Vol.1, No.3, 244-248 (2009)
Copyright © 2009 http://www.scirp.org/journal/HEALTH/
Openly accessible at
Application of on-line quality control for salvianolic acid
B by near infrared spectroscopy
Jin-Wei Zhang*, Yan Liu, Wei-Wei Liu, Yan-Ying Zhang
Tasly Modern TCM Resources Co., LTD., Tianjin, China; tjvv@163.com
Received 11 August 2009; revised 23 September 2009; accepted 22 October 2009
OBJECTIVE: To study and establish quality con-
trol model of the Salvianolic Acid B by Near In-
frared Spectroscopy (NIRS), and to realize on-line
quality control of extracting and purifying proc-
esses of industrial scale herbal product manu-
facturing. METHOD: NIR chromatography was
obtained from on-line NIR detection of extract-
ing process and purifying process. HPLC
analysis was carried out to determine the con-
tents of salvianolic acid B. Partial Least Squares
Regression (PLS) was used to establish the
model between the information between NIRS
and HPLC. RESULTS: For extracting model: the
optimum Near Infrared (NIR) wavelength range was
9815- 5430cm-1, R=0.9784, RMSEC=0.258; for puri-
fying model: the optimum NIR wavelength range
was 9815-5430cm-1, R=0.9776, RMSEC=4.02. The
average relative error was <5%. CONCLUSION:
NIR technique is applicable for on-line quality
control in production of salvianolic acid B.
Keywords: NIR; Salvianolic Acid B; On-Line
Quality Control; Industrial Scale
Real-time control of contents has ground to gain in con-
trolling mode of Chinese medicine manufacturing in
China, as the very traditional techniques remain the
mainstream. Off-line tests do not meet the on-line control
demands for production process. On-line test is the solu-
tion to quality consistency, which is still the bottle-neck in
manufacturing of Chinese medicine.
Near infrared (NIR) Spectroscopy technology develops
rapidly in recent years as a fast analytical technology. It
shows the substances and structures of the tested samples
indirectly. The procedure of acquiring information by
NIR chromatography is easy and low-cost. It does not
require complicated pre-treatment of the samples but still
meets the requirement of on-line testing.
As the chemistry metrology and computer technology
develop, NIR has already been widely adopted in agri-
culture and petroleum industries [1-2]. In 2002, USFDA
approved NIR to be one of the standard measurement
methods. In 2005, “China Pharmacopoeia” listed “NIR
Spectroscopy instruction” in [3]. NIR Spectroscopy
technology turns out to be a research hotspot in on-line
testing in Chinese medicine industry, especially in manu-
facturing process recently. Some of the experts use NIR to
measure the content of Tanshinone and Salvianolic acid B
in Danshen liquid distilling process [4] while some others
use NIR in quantitative analysis of Gardenia herbs dis-
tilling and Notoginsenosides [5-6]. NIR is also reported
to be used in real-time analysis of CoptisRoot and Bit-
tetOrange extract purification processes [7-8]. But these
are limited in laboratory scale simulated manufacture
research, but not yet in manufacture scale distilling or
purification of TCM.
This research covers the key working procedures in
manufacturing of Salvianolic acid B, including distilling
and purification, and focuses on concentration of Salvi-
anolic acid B, which is the main effective ingredient in
aqueous extract and elute. On-line NIRS chromatography
charts are collected and chemometrical calculation is
done based upon data from HPLC testing. Models for
content analysis of Salvianolic acid B in distilling and
purification processes are established in order to facilitate
real-time quality monitoring in manufacturing of Salvi-
anolic acid B.
2.1. Instruments and Materials
2.1.1. Instruments
ANTARIS FT-NIR(Thermo), optical fiber and TQ Ana-
lyst software; Agilent 1100 HPLC; 500L Multifunctional
distill tank (Tianjin Tasly Modern TCM Resources co.,
LTD); 150L Stainless steel chromalography columns
(Tianjin Tasly Modern TCM Resources co., LTD).
2.1.2. Materials
Danshen (From Shangluo, Shanxi, China; identified as by
J. W. ZHANG et al. / HEALTH 1 (2009) 244-248
SciRes Copyright © 2009 http://www.scirp.org/journal/HEALTH/
Openly accessible at
Quality Control Department of Tianjin Tasly Modern
TCM Resources co., LTD);
Salvianolic acid B reference standard (from National
Institute For The Control of Pharmaceutical and Bio-
Methanol, acetonitrile (chromatographical grade)
Methanoic acid is (Analytical grade; Tianjin Chemical
Reagent Co., LTD).
2.2. Rationale and Method
2.2.1. Rationale (Moved To the Introduction
The NIR is electromagnetic wave between VIS and MIR.
