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1
Application of Near-Infrared spectroscopy (NIRS) as
a Tool for Quality Control in Traditional Chinese
Medicine (TCM)
L. P. Guo1, L.Q. Huang1*, X. P. Zhang1, L. Bittner1 , C. Pezzei1, J.
Pallua1, S. Schönbichler1, V.A. Huck-Pezzei1, G.K. Bonn1, C.W.
Huck2*
1Institute of Chinese Materia Medica, China Academy of Chinese Medical
Sciences, Beijing, China
2Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens
University, Innrain 52a, 6020 Innsbruck, Austria
* address correspondence to:
Professor Lu-Qi Huang, PhD
Institute of Chinese Materia Medica, Chinese Academy of Chinese Medical
Science, Beijing 100700, China
Tel: +86 10 64014411; Fax: +86 10 6401 3996
Email: [email protected]
and
Prof. Dr. Christian W. Huck
Head of Spectroscopy Group
Institute of Analytical Chemistry and Radiochemistry
Leopold-Franzens University
Innrain 52a
6020 Innsbruck
Austria
Tel.: +43 512 507 5195 ; Fax: +43 512 507 2965
Mail: [email protected]
2
Abbreviation list
ANN, artificial neuronal network; ANOVA, analysis of variance; COE, constant
offset elimination; DN, data normalistion; DLPS, discriminant partial
least-squares; DR, diffuse reflection; ESI, electrospray ionization; FODR, fiber
optic diffuse reflection; FT, fourier transform; LC, Liquid chromatography; GMP,
good manufacturing practice; MPLS, modified partial least square; MS, Mass
spectrometry; MIR, mid infrared; MVA, multivariate analysis; MSC, multiplicative
scatter correction; NIR, near infrared; PCA, principal component analysis; PCR,
principal component regression; PHP, patented herbal preparations; PLSR,
partial least square regression; QC, quality control; RBFNN, radial basis
function neuronal network; SEC, standard error of calibration; SEP; standard
error of prediction; SFC, super critical fluid chromatography; SOP, standard
operation practice; SPE, solid-phase extraction; SVR, support vector regression;
WT, wavelength transformation
Abstract
Traditional Chinese Medicine (TCM) is becoming more and more popular all
over the world. Novel analytical tools for quality control are highly demanded
enabling analysis starting at breeding and ending at biological fluids including
urine or serum. Compared to analytical separation methods (chromatography,
electrophoresis) near-infrared spectroscopy (NIRS) allows analyzing matter of
interest non-invasively, fast and physical/chemical parameters simultaneously. It
can be used for the quantitative control of certain (active) ingredients. In many
cases identification can only be achieved by pattern recognition. Therefore,
NIRS combined with cluster analysis offers huge potential to identify e.g.
species, geographic origin, special medicinal formula etc. In the present
contribution the fundamentals, possibilities of NIR applied in quality control of
TCM are pointed out and its ad- and disadvantages are discussed in detail by
several practical examples.
3
Content
Introduction
1 Fundamental principles of NIRS
2 NIR in quality control in TCM by quantitative measurements
3 Applications of NIRS in TCM
3.1 Control of raw materials
3.1.1 Identification of falsification
3.1.2 Classification into species, geographic regions
3.1.3 Identification of multi-originated raw materials
3.1.4 Identification of geoherbs and habits
3.1.5 Quality control by quantitative analyses of ingredients
3.2 Processing and extraction
3.2.1 Processing quality control
3.2.2 Extraction quality control
3.3 Preparation of medicinal formulation
3.3.1 On line detection during manufacturing
3.3.2 Quality control of patented formulations
4 Perspectives
5 References
Figure legends
Figures
4
Introduction
Authentication of plant material and origin, identification of parts of the plant,
qualitative and quantitative analysis of primary and secondary metabolites are
the demanded analytical key challenges to efficiently ensure quality in
Traditional Chinese Medicine (TCM) [1]. Traditionally applied identification,
physical and chemical description follow distinct and complex experience rules
[1,2], which in many cases do not enable an exact, clear and objective
determination. Often only a (dried) part of the plant (or even animal) is present
and then identification is based on personal and subjective operator decisions.
