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Reliable origin identification of Scutellaria baicalensis based on terahertz time-domain spectroscopy and pattern recognition
Optik - International Journal for Light and Electron Optics 174 (2018) 7–14
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Original research article
Reliable origin identification of Scutellaria baicalensis based on terahertz time-domain spectroscopy and pattern recognition
T
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Jie Lianga, Qijia Guoa, Tianying Changa,b, , Ke Lib, Hong-Liang Cuia,c a b c
College of Instrumentation & Electrical Engineering, Jilin University, Jilin 130061, China Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
A R T IC LE I N F O
ABS TRA CT
Keywords: Terahertz time-domain spectroscopy (THzTDS) Principal component analysis Support vector machines Particle swarm optimization Scutellaria baicalensis
An effective approach for identification of the origin of Scutellaria baicalensis, an essential member of the family of Chinese herbal medicine and known to be an effective anti-inflammatory, is proposed based on terahertz time-domain spectroscopy (THz-TDS) and pattern recognition. Terahertz absorption spectra of Scutellaria baicalensis collected from its main growth areas in China, including Inner Mongolia, Shanxi and Shaanxi are investigated using the proposed method, in the frequency range from 0.2 to 1.7 THz. To reduce the dimensionality of the original spectral data and extract useful features of the data, principal component analysis is employed. The matrix of the selected principal component scores is fed into a classification model established by support vector machines. We use the particle swarm optimization to optimize the parameters of the classification model to achieve an identification rate of 95.56% for the samples, demonstrating that terahertz time-domain spectroscopy combined with particle swarm-support vector machines approach can be efficiently utilized for automatic identification of the origin of Scutellaria baicalensis.
1. Introduction Scutellaria baicalensis (SB) is a widely used Chinese herbal medicine known to be an effective anti-inflammatory [1]. In China, it is regarded as a very important herb with high medicinal value [2]. Scutellaria baicalensis is a labiatae perennial herb, whose main planting areas are Inner Mongolia, Shanxi and Shaanxi provinces in China. Due to the difference of soil and climatic conditions in different places, the content of the chemical active ingredients contained in Scutellaria baicalensis vary substantially, strongly impacting the curative effect of Scutellaria baicalensis in clinical application and prescription compatibility [3]. Therefore, reliable identification of the origin of Scutellaria baicalensis is imperative so as to provide a basis for the selection of Scutellaria baicalensis clinical herbs and improve the quality of Chinese herbal medicine. At present, the main methods employed to identify the origin of Chinese herbal medicine are infrared spectroscopy [4], high performance liquid chromatography (HPLC) [5], and electronic nose technology [6]. Li et al. identified the origin of Chinese wolfberry by infrared spectrum and artificial neural network; Xiao et al. employed the HPLC to distinguish the fingerprints of Scutellaria baicalensis from different origins; Huang et al. distinguished the Schisandrae Sphenantherae Fructus from different producing areas based on electronic nose technology. Although these methods identify the origin of Chinese herbal medicine successfully, each of these methods has drawbacks, such as low detection sensitivity, long detection time, or low resolution. In view of the
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Corresponding author at: College of Instrumentation & Electrical Engineering, Jilin University, Jilin 130061, China. E-mail address: [email protected] (T. Chang).
https://doi.org/10.1016/j.ijleo.2018.08.050 Received 6 June 2018; Accepted 14 August 2018 0030-4026/ © 2018 Elsevier GmbH. All rights reserved.
