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  Table of Contents  
ORIGINAL ARTICLE
Year : 2017  |  Volume : 13  |  Issue : 51  |  Page : 528-533  

Quality evaluation of raw moutan cortex using the ahp and gray correlation-TOPSIS Method


1 Department of Automation, Guangdong University of Technology, Guangzhou; College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China
2 Department of Automation, Guangdong University of Technology, Guangzhou, People's Republic of China
3 College of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China

Date of Submission05-Jun-2016
Date of Acceptance11-Jul-2016
Date of Web Publication19-Jul-2017

Correspondence Address:
Jiang Meng
College of Traditional Chinese Medicine of Guangdong Pharmaceutical University, Guangzhou Higher Education, Mega Center, Guangzhou
People's Republic of China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1296.211029

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   Abstract 

Background: Raw Moutan cortex (RMC) is an important Chinese herbal medicine. Comprehensive and objective quality evaluation of Chinese herbal medicine has been one of the most important issues in the modern herbs development. Objective: To evaluate and compare the quality of RMC using the weighted gray correlation- Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. Materials and Methods: The percentage composition of gallic acid, catechin, oxypaeoniflorin, paeoniflorin, quercetin, benzoylpaeoniflorin, paeonol in different batches of RMC was determined, and then adopting MATLAB programming to construct the gray correlation-TOPSIS assessment model for quality evaluation of RMC. Results: The quality evaluation results of model evaluation and objective evaluation were consistent, reliable, and stable. Conclusion: The model of gray correlation-TOPSIS can be well applied to the quality evaluation of traditional Chinese medicine with multiple components and has broad prospect in application.

Keywords: AHP, gray correlation, Moutan cortex, quality evaluation, TOPSIS


How to cite this article:
Zhou S, Liu B, Meng J. Quality evaluation of raw moutan cortex using the ahp and gray correlation-TOPSIS Method. Phcog Mag 2017;13:528-33

How to cite this URL:
Zhou S, Liu B, Meng J. Quality evaluation of raw moutan cortex using the ahp and gray correlation-TOPSIS Method. Phcog Mag [serial online] 2017 [cited 2020 Feb 23];13:528-33. Available from: http://www.phcog.com/text.asp?2017/13/51/528/211029

Sujuan Zhou, Bo Liu
These two authors have contributed equally to this work.




Summary

  • The experiment tries to construct a model to evaluate the quality of RMC using the weighted gray correlation- Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. Results show the model is reliable and provide a feasible way in evaluating quality of traditional Chinese medicine with multiple components.



   Introduction Top


Moutan Cortex, the root bark of Paeonia suffruticosa Andrews (Paeoniaceae), is used as a common traditional Chinese medicine (TCM) drug.[1] The RMC is prepared by collecting the root bark of P. suffruticosa, washed, cleaned, and sun-dried. According to the TCM theory, the raw moutan bark serves to clear excessive heat, cool the blood, promote blood circulation, and remove blood stasis; it is indicated for diseases associated with the “heat syndromes” (typically manifesting in inflammation and related symptoms), stagnated blood conditions, and traumatic injuries.[2],[3] For the differences of growing environments and planting ways, RMC from different producing areas are uneven in quality. In order to evaluate the quality of RMC overall, multiple components should be considered. Pharmacology researches showed that paeoniflorin, benzoylpaeoniflorin, paeonol, catechin, and oxypaeoniflorin have been reported to be able to inhibit platelet aggregation, promote blood circulation, and remove blood stasis,[4],[5] In this study, gallic acid, catechin, oxypaeoniflorin, paeoniflorin, quercetin, benzoylpaeoniflorin, and paeonol were included for efficient quality control of RMC.

The main objective of this study is to propose a systematic quality assessment model based on multiple components of RMC. Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), known as one of the most classical Multiple Attribute Decision Making (MADM) methods, is based on the idea that the chosen alternative should have the shortest distance from the positive ideal solution and on the other side the farthest distance from the negative ideal solution. But this method is mostly used in linear relationship. For nonlinear problems, this method works not so well. Gray relational analysis (GRA) can cover the shortage. GRA is applied mostly in the area of system modeling, predicting and controlling, which can solve MADM problem containing subjectivity, uncertainty, and ambiguity in assessment process and uncover the non-linear relationship. By introducing gray correlation coefficient to traditional TOPSIS and combining gray correlation coefficient and traditional Euclidean distance, a new relative closeness is built, which reflects position relation between ideal solution and negative ideal solution, and similarity difference of data curve.[6]

Different components of samples have different important weights of evaluation; this study first utilizes analytic hierarchy process (AHP)[7] to establish the assessment structure then determines the important weights of evaluation criteria according to a group of decision-makers, and last applies gray correlation-TOPSIS to obtain the final ranking order of quality.