Due to these circumstances, several new analytical techniques were
established during the last three decades enabling a more objective analysis:
The introduction of liquid chromatography (LC) [3] in the early 80ies and
capillary electrophoresis (CE) [4] allowed a fast separation of low nad high
molecular weight ingredients. Due to the establishment of LC coupled to mass
spectrometry (MS) via an electrospray interface (ESI), which was honored with
the Nobel Prize given to Fenn in 2002, much more complex samples of interest
could be put under consideration. Different kinds of carrier materials applied as
a stationary phase in solid-phase extraction (SPE) were designed to selectively
enrich analytes of interest deriving from crude biological matrices including plant
extracts, serum and urine [5]. Biochemical methods including DNA marker and
coding were introduced for species identification [6]. Although, each of these
methods is highly efficient, several different expensive machines controlled by
special trained staff are required and finally, experiments are time consuming,
being hardly suitable to high-throughput analysis [7]. Near infrared spectroscopy
(NIRS), which was already introduced in 1980, offers several advantages,
including fast and simultaneous determination of different physical and chemical
parameters, as well as easy operation at low costs after a careful calibration of
the entire system. In the following chapters the fundamental principle, potential,
ad- and disadvantages of NIRS applied as a tool for quality control in TCM are
pointed out and discussed in detail.
1 Fundamental principles of near-infrared spectroscopy
Near-infrared spectroscopy (NIRS) is a spectroscopic method using the region
of the electromagnetic spectrum from 4.000 to 12.800 cm-1 (2500 – 780 nm) [8],
5
which was already discovered by Herschel in 1800. The NIR region covers the
overtone and combination transition vibrations of mainly the C-H, O-H and N-H
groups. The molar absorbance in the NIR region is typically quite small and due
to broad signals more difficult to identify in comparison to mid-IR (MIR, 2.500 –
25.000 nm) spectra because of the higher grade of overtone and combination
excitations. For spectral data evaluation two methods, raw spectra interpretation
and chemometrics/multivariate data analysis (MVA) are commonly used to
elucidate the NIR spectra [9]. Visual spectra interpretations and absorbance
band assignments play an important role, especially for the comparison of pure
materials but also of rather complex spectra [10]. MVA based calibration
techniques are applied to combine the spectral data with target parameters
transferred from reference techniques or to expose similarities and hidden data
structures in the spectra [11-13]. NIRS coupled with spectra pre-treatment
methods (derivatives, smoothing, normalization, filters, and baseline- and
multiplicative correction methods) and multivariate methods (e.g., principal
component analyses, PCA; partial least squares, PLS; multiple linear regression,
MLR; principal component regression, PCR) has been successfully used for the
simultaneous analysis of chemical and physical parameters in agriculture,
pharmaceutical and material analysis [14-16]. One of the forerunners of modern
NIR applications, Karl Norris of the U.S. Department of Agriculture, used the NIR
wavelength region for the spectral analysis of moisture content of grain and
seeds [17], which at the same time was the first application of NIR in plant
analysis. Radiation in the NIR region can typically penetrate deeper into a
sample than MIR. Instrumentation for NIRS is suitable either for measurement in
reflection/transflection (R), transmission (T) or interaction (I) mode (Figure 1). It
has generally been described to be very useful in probing bulk material with little
or no sample preparation. NIR is a non-invasive, fast analytical technique since
the sample of interest (tissue, extract, tablet, etc.) must not be destroyed for the
analytical procedure. Next to this, NIRS possess the following additional
advantages over other analytical techniques: Chemical (class of plant
ingredients) and physical parameters (solvent composition, viscosity, pH, and
conductivity) can be determined simultaneously; Measurements are robust and
cheap; Analyses can be carried out off-line, on-line or in-line; High suitability for
6
automation and high-throughput screening is guaranteed and measurements do
not require special trained staff.