Optik - International Journal for Light and Electron Optics 174 (2018) 7–14
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shortcomings of the above detection methods, we need a new method applied to the identification of the origin of SB. Terahertz (THz) radiation is located in the spectral region 0.1–10 THz (3 mm–30 μm in wavelength) between traditional microwave and far infrared of the electromagnetic spectrum. Compared with conventional spectroscopic techniques, such as infrared spectrum and X-ray, THz spectroscopy has the unique advantages of low photon energy, which does not destroy the molecular structure by ionization; sensitivity to polar molecules; and resonant coupling to the rotational, vibrational energy levels of many molecules and the weak interaction amongst them [7]. Owing to these advantages, THz technology has important application value in biomolecule identification [8], food analysis [9,10], medicine analysis [11–13], and non-destructive testing [14,15]. Terahertz time-domain spectroscopy (THz-TDS) is a coherent detection technique with an excellent signal-to-noise ratio (SNR) and high dynamic range [16]. It can directly measure the amplitude and phase information of the terahertz wave passing through the sample, and obtain the refractive index and absorption coefficient about the sample by Fourier transform without the need for the Kramers–Kronig dispersion relationship [17]. THz-TDS has been demonstrated to be a powerful tool in drug detection, and in such an application it generally performs two separate tasks, namely, the detection of drug components and the qualitative and quantitative analysis of the mixture. Liu et al. used THz-TDS combined with fuzzy recognition to study the components of explosives and illegal drugs [18]; Zhao et al. carried out qualitative and quantitative analysis of lamivudine and zidovudine using THz-TDS [19]. Recently, THz-TDS has begun to be applied to identification of Chinese herbal medicine. These studies mainly focus on the identification of different kinds of Chinese herbal medicines [20,21]. However, the identification of the origin of the same kind of Chinese herbal medicine by THz-TDS has not been reported to date. Because of the minute differences in terahertz spectra of the same kind of the Chinese herbal medicine from different origins, as in the case of Scutellaria baicalensis, it is a technical challenge to identify the origin of Chinese herbal medicine by THz-TDS. With the development and increasing adaptation of pattern recognition technology, support vector machines (SVM) has been applied in THz spectral analysis. Support vector machines (SVM) is based on the VC dimension theory which belongs to the statistical learning theory and structural risk minimum principle [22]. It seeks the best compromise between model complexity and learning ability based on limited sample information, in order to obtain the best generalization ability. It avoids the traditional process from induction to deduction, and achieves efficient “transduction inference” from training samples to forecasting samples [23]. SVM is a robust approach to simplify the classification problem, and the method has been combined with THz-TDS and Raman spectroscopy for the identification of the geographical origin of tea [24] and cancer cells [25,26]. This paper demonstrates an efficient and reliable approach for detecting and identifying Scutellaria baicalensis from different origins, employing THz-TDS and SVM with particle swarming optimization (PSO). A classification model based on the absorption spectrum of the sample is established by combining support vector machine (SVM) with particle swarm optimization (PSO). The proposed method, called PSO-SVM hereafter, is compared with other approaches including the support vector machine combined with grid search (Grid Search-SVM) and the support vector machine combined with genetic algorithm (GA-SVM). The results showed that PSO-SVM outperforms the other methods and is, thus, an attractive potential identification tool for the automatic determination of the geographical origin of Scutellaria baicalensis. 2. Experiments and methods 2.1. Sample preparation Scutellaria baicalensis extract in powder from different origins, namely, Inner Mongolia, Shanxi, Shaanxi, are obtained from Shandong Institute of Traditional Chinese Medicine. Before sample preparation, all samples were gently ground and dried in DZF6020 vacuum drying oven at 40℃ for about four hours to remove water. Samples were pressed into tablets about 13 mm in diameter and 0.9 mm thickness under a pressure of 2 MPa. Special care was exercised to make sure that the surfaces of the tablets are smooth and parallel. A total of 135 tablet samples were prepared, with 45 for each origin. 2.2. Experimental setup The FICO THz time-domain spectroscopy system (Zomega Inc., USA) is employed in this work. The key performance indicators include: time delay from 0 to 110 ps with 0.05 ps resolution, effective spectral range of 0.1–2 THz with 11 GHz resolution, and average output power of 10–100 nW. The dynamic range is greater than 70 dB. The system has two modes, transmission and reflection, and it is configured in transmission mode in this work. The schematic diagram of the THz-TDS system is shown in Fig. 1. The terahertz beam path (red dotted line frame in Fig. 1) is enclosed in a box which is purged with dry nitrogen in order to reduce the interference of water-vapor in the air. The relative humidity of the experimental system is less than 1% and the environmental temperature is kept at around 23℃. 2.3. Data processing The physical model developed by Dorney and Duvillaret was employed to extract the THz optical parameters [27,28]. In this ∼ (ω) = n (ω)−ik (ω) , where n (ω) is the real index of refraction and k (ω) is the extinction model, material complex refractive index is n coefficient. The time domain signals from sample and reference are Esample (t ) and Eref (t ) , respectively, and their frequency domain counterparts are Esample (ω) and Eref (ω) .The system response function of the sample is given as 8
