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: huangluqi@263.net

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: Christian.W.Huck@uibk.ac.at

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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.

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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

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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],

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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

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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

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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.

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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].

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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

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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;

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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

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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,

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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

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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

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=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].

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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.

25

Figure 1

26

Figure 2

27

Figure 3

28

Figure 4

29

Figure 5.

30

Figure 6