2 NIR in quality control of TCM by quantitative measurements
In many cases quality control is achieved by quantitative measurement of
interesting components. Therefore, it is essential to calibrate the NIR system
with a suitable set of samples, analysed by a reference method. Reference
analysis is carried out by chromatographic/electrophoretic methods including
iquid chromatography (LC), LC coupled to mass spectrometry (MS), micro-liquid
chromatography (µ-LC), gas chromatography (GC), capillary electrophoresis
(CE) and capillary electrochromatography (CEC) or wet chemical analysis
(titration etc) [3,7]. Thus, Huck et al. introduced in 1999 a strategy, which
enables determination of plant content in a multi-plant extractive system by
analysing its corresponding leading compound (Figure 2) [19]. Furthermore,
other parameters e.g. pH, viscosity, solvent composition can be determined
simultaneously by calibrating the system with the appropriate reference method.
The suitability of this strategy of analysis was successful demonstrated by
simultaneously analysing the leading compound 3´,4´,5´-trimethoxyflavone,
water and ethanol content in a huge sample set of Flos Primulae veris [19] and
was found to be also applicable to the analysis of St. John´s Wort [20] extracts.
The biggest obstacle for quality control in TCM is the fact that there are often
many compounds (some time more than hundred) in a raw material and no
information is present about the individual health effect, which is valid for most
Traditional Chinese Medicines. In this case, the second quality control approach
by NIRS is the establishment of a qualitative cluster model, which can be
suitable for a fast authentication and identification of raw material recording to
their origin and composition, respectively. In the past, this method was shown to
be highly efficient for controlling the sort, origin and year of wines [21, 22].
3 Applications of NIR in TCM
In Austria NIRS in the analysis of medicinal plants (“phytomics”) has been
introduced at the Institute of Analytical Chemistry and Radiochemistry,
Universtiy of Innsbruck, in 1999 [19]. In parallel, approximately at the same time
NIRS was established as a novel tool for quality control in China. Now, NIRS is
7
used not only for authentication, identification and quantification of raw material,
but also for process quality control and/or extraction monitoring. Not only single
raw materials, but also complex formulas are current subjects of investigation.
TCM includes besides medicinal plants also medicine prepared from animals,
fungus and minerals; NIRS is used not only for monitoring the secondary
metabolites, but also for the determination of additional parameters, e.g., fiber,
moisture, etc. Due to short measurement times of only a few seconds, the use of
optical fiber probe makes NIRS attractive for on-line monitoring. In Figure 3 the
main application fields of NIRS in TCM are summarised including control of
raw materials
production and extraction processes
preparation of medicinal formulation,
which is described in detail in the following chapters.
3.1 Control of raw materials
Compared to chromatography, electrophoresis and MS no sample destruction is
required for NIRS. Information can be gathered from the intact entire piece of
sample. This circumstance makes NIRS the preferred tool for pattern
recognition of raw materials applied to
identify falsification
classify material into species, geographic regions
identify multi-originated raw materials
identify geographical provenance (“geoherbs”) and habits
quality control by quantitative analyses of ingredients
3.1.1 Identification of falsification
Truth or false identification is the first step in the characterisation of raw
materials. Xiang et al. [23] as well as Tang et al. [24] described the identification
of 25 official and 27 unofficial rhubarb (Rheum palmatum) samples by NIRS and
a tailored artificial neural network (ANN). Recorded spectra were compressed
by wavelength transformation (WT) and allocated into clusters. The established
model allowed identifying true/false with a selectivity of 96 % [23]. Zhao et al.
8
applied wavelength packet entropy and Fisher classification to identify medicinal
rhubarbs [25], while Zhang et al. used vector machines [26].
Zhong et al. established a cluster model of Pollen Typhae from different sources,
which is used to relieve blood stasis, stop bleeding, treat stranguria and
aponicas pain. The established model used the 2nd derivative spectra, vector
normalization and factor method for classification [27].