Optik - International Journal for Light and Electron Optics 174 (2018) 7–14
J. Liang et al.
Fig. 1. Experimental setup for transmission THz time domain spectroscopy (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
H (ω) =
Esample (ω) Eref (ω)
= ρ (ω) exp ⎡−iϕ (ω) ⎤ ⎥ ⎢ ⎦ ⎣
(1)
Where ρ (ω) is the amplitude ratio of the sample signal to the reference signal; ϕ (ω) is the phase difference between the sample signal and the reference signal; ω is angular frequency. The refractive index n (ω) , extinction coefficient k(ω), and absorption coefficient α(ω) can be expressed as
n(ω) =
ϕ (ω) c +1 ωd
(2)
4n(ω) ⎫ k(ω) = In ⎧ ⎨ (ω)[n(ω) + 1]2 ⎬ ρ ⎩ ⎭ α (ω) =
(3)
2k (ω) ω 2 4n (ω) = ln { } c d ρ (ω)[n (ω) + 1]2
(4)
Where c is the vacuum speed of light; d is the sample thickness. 3. Results and discussion The absorption spectra of Scutellaria baicalensis from Inner Mongolia, Shanxi, Shaanxi are obtained, as shown in Fig. 2. Two obvious absorption peaks centered about 1.068 THz and 1.664 THz are observed from the absorption curves, i.e., the samples from different origins share the same absorption peaks. As a result, such spectral features alone cannot be used directly to identify the samples of different origins. In order to distinguish the absorption spectra of samples from different origins, we employ patternrecognition method. Specifically, we use SVM to establish the classification model on the origin of Scutellaria baicalensis. In order to improve the prediction accuracy of the classification model, it is necessary to analyze all spectral data by principal component analysis (PCA) [29,30] before SVM. PCA is a classical data processing method exploited to reduce the dimensionality and remove overlapping information of the data set, through which, features of the data can be accentuated by its principal components. In practice, principal components are uncorrelated and arranged in descending order of variance. When the cumulative variance contribution rate of the first k principal components is more than 85%, the first k principal components can instead of the original data set. In the present work, a total of 135 absorption spectra in the spectral range between 0.2–1.7 THz, consisting of 45 from each origin, are used as the initial input for PCA. Three principal components (PC1, PC2, PC3) are obtained with a distribution of 65.23%, 15.82% and 9.45%, respectively. The total contribution rate of the three principal components is up to 90.5%, thus the first three principal components are deemed to largely represent the spectral features of the original absorption spectra. A three-dimensional map of the first three principal components is depicted in Fig. 3. After performing the PCA, a new data matrix (135 × 3) which replaces the original spectral data matrix is fed into the SVM to establish the classification model. In SVM, the data set is divided into two categories, a train category and a prediction category. The
Fig. 2. THz absorption spectra of Scutellaria baicalensis of different origins. 9
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Fig. 3. Three-dimensional map of the first three principal components PC1, PC2, PC3.
former contains 90 sets of spectral data randomly selected from the tree origins, with 30 sets from each, the remaining 15 sets of data in each origin make up the latter, prediction category. The performance of the classification model depends primarily on the penalty parameter C and the Radial Basis Function (RBF) kernel parameter g. In order to achieve the desired classification effect, it is very important to select the appropriate model parameters. Grid search is applied to optimize the parameter C and g firstly, which establishes a Grid-search SVM model. The isoline and result of SVM parameter selection by Grid search are shown in Figs. 4 and 5. In order to improve the prediction accuracy of the classification model, better optimization algorithms should be used to optimize the parameters C and g. Genetic algorithm (GA) and particle swarm optimization (PSO) are introduced to search for the best combination of parameters (C and g). Both GA and PSO belong to the class of heuristic algorithms. GA is inspired by the change in inherited characteristics of living organisms over successive generations, which starts the search process from a randomly generated initial population bi (i = 1,2, ⋯, n) . GA encodes all the data of a search space into a simple string called a chromosome, which is usually of a fixed length. Each chromosome has a fitness value. GA is commonly suitable for solving optimization problems [31,32]. The process of optimizing SVM parameters by GA is as follows: Step 1: Initialize the population to produce individuals (C, g) and compute the fitness value of each individual Step 2: Use selection, crossover and mutation to manage the group of the current generation and select several individuals with the best adaptation to next generation Step 3: Use crossover and mutation to manage the group of the current generation and produce the group of the next generation. Step 4: Step 2 to Step 3 is repeated several times until the required criterion is satisfied. New population progresses by taking the solutions from the preceding populations. PSO is inspired by the dynamics of social interaction amongst group of animals. It starts with a group of particles represented a possible optimization scheme, and then searches the optimal solution by iteration. Each particle is characterized by two properties, i.e., position (x) and velocity (v), respectively. The x refers to the distance between the particles and the ideal optimum solution. The v represents the movement of the particle in the solution space [33]. In each iteration, the particle updates itself by tracking two “extremes”, which is p* and g*. The p* represents the personal best position of a particle through the iterations of the optimization, while g* represents the best position achieved by the whole swarm [34]. When the two optimal values are found, the particles update their new position and speed according to the following formula:
Fig. 4. Isoline of SVM parameter selection. 10
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Fig. 5. Result of SVM parameter selection.