The uses of gray correlation-TOPSIS method have been reported in the application of mining method optimization,[8] evaluation of channel width design guidelines,[9] customer value assessment,[10] and so on. However, this study is the first report to apply the method for quality evaluation of RMC. The study can add to knowledge of the quality assessment of Chinese herbal medicine.


   Materials and Methods Top


Chemicals and materials

Samples of RMC were collected from different regions of People's Republic of China shown in [Table 1] and authenticated by Dr. Shumei-Wang from the School of Chinese Medicine, Guangdong Pharmaceutical University. Seven compounds were simultaneously determinate with high-performance liquid chromatography coupled (HPLC-DAD-MS), shown in [Figure 1]. The methods and results of content determination had been reported in the article of “Liquid chromatography-diode array detector-electrospray mass spectrometry and principal components analyses of raw and processed Moutan cortex.”[3]
Table 1: Sources and components of RMC samples (mg.g-1) (n = 3)

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Figure 1: (A) HPLC chromatogram of standard solution and (B) a typical HPLC chromatogram of RMC.1, gallic acid; 2, catechin; 3, oxypaeoniflorin; 4, paeoniflorin; 5, quercetin; 6, benzoylpaeoniflorin; 7, paeonol

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Reference standards (purity > 98%) of gallic acid, catechin, oxypaeoniflorin, paeoniflorin, quercetin, paeonol, and benzoylpaeoniflorin were obtained from the National Institute for Food and Drug Control, Beijing, People's Republic of China. HPLC-grade acetonitrile (Yu-Wang Chemical Factory, Shandong, People's Republic of China), formic acid (Beijing Reagent Co., Beijing, People's Republic of China), and analytical grade methanol (East giant Experimental Instrument Co., Guangzhou, People's Republic of China) were used. Water was purified by the Milli-Q water system (Millipore, Bedford, MA, USA).[3]

Methods

The proposed model for assessment of the quality of RMC by the weighted gray correlation-TOPSIS method consists of three basic phases: (1) AHP is utilized to determine the important weights of evaluation criteria of RMC, (2) gray correlation analysis method is used to get gray correlation coefficient, and (3) TOPSIS is employed to achieve the final ranking results based on the above phases. Schematic diagram of the proposed model is explained in [Figure 2].
Figure 2: Phases of constructing assessment model.

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AHP weights

Comprehensive evaluation means overall evaluation of multicriteria. In most conditions, the important weights of attributes differ from each other. This should be taken into account when using the gray correlation-TOPSIS analysis method. And then the evaluation results would be more reliable and stable. There are many methods of determining the importance weights of evaluation criteria, including Delphi method,[11] AHP, entropy method,[12] and so on. By far, AHP is one of the frequently used methods for its simplicity and ease of use. AHP is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. It was developed by Saaty and Peniwati.[13] It has particular application in group decision making. The procedure for using the AHP can be summarized as follows[14]:

(1) Perform pair-wise comparisons

For the quality evaluation of RMC, different components of samples have different important weights of evaluation. The nine scale method [7] shown in [Table 2] is utilized to get the relative importance of each indicator.
Table 2: Nine scale method

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(2) Assess the consistency of pairwise judgments

To avoid the subjective judgment that will make the result inaccurate. Consistency check to verify the rationality of the matrix [15] is necessary. Eigenvector is calculated as the following:



Where, A represents judgment matrix, λmax represents the max eigenvalue and W represents the weight matrix.



CR should satisfy the condition as CR = CI/RI. Where, CI means values of consistency check indicators and RI means values of average stochastic coincidence indicators. A consistency index (CR) of 0.1 or less was considered acceptable.[16]

(3) Standardize the weight matrix



Gray correlation coefficient matrix

The gray correlation is one method that uses the correlation degree between the referring series and the comparing series to evaluate the proposed schemes.[17] The steps of calculating the correlation degree summarized as follows:[9]

Determination of positive and negative ideal scheme

Suppose there are n kinds of samples of Chinese herb and each sample with m evaluation index. The evaluation unit sequence is{Xij}

(i =1, 2, 3……n; j =1, 2, 3……m); in this topic, n = 10, m = 7. {X}sj represents the positive ideal scheme, while {X}tj means the negative ideal scheme: {X}sj= max (1≤i≤n) {X}ij, {X}tj= min (1≤i≤n) {X}ij

Normalization of original index value

First, the original index value should be normalized to be the value belonging to [0, 1] for making sure of the evaluation accuracy. Here adopts equalization method in dimensionless treatment to reduce the loss of information in data. So, the index after being normalized by equalization method is as follows:

Yij= Xij/ Xj

where, Xj represents the mean value of j index of the sample.