3.1.2 Classification into species, geographic regions
In some cases original plants or animal species and origin are unconfirmed and
disputable. NIRS based cluster analysis offers solutions to answer both
questions. Licorice (the roots of Glycyrrhizia uralensis Fisch) is used as a
medicinal herb and also as a food additive in China. The classification of licorice
samples according to their growing conditions (Figure 4a), geographic areas
(Figure 4b) and plant parts was developed using fiber optic diffuse reflection
NIR spectroscopy (FODR-NIR). With the use of multiplicative signal correlation
(MSC) and Norris derivative filtering, the differences of the NIR spectra among
different licorice samples were enhanced even though the raw spectra showed
only slight differences. The results showed that the NIR spectra of the samples
were moderately clustered in the principle component spaces. Pattern
recognition of soft independent modeling of class analogy (SIMCA) provided
satisfactory classification results. Additionally, a partial least square (PLS)
method using HPLC data set as reference was constructed to predict the value
of glycyrrhizic acid (GA) in licorice. The results showed that PLS models with
both data normalization (DN) coupled with first derivative and MSC
pretreatments provided acceptable results [28]. Liu et al. used NIRS based
cluster analysis and discriminative analysis to classify Yangti (Chinese herb
from Rumex patientia L., R aponicas Houtt, R chalepensis Mill and R dentatus
L.). The obtained NIR results fit well with the traditional phytotaxonomy [29]. Wu
et al. classified Baizhi (Angelica anomala, A dahurica, A dahurica cv. Hangbaizhi,
A dahurica cv qibaizhi, A. porphyrocaulis and A formosana) by NIRS coupled
with pattern recognition. The results showed that the elaborated NIRS method
can provide information for identifying species of these herbal medicines [30].
9
3.1.3 Identification of multi-originated raw materials
Raw materials composited of several plants or animal species deriving from
different origins are called “multi-originated”. In most cases they belong to the
same genus and have similarity in morphology, secondary metabolites and
others. To indentify each component and proof for falsification is much more
complicated than in case of a single raw material. According to TCM, the
combination of several different methods is necessary for the identification of
multi-original materials. NIRS allows to simplify this procedure applying cluster
analysis. For example, 7 certified Fritillaria species, i.e., F. przewalskii Matim, F.
cirrhosa D. Don, F. unibraacteate Hsiaaoet. K. C. Hsia, F. thunbergii Mig, F.
pallidiflora, F. ussuriensis Matin and F. kupehensis Hsiaet K. C. Hsia, and 3 fake
species, i.e., F. thunbergii Varchekiugensis Hsiaet K. C. Hsia, Tulipa edulis and
Iphigeniaindica were dried, grinded, sieved and studied by NIR applying cluster
analysis, convolution and transform-visualization-similarity analysis. In the frist
step, this method enabled differentiation between true and false species but no
assignment of individual species. Finally, convolution transform
–visualization-similarity analysis allowed to magnify and also quantify the minute
differences between the certified Fritillaria species [31]. Achillea millefolium and
3 of its related species, namely, A. clypeolata, A. collina and A. nobilis were
discriminated by principle component analysis (PCA) [32]. 42 different Cnidium
monnieri (L ) Cusson species and their origins were identified by Cai et al. [33].
3.1.4 Identification of geoherbs and habits
The term “geoherblizm” was introduced in ancient Chinese to describe raw
materials related to a certain habit and is used as a synonym for good quality. A
top-geoherb is geoherblizm with superior quality originating from preferred
geographical areas. This “geoherblizm” principle is applied as a quality standard
controlling method of TCM raw materials. The traditional identification strategy is
based on experience and in many cases difficult underlying subjective decisions.
Via NIRS geoherblizm can be characterised using cluster analysis.
Wang et al. collected 102 samples of Cordyceps from Tibet and Qinghai
province (Cordyceps is the top-geoherb of Cordyceps according to TCM
growing in Tibet), and used NIR in diffuse reflection and transmission mode for
the analysis of both its milled and extracted form, respectively. The result
10
showed that all Cordyceps samples can be allocated according to their source
[34]. Wang et al. investigated 57 samples of Panax gensing from Jilin provine
and 60 from Liaoning province (p. gensing from Jilin province is the top-geoherb
of P. gensing) by NIR diffuse reflection spectroscopy. After performing
Savitzky-Golay filtering, calculation of first and second derivative spectra, they
found more intense NIRS absorption in Jilin P. gensing than Liaoning P. gensing
due to the higher content of ingredients accompanied by smaller scattering and
shifting effects [35]. Zhang et al. identified Forsythia suspense from 5 different
habits by NIRS using pattern recognition based on SIMCA. 5 predictive models
were built separately, only 1 sample among 10 prediction samples could not be
indentified correctly [36]. Xing et al. successfully identified Red Kojic made by M.
purpureus fermentation (Fermentum Rubrum) from 18 different habits by NIR
diffuse reflection spectroscopy and cluster analysis [37]. Yi et al. used a NIRS
approach to discriminate Ganoderma lucidum according to its cultivation area.
