done by cross-validation. Outliers were
detected using the Chauvenet test at the 95% confidence
level.
The anomalous points should be rejected when we
build the model. This experiment used interactive check
method, optimized the model step by step, determined the
PCs, as far as get the best model. The number of method
of judging and rejecting anomalous points or influential
point were two. The first method was mahalanobis dis-
tance, MD = si
T( STS)-1si， MD was mahalanobis distance,
T was inverse matrix symbols, si was Score, S was cali-
bration set score matrix. The second method was that

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Figure 3. The changes of residual variance with PCs: chloro-
genic acid.
Figure 4. The changes of residual variance with PCs: baicalin.
the influence of model was investigated by Leverage; it
was also called “reject one” cross-check method. It was
combined diagnosed by Leverage and studentized resid-
ual, the influential point rejected step by step. This ex-
periment used Leverage and Residual to reject the ano-
malous points of spectrum and chemical value. Leverage
was set as 3.0, error of chemical value is 6.0%, optimized
the model by rejecting anomalous points gradually, got
the best PLS1 regression model of determining chloro-
genic acid and baicalin. The changes of residual variance
with PCs were Figure 3 and Figure 4.
3.4. External Verification of Model
15 heat-clearing and detoxicating oral liquid samples of
prediction set was predicted about chlorogenic acid and
baicalin with the model that built in this experiment. SEP
of chlorogenic acid and baicalin were separately 0.356 and
0.370. The experimental results were credible. The scatter
plot of chlorogenic acid and baicalin between NIR pre-
dicted and measured values were Figure 5 and Figure 6.
3.5. Application of Model
Thirty heat-clearing and detoxicating oral liquid samples
from six manufacturers were selected for application
Figure 5. Correlation coefficient graph of chlorogenic
acid between prediction result and actual value.
Figure 6. Correlation coefficient graph of baicalin
between prediction result and actual value.
Table 2. Range of content of chlorogenic acid and baicalin
about six manufacturers.
manufacturer
chlorogenic
acid
（） mg/ml
baicalin（）
mg/ml
Sichuan Taihuatang
Pharmaceutical Co.,
Ltd
0.373-0.410 0.555-0.605
Chengdu Tianyin
Pharmaceutical Co.,
Ltd
0.374-0.425 0.650-0.736
Sichuan Xuyang
Pharmaceutical Co.,
Ltd
0.281-0.308 0.650-0.709
Zhengzhou Ruilong
Pharmaceutical Co.,
Ltd
0.840-0.865 0.574-0.597
Jiangxi Nanchang
Pharmaceutical Co.,
Ltd
0.380-0.416 0.238-0.255
Sichuan Good doctor
Pharmaceutical Co.,
Ltd
0.926-0.949 0.386-0.395
experiment. NIR spectrum of the samples were collected,
and predicted with quantitative model. Range of content
of chlorogenic acid and baicalin about six manufacturers
was in Table 2.
4. CONCLUSIONS
The results obtained in this research show the potential

Z. Y. Liu et al. / HEALTH 1 (2009) 134-138
SciRes Copyright © 2009 http://www.scirp.org/journal/HEALTH/
138
superiority of NIR spectroscopy to detect chlorogenic
acid and baicalin simultaneously in heat-clearing and de-
toxicating oral liquid with the reference method of HPLC.
The combination of NIR spectroscopy and PLS methods
had been found to be a convenient, versatile method. It
had the ability to dramatically reduce consuming time and
cost of monitoring without using any chemical reagent, it
was fit for on-line detection in pharmaceutical companies.
Openly accessible at
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