ASTM (American Society of Testing Materials) defined
The area of NIR chromatography as 780~2526nm
(12820~3959cm-1) [9]. The wavelength of NIR covers
frequencies of organic molecule groups with hydrogen
(OH, NH and CH). Scanning organic samples with NIR
produces information about the organic molecules with
hydrogen. Absorption and maximum absorption wave-
length differ from each other with different groups or
same group in different chemical constitutions, and there-
fore, the chromatography bears information of chemical
structures, which ensures its application in analysis of
bioactive ingredients [10]. Because of the width and
overlapping of NIR absorption ranges, application of
chemometrics is a necessity in extraction of chroma-
tographic information. In this research, HPLC test is
applied for content analysis of entities, while PLS re-
gression applied for establishment of model between NIR
information and content analysis information for on-line
testing and quality monitoring of Salvianolic acid B.
2.2.2. Methods
1) Research system
Weigh 50kg Danshen, put into the extracting tank-500L,
add 5.5 times of water and start decoction; after 1 hour,
export extract solution then add triple water to extracting
waste; after 0.5 h, export extract solution. Mix and prepare
the extract solutions for separation with polyamide resins.
In the process of extracting and refine, we collect chro-
matography data at regular intervals; test samples by
HPLC. The extract and refine devices were shown in the
following illustration:
2) On-line collection of NIR chromatography data
2 parallel chromatographic charts are collected at
2-5min intervals in the process of extracting and refining.
Samples for HPLC tests are collected at the same time of
collecting NIR chromatography. Samples and NIR chro-
matography charts are coded correspondingly.
NIR chromatography is collected at scanning scope of
10000-4000cm-1, scanning number of 32, and resolution
of 8 cm-1. The NIR chromatography of Danshen extract
and elutriant is shown in the following illustration 3-4.
In the process of collecting NIR chromatography, air
SoblR diffuse
Reflectance probe
Figure 1. Extracting equipment of Salvianolic acid B.
SoblR diffuse
Reflectance probe
Figure 2. Separation equipment of Salvianolic acid B.
bubble in the flow cell is a problem in the process of
experiment, as it is directly influence the absorption and
emission of signals, resulting in irregular peaks in chro-
matography and serious interference of chromatography
information. Measures are taken with equipment and
process till the solution of the issue.
3) Testing method of Salvianolic acid B
Chromatographic column: Agilent ZORBAX SB-C18,
5um, 4.6*250mm; mobile phase: methanol-acetonitrile-
J. W. ZHANG et al. / HEALTH 1 (2009) 244-248
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Openly accessible at
Figure 3. NIR chromatography of salvianolic acid B extract.
Figure 4. NIR chromatography of salvianolic acid elutriant.
methanoic acid-water=30:10:1:59; detection wavelength:
286nm; flow rate: 1ml/min; column temp: 30C; injection:
5ul; preparation of reference substance solution: weigh
accurately salvianolic acid B reference, and add it to the
75% methanol to make methanol solution of 0.14mg/ml;
preparation of test solution: weigh accurately extract
solution and elutriant, add it to the 75% methanol and
dilute to 10ml, shake till well mixed, and then filtrate to
the 0.45um. Filtrate is the test solution.
3.1. Pretreatment of Chromatography Data
In the process of collecting chromatography, environ-
mental changes produce baseline excursion, while ran-
dom noise of sample background produce influences on
calibration results. Pretreatment of the chromatography
data is conducted for the issue. Factorial analysis of
various pretreatment methods and statistical factors in
extracting and refining models are listed in Table 1 and
Table 2.
The methods of pretreatment are MSC, SNV, first-
order differential equation, second-order differential
equation, S-G (Savitzky-Golay) smooth and Norris De-
rivative Filter smooth etc. 1st derivative +Norris is se-
lected as the pretreatment for both extract and refine
model accordingly.