Raw, first, and second derivative NIR spectra were compared to develop a
robust classification rule. The chemical properties of G. lucidum samples were
also investigated to find out the difference between samples from six different
origins. It could be found that the amount of polysaccharides and triterpenoid
saponins in G. lucidum samples was considerably different based on cultivation
area. Principal component analysis (PCA), discriminant partial least-squares
(DPLS) and discriminant analysis (DA) were applied to classify the geographical
origins of those samples. For the discrimination of samples from three different
provinces, DPLS provided 100% correct classifications. Moreover, for samples
from six different locations, the correct classifications of the calibration as well
as the validation data set were 96.6% using the DA method after the SNV first
derivative spectral pre-treatment (Figure 5) [38].
3.1.5 Quality control by quantitative analyses of ingredients
One important point in quality control is the presence of certain compounds at
specific concentration levels. Therefore, ingredients of interest in raw materials
or deriving extracts can be determined directly and quickly by applying
quantitative regression analysis, which is established by calibrating NIR values
against true values deriving from reference (such as gas chromatography, GC;
11
high performance liquid chromatography, HPLC; electrophoresis, EP;
supercritical fluid chromatography, SFC etc).
Yang et al. applied NIRDS and back propagation based artificial neural network
(BP-ANN) to realize fast determination of the mannitol content in a range from
8.08% to 14.55% in fermented Cordyceps sinensis powder. For modelling, first
derivative NIR spectra of fermented powder, three different multivariate
calibration strategies were employed, including principal component regression
(PCR), partial least square regression (PLSR) and BP-ANN. Furthermore, the
root mean square error of cross validation (RMSECV) and the root mean square
error of prediction (RMSEP) were selected as the indices for evaluating and
comparing the performance of calibration models. Within the wavenumber
ranges of 7.501.7~6.097.8 cm-1 and 5.453.7~4.246.5 cm-1, the obtained lower
values of RMSECV (0. 475) and RMSEP (0. 608) indicated that BP-ANN was
the superior utilisation tool [39]. Ye et al. used NIR reflectance spectroscopy for
quantifying isorhamnetin between 0.1%-0.8% in Hippophae rhamnoides Linn
from West Sichuan plateau. Calibration models were established using the PLS
(partial least squares) within the range of 12.000 – 4.000 cm-1. Different spectra
pre-treatments methods were compared. The study showed that spectral
information can be extracted thoroughly by constant off set elimination (COE)
pre-treatments method with the correlation coefficient r2 of 0.7398 , SEC of
0.107 and SEP of 0.073 ( standard deviation of the prediction sets) [40]. Bai et al.
determined ecdysterone`s content in Radix Achyranthis Bidentalae applying
(PLS). The result showed that the correlation coefficient of the quantitative
mathematics model between the prediction and the true values was 0.9489 [41].
Yang et al. detected the content of flavonoids in ginkgo leaves. The result
indicated that the relative warp is little and forecast value can be close to real if
precision is high by chemical reference analyses [42]. Liu et al. determined
arteannuin in Artemisia annua L. The corresponding model was established by
PLS. RMSEP and r2 of the validation set samples of the model based on 6
coefficients were 0.544‰ and 0.998, respectively [43].