Calculation of the weighted normalized decision matrix

The weighted normalized decision matrix is constructed by multiplying the normalized decision matrix by its associated weights. The weighted normalized value is calculated as follows:

Yij = Wi×Yij, i = 1, 2, …, m; j = 1, 2, …, n;

where, Wi means the weight matrix got from AHP method.

Calculation of the gray correlation coefficient

The gray correlation coefficient on the jth index between the scheme Yij and the Ysj and Ytj respectively is and



where, Δmin= min|Yij-Ysj|, Δmax= max|Yij-Ysj| (i =1, 2……n; j =1, 2……m)



where, Δ'min= min|Yij-Ytj|,Δ'max= max|Yij-Ytj|(i =1, 2……n; j =1, 2……m),

ρϵ[0,1] ρ is introduced as the resolution coefficient to reduce the influence of the extreme value, usually values 0.5.

TOPSIS based on gray correlation degree

Considering that when using gray correlation method on comparing of different schemes, one scheme with a great correlation degree to the positive ideal scheme does not mean a relatively little correlation degree to the negative ideal scheme. So, for making sure of the accuracy of the comparison, the TOPSIS method, a preference decision method using relative closeness to consider synthetically the Euclidean distance between the evaluated scheme and both the positive and negative ideal scheme, was introduced into calculating the gray correlation degree between the evaluated scheme and both the positive and negative ideal scheme respectively.[18],[19]

(1) Determine the positive ideal and negative ideal solution.



(2) Calculate the weighted Euclidean distance.

The weighted Euclidean distance of each alternative from the ideal solution is given as follows:



(3) Calculate the relative closeness to the ideal solution. The relative closeness of the alternative Ci with respect to Di+ is defined as follows:



(4) Rank the alternatives Ci(i = 1, 2,...,m) according to the relative closeness.

The best alternatives are those that have higher value Ci because they are closer to the positive ideal value. The alternatives are ranked in descending order of the Ci index.[20]


   Results and Discussion Top


Results of AHP weights

According to the experts of Chinese medicine, the importance of different components may be group like this:

Group 1: Paeoniflorin, benzoylpaeoniflorin, paeonol

Group 2: Catechin, oxypaeoniflorin

Group 3: Quercetin, gallic acid

And then based on nine scale method, the pair-wise comparisons of different components of RMC can be represented as matrix A.

After calculating, the weight value of the seven components of RMC is shown in [Table 3].
Table 3: Weight value of components of RMC

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Result of gray correlation coefficient

Samples of RMC from different regions of People's Republic of China were analyzed by gray analysis method mentioned above. Programed using Matlab (Matrix Laboratory) R2010a, the running result was shown in [Table 4].
Table 4: Correlation coefficient of RMC compared with the best reference sequence

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Quality evaluation result of weighted gray correlation-TOPSIS method

On the basis of gray correlation coefficient result of [Table 4] and constructed by method of TOPSIS, the quality evaluation and rank result of RMC from different batches was shown in [Table 5]. Results showed that RMC from Dezhou, Baoding, Tongling, and Heza were of good quality. For contrast, a single method of GRA or TOPSIS was also adopted to evaluate quality of RMC [[Table 6]]. Difference degree was also counted to show the significance level of difference. From the contrast result, we could see the biggest difference degree of the weighted gray correlation-TOPSIS method (80.444%) was larger than GRA (36.357%) and TOPSIS (78.494). And the weighted gray correlation-TOPSIS method could evaluate the quality of RMC comprehensively and objectively.
Table 5: Quality evaluation and rank result of RMC by weighted gray correlation-TOPSIS method

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Table 6: Quality evaluation and rank result of RMC by single GRA or TOPSIS method

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   Conclusion Top


The uses of weighted gray correlation-TOPSIS method have been successfully applied in many areas as effective MADM methods. However, there is no report regarding the application of these methods for quality evaluation of RMC. This research sought to develop a comprehensive, objective method for quality evaluation of RMC and proposes a decision-making method on the basis of gray correlation degree and ideal solution, offering a new idea for quality evaluation of Chinese herb. Simulation results indicate that the decision-making method by combination of gray correlation degree and TOPSIS can get rid of errors caused by subjective factors using one single method, improve reliability of selection solution evaluation, and have a great value of application in quality evaluation of Chinese herb.

Financial support and sponsorship

This work was financially supported by the Project of the National Natural Science Foundation of People's Republic of China (No. 81102809) and the Project of Guangdong Provincial Administration of Traditional Chinese Medicine (No. 20151266). This work was also supported in part by the Natural Science Foundation of China under Grant 61472090 and Grant 61672169, in part by the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant S2013050014133, in part by the Natural Science Foundation of Guangdong under Grant 2015A030313486, in part by the Science and Technology Planning Project of Guangzhou under Grant 201605131121583.

Conflicts of interest

There are no conflicts of interest

 
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    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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