Table 1. Effect of various pretreatment methods on extracting
Method of the chromatography
pretreatment R RMSEC RMSECV
Original chromatography 0.9581 0.358 0.389
MSC 0.8094 0.733 0.807
MSC+ 1st derivative 0.9169 0.498 0.566
MSC+ 2nd derivative 0.9055 0.530 0.911
MSC+1st derivative +Norris 0.8978 0.550 0.612
MSC+2nd derivative +Norris 0.8907 0.568 0.666
SNV 0.8131 0.727 0.800
SNV+1st derivative 0.9179 0.496 0.743
SNV+2nd derivative 0.9064 0.528 0.906
SNV+1st derivative +Norris 0.8988 0.547 0.610
SNV+2nd derivative +Norris 0.8906 0.568 0.666
1st derivative +Norris 0.9784 0.258 0.286
2nd derivative +Norris 0.9574 0.361 0.430
1st derivative +S-G 0.9746 0.279 0.378
2nd derivative +S-G 0.9110 0.515 0.761
Table 2. Effect of various pretreatment methods on purifying
Method of the chromatography
pretreatment R RMSEC RMSECV
Original chromatography 0.6681 14.9 15.8
MSC 0.7782 12.6 13.4
MSC+1st derivative 0.9657 5.27 7.09
MSC+2nd derivative 0.9761 4.38 11.3
MSC+1st derivative +Norris 0.9635 5.39 6.95
MSC+2nd derivative +Norris 0.9524 6.15 7.05
SNV 0.9313 7.34 8.18
SNV+1st derivative 0.9624 5.49 6.99
SNV+2nd derivative 0.9730 4.65 11.5
SNV+1st derivative +Norris 0.9551 5.97 6.80
SNV+2nd derivative +Norris 0.9500 6.28 7.06
1st derivative +Norris 0.9776 4.02 4.42
2nd derivative +Norris 0.9668 5.26 6.09
1st derivative +S-G 0.9733 4.56 5.53
2nd derivative +S-G 0.9760 4.46 9.59
3.2. Selection for the Best Principal Factors
In regression for the models with PLS, rational selection
of principal factors is critical to avoid “over fitting”.
Through LOOCV (leave-one-out cross-validation), we
inspect the influence principal factors to RMSECV
(Root-Mean-Square Error of Cross-Validation). The re-
sults for extracting and refining models are in Figure 5
J. W. ZHANG et al. / HEALTH 1 (2009) 244-248
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Openly accessible at
and Figure 6.
When the principal factor number is 5, the RMSECV is
0.286 and reaches pleateau phase. It is decided the prin-
cipal factor number is 5. From Figure 6, RMSECV
reaches peak at 4.42 when principal factor number of
refining model is 6.
3.3. Selection of Chromatography Scope
The decision of chromatography scope is the most diffi-
cult step in the quantitative analysis model of NIR, as the
calculation is not well established in chemometrics. The
wave band of 9815-5430 cm-1 is validated as the optimal
chromatography scope with optimized correlation coeffi-
cient and forecast effects.
3.4. Establishment of Models
Salvianolic acid B contents in 6 batches are analyzed,
while 473 experiment samples (222 from extracting
process and 251 from refining process) are collected.
First-order differential equation is the statistical method
in establishment of Salvianolic acid B models through
PLS. According to the result of cross-validation: extra-
cting model: the best principal factor number is 5,
correlation coefficient R=0.784, RMSEC=0.258; refin-
ing model: the best principal factor number is 6, corr-
elation coefficient R=0.9776, RMSEC=4.02. The correl-
ation coefficients of predicted results and test results are
shown as following table:
3.5. Evaluation of Prediction
In order to validate the predict effect of the model or REF,
a batch with the same manufacturing conditions, is pro-
duced, and 19 extracting samples and 33 refining samples
are collected respectively for the validation of the models.
The trend in predicted values and test values of Salvia-
nolic acid B is shown as following:
Obvious from Figures 9 and 10, predicted values
match test values in a constant and steady style with
exceptions in few outliers. The relative error is less than 5
between predictive values and true values (3.2% in ex-
tracting model and 4.8% in refining model). Abnormal
data points may be result of several facts. First of all, NIR
test requires tested content to be at least 0.1%. Very low
target concentration leads to more errors in measurement.
The on-line nature of the measurement also contributes in
the errors, as test liquid is in constant movement in pipe-
line, and instability, as well as formation of bubbles in
circulation pool is inevitable.
1) NIR (near infrared) Spectroscopy technology have
many strong points including convenience, time effec-
tiveness, cost effectiveness, and environment friendliness.
On the basis of the research and model, it is feasible to
Figure 7. Correlation of salvianolic acid B’s content between
actual and calculated.
Figure 8. Correlation of salvianolic acid B’s content between
actual and calculated.
Figure 9. Predicted and test values of Salvianolic acid B content.
159 131721252933
test values
Figure 10. Predicted and test values of Salvianolic acid B content.
J. W. ZHANG et al. / HEALTH 1 (2009) 244-248
SciRes Copyright © 2009 http://www.scirp.org/journal/HEALTH/
implement on-line testing and quality monitoring as the
prediction effects of the extraction and refinement models
are acceptable in manufacturing of Salvianolic acid B.
Openly accessible at
2) Pretreatment of the chromatography data and prin-
ciple factor number has influence on the accuracy of the
model. Analysis and comparison of various conditions
and is necessary in selection of the principal factors.
3) Extremely low concentration of target entity, insta-
bility and bubble formation in the current have influence
over collection of chromatography data and over accu-
racy of the model in prediction. Necessary measurement
should be taken to reduce the influences.
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