In some cases it is necessary to detect several independent active ingredients in
a formulation simultaneously. Analyzing five components (total isoflavones,
puerarin, daidzin, starch, crude protein) components of P. Lobata (Pueraria
lobata Willd Ohwi) demonstrated that the correlation between the chemical
12
value (true value) of the five components of sample set and the NIR predicated
value was 0.9752, 0.9839, 0.9659, 0.9628 and 0.9829, respectively. The
correlation between the calibration and test set was 0.9818, 0.9752, 0.9772,
0.9737 and 0.9798, respectively [44]. 14 relevant compounds in the medicinal
plant Achillea millefolium were detected by NIRS using gas
chromatography-mass spectrometry (GC-MS) as a reference technique. PLSR
was used to create 14 single-compound models (SCM, one regression model
for each compound) on one hand and one multi-compound model (MCM, one
regression model for 14 compounds) on the other hand. SEP was 0.49 % for
SCM and 0.62 % for MCM. Paired t-test and one way analysis of variance
(ANOVA) showed both SCM and the MCM work well in quantities of the
compounds in A. millefolium. Pearson bivariate correlation, principle component
analysis (PCA) and hierarchical cluster analysis were conducted to uncover the
significant relationship between the 14 compounds [45].
3.2 Processing and extraction
In the following two chapters the suitability of NIRS to monitor the quality during
the production and extraction procedure are summarized.
3.2.1 Processing quality control (QC)
Most raw materials are further processed prior to clinical use or manufacturing
according to TCM theories and clinic practices. The commonly used processing
steps include cleaning, freezing, boiling, steaming, etc. Sometimes, vinegar,
honey, salt is added. Thereby, processing ensures dosage and removement by
purification on one hand and improvement or change of the effect on the other
hand. In many cases, the toxicity of the used raw materials is reduced. For
example, heads and legs of Chantui (periostracum cicadae, Cryptotympana
pustulata Fabricius) is removed; the small hair on leaves loquat (Eriobotrya
japonica) are brushed off in order to avoid itch of throats, Dahuang (Rhizoma et
Radix Rheum palmatum, R Tangutici and R Officinalis) is be steamed with wine
to avoid abdominal pain or receive loose bowels. It is obviously that both
physical and chemical parameters are changed. Processed and unprocessed
raw materials are declared as two different kinds of medicine in TCM practice,
13
i.e. dried Rehmannia root and streamed Rehmannia root (radix rehmanniae
preparata).
The commonly used identification methods include experienced identification,
and some simple physical or chemical identification procedures. NIRS provides
a very fast, user-friendly identification of processed materials. American ginseng
(Panax quinaquefolia) and Asiatic ginseng (Panax ginseng C. A. Mey) as well as
Asiatic ginseng processed products were analyzed by NIRS in diffuse reflection
mode. The result show that American ginseng and Asiatic ginseng are more
similar than Asiatic ginseng processed products, which indicated that
processing can change the Asiatic ginseng [46]. Bai et al. determined the
reducing sugar content in powder of decoction pieces of Shu Dihuang (radix
rehmanniae preparata) stewed with wine by FT-NIRS and data analysis. Cross
validation and test samples determination showed that the correlation coefficient
of the prediction model were 89.02 and 88.47, the RMSECV were 0.962 and
0.887, respectively. t-Test confirmed that there was no significant difference
between the true data and predictive value [47].
3.2.2 Extraction quality control
During recent years, extracts have even become more popular in TCM, because
they are easy to prepare and are known to have a good patience compliance.
The collected powder samples of Ginkgo biloba extracts were analyzed by
HPLC and NIR, followed by chemometrical data treatment. The results showed
that MPLS (modified partial least squares) regression model gave the best result.
The multi-correlation coefficient of the prediction samples was 0.973, the
recovery 96.15%-102.0%, with RSD of 1.1%. The elaborated method indicated
that the total flavones in powder of G. biloba extract could be determined directly
with high accuracy [48].
NIRS was used for measurement of water content in 5 kinds of TCM extracts,
namely Radix Scutellariae, Forsythia suspense, Flos Lonicerae, Cornu gorais
and Bear gall powder. Spectra were recorded with the average of 64 scans over
the spectral region 4.000 – 10.000 cm-1 at 8 cm-1 interval. The wavenumber
bands containing 3 characteristic wavelengths of water (6.900, 5.200, 5.180
cm-1) were selected and pretreated applying multiplicative scatter correction,
Savitzky - Golay filtering, and first derivative. The calibration was obtained
14
applying PLSR and optimized via inner cross validation and external validation.
Results showed that the coefficients of correlation of inner cross validation and
external validation were both above 0.90, and both RMSECV and RMSEP
below 0.05. The calibration models were used for testing a new set of unknown
samples, and the results were highly satisfying. The presented method is
timesaving and accurate, which indicates potential to be widely used in the
water content determination of TCM extracts [49].
Panax notoginseng herb extracts were investigated by NIR, HPLC and
colorimetric method to monitor the content of ginsenosides Rg1, Rb1, Rd and
saponins. The NIRS calibration models of ginsenoside Rg1, Rb1, Rd were built
by using support vector regression (SVR). This method was compared with
partial least square regression (PLSR) and radial-basis function neural network
(RBFNN) modeling methods. The results showed that the predictive accuracy of
NIR calibration models built by SVR was much better than that of the models
built by PLSR and RBFNN [50].
3.3 Preparation of medicinal formulation
On one hand it is important to have a tool enabling online control during
preparation according to special traditional protocols and on the other hand to
control quality of medicine prepared according to patent guidelines.
3.3.1 On line detection during manufacturing
Good manufacturing practice (GMP) is a worldwide popular modus operandi for
ensuring quality of medicine and was introduced into TCM in 1990. Quality
control based on SOP (standard operation practice) during the courses is the
key of success for GMP. The commonly used off-line detection is not only
time-costing, but also increasing the production. Therefore, NIRS offers the
additional advantage of on-line detection using fiber optics.
To study the relationship between on-line NIR spectra and off-line HPLC of
Salvia miltiorrhiza Bunge during water extracting process the representative
active components salvianolic acid B and tanshinone were analyzed. PLS was
used correlating the relationship between the information of NIR and HPLC. The
results showed that the optimum NIR wavelength range for establishing the
calibration model was 1.300-1.600 nm and 2.200-240 nm. For tanshinone IIA r2
15
=0.9427, SEC= 0.9177, with the largest absolute predication error found was
1.40%; for salvianolic acid B, r2 =0.9143, SEC= 1.1212, the largest absolute
predication error was 3.08%. The results indicated that NIR technique can be
used in the on-line detection and quality control of TCM extraction procedures
[51].
To determine glycyrrhizic acid (0.94%-3.06%) in Glycyrrhiza uralensis fisch, NIR
spectra in the range of 10.000 – 4.000 cm-1 were recorded. Calibration models
were established using the PLS (partial least squares) and PCR (principle
component regression) algorithm. Different spectra pretreatments methods
were compared. The study showed that PLS model gave better results than
PCR with the correlation coefficient 0.958, SEC 0.179 and SEP 0.197. Results
indicated that fiber optic NIR can be used to on-line control the valid component
in Chinese herbs [52].
To determine active components during the water extraction process of Paeonia
lactiflora with NIRS, HPLC was used as the reference method to determine the
content of paeoniflom. A multivariate calibration model based on PLS algorithm
was developed. Results showed that the correlation coefficient of the calibration
model was 0.9962, and the predicted coefficient was 0.9895. The RMSEC and
RMSEP were 0.109 g·L- 1 and 0.138 g·L- 1, respectively, and the RSEP 5.6%. It
indicated that NIRS is accurate and reliable, and is applicable for fast analysis
and monitoring of active components in extraction process of TCM (Figure 6.)
[53].
To realize the on-line quality control during the extraction of the multi-originated
Danshen herbal system, compound danshensu was chosen as a representative
active component. PLS was used to establish the relationship between NIR
spectra and HPLC analysis. The optimum NIR wavelength range was 9.715 –
7.082 cm- 1, R = 0.9594, RMSEC =0.0494, the average relative error was 7.2 %.
It was demonstrated that this elaborated NIR technique could be used in the
on-line quality control of compound Danshen’s extracting process [54].
To study the most suitable conditions for the extraction process of oridonin from
Rabdosia rubescens (Hemsl.) Hara, chromatographic data of oridonin content
were correlated with NIR spectra. The model allowed optimizing the extraction
procedure towards high purity of oridonin. Finally, methods advantages were
summarized being fast, safe, lower cost, simple and high reproducibility [55].
16
In order to optimize extraction and separation conditions of indirubin in Folium
Isatidis extracts, indirubin was chromatographed on an aluminium oxide column
employing chloroform-ethyl acetate as an eluent. The results showed that
indirubin extracted by this method was of high purity (97.78 %) [56].
3.3.2 Quality control of patented formulations
The forms of patented herbal preparations (PHP) include powder, extract, pill,
tablet, capsule, plaster, drip-pills, ointments, medicated-wine, injections, etc. Liu
et al. proposed a new method of NIRS combined with fuzzy neural network to
distinguish the complex chemical components of Shenmai injection (constituted
by 3 kinds processed raw materials). The results showed that the classification
accuracy reached 94.2%, obviously better than that of classical BP neural
network (84.6%) [57].
On the basis of a multivariate NIRS model, Xiaoerchoufeng powder components
scolopendra, scorpio, bombyx batryticatus, eupolyphaga seu steleophaga and
periostracum cryptotympanae, were identified using SIMCA [58].
Wang et al. established a new method for the rapid analysis of puerarin in
Xintong oral liquid (constituted by 13 kinds processed raw materials) by
acousto-optic tunable filter NIRS, HPLC was used as a reference method to
determine puerarin content. Calibration model based on PLS was developed to
correlate the spectra and reference values. The RMSEP of the model for
puerarin was 0.1371, R2 =0.9845. The correlation coefficient of the true value
and predication value from validation was r2 = 0.9964 [59].
Multiple components in PHP can be quantified by NIRS just as in a raw material.
Liu et al. provided an accurate and efficient method for the quantitative analysis
of Yuanhu Zhitongsan pulvis (constituted by 2 kinds processed raw materials).
NIR spectra of 25 simulated samples were collected and treated with BP-ANN
or PLS method. Three batches of actual samples were chosen to test the model.
The results showed that both simulated and actual samples were determined
well. SEP of Yuanhu (Rhizoma Corydalis) by BP-ANN and PLS were 1.5% and
2.5%, respectively, of Baizhi (Radix Angelicae dahuricae) are 2.9% and 4.4 %,
respectively. The detected content was 95-105% of the true value. It is feasible
to apply NIR for the quantitative analysis of Yuanhu Zhitongsan pulvis [60].
17
4 Perspective
NIRS was embodied in Chinese Pharmacopoeia (2005 Edition) as a legal
method for the quality control of TCM in 2005 [61]. Since then NIRS technology
is still developing fast and offers huge potential in TCM quality control. With the
further optimization of NIRS applications in TCM, more and more research will
focus on this technique. The reliability, applicability and popularization of NIRS
is increased, and the related questions, such as effect of presentation of the
samples set, the relationship between robustness and sensitivity of the model,
etc. are arousing more attention. So NIRS becomes more and more likely to
answer difficult questions like geographical origin or species. NIR imaging
technology will enable a high resolution investigation of TCM tissue samples
and give e.g. knowledge upon active ingredient distribution [62].
5 Acknowledgements
All authors want to thank the Austrian Ministry for Science and Research and
the Ministry of Health, Family and Youth for their financial support (Project
“Novel analytical tools for quality control in TCM”).
18
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Figure legends
Figure 1. Measurement modes in NIRS
Figure 2. Strategy of analysis to establish a calibration model in NIRS
Figure 3. Flow diagram of NIRS application fields in TCM
Figure 4. PC score plot of licorice samples originated from different (a) growing
conditions (Tandi ○, Liangdi ▲shadi and (b) geographic areas (Gansu ○, Inner
Mongolia ▲). Reproduced from reference [28], with permission.
Figure 5. Three-dimensional score plot using PC1, PC2, and PC3 for
discriminationg six Ganoderma lucidum origins, class 1, Jiaxiang; class 2,
Huangshan; class 3, Taishan; class 4, Longquan; class 5, Jinzhai; class 6,
Jingdangpu. Reproduced from reference [37], with permission.
Figure 6. Calibration model for the quantitative analysis of glycyrrhizic acid
(0.94%-3.06%) in Glycyrrhiza uralensis fisch. Reproduced from reference [52,
with permission.