x ik + 1 = x ik + vik + 1
(5)
vik + 1 = vik + α1 r1, i (p* −x ik ) + α2 r2, i (g *−x ik )
(6)
vik
Where x ik and are the position and velocity of the ith particle in the kth iteration, r1, i and r2, i are uniformly distributed parameters of the ith particle, α1 and α2 are acceleration constants. The process of optimizing SVM parameters by PSO is as follows: Step 1: Initialize the locations x i and the velocity vi of the particles. Step 2: Compute the fitness values of each particle and determine g* having the lowest value for the objective function. Step 3: Update the location ( x i ) and velocity (vi ) of each particle according to formula (5) and (6). Step 4: Evaluate objective function at new location x ik + 1 and determine p*. Step 5: Repeat Step 3 to Step 4 for the number of times predefined. Step 6: Update the current g*. Step 7: Repeat Step 1 to Step 6 for the number of times predefined. Compared with GA, the rule of PSO is simpler. Furthermore, PSO does not have “crossover” and “mutation” operations, and it finds the global optimum by following the current best value of the search. Thus PSO has the advantages of easy realization, high precision, and fast convergence. The fitness curves of GA and PSO are shown in Figs. 6 and 7. The optimization results of the parameter C and g by Grid search, genetic algorithm and particle swarm optimization are shown in Table 1. Among these three methods, 3-fold cross-validation is employed, resulting in an optimal solution for C and g, with the values 0.0625 and 0.25, 0.41037 and 21.1401, 1 and 1, respectively. The accuracy of the train set is up to 100% in the three methods. The classification results using Grid-search SVM, GA-SVM and PSO-SVM are shown in Tables 2–4, respectively. The mean predicted accuracy in train set reaches 100% in all cases. But the mean predicted accuracy of the test set is different, being 82.22% for Grid-search SVM, 93.33% for GA-SVM, and 95.56% for PSO-SVM. In Grid-search SVM, two samples of Inner Mongolia were wrongly identified as Shanxi and Shaanxi respectively, two samples of Shanxi were wrongly identified as Inner Mongolia and one is mistaken as Shaanxi respectively, three samples of Shaanxi was wrongly identified which one is mistaken for Inner Mongolia and two are
Fig. 6. Fitness curve of GA. 11
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Fig. 7. Fitness curve of PSO. Table 1 Parameters of Grid Search, GA and PSO. Method
Best C
Best g
Population
Iteration
Cross Validation
Train Set Accuracy
Training Time (s)
Grid Search GA PSO
0.0625 0.41037 1
0.25 21.1401 1
20 20 20
200 200 200
3 3 3
100% (90/90) 100% (90/90) 100% (90/90)
3.3609 7.8978 6.2613
Table 2 Classification results of Grid Search-SVM. Type
Predicted Group Membership Inner Mongolia
Shanxi
Shaanxi
Correct
Accuracy (%)
Mean Predicted Accuracy (%)
Train Set Inner Mongolia(30) Shanxi (30) Shaanxi (30)
30 0 0
0 30 0
0 0 30
30 30 30
100% 100% 100%
100%
Test Set Inner Mongolia(15) Shanxi (15) Shaanxi (15)
13 2 1
1 12 2
1 1 12
13 12 12
86.66% 80% 80%
82.22%
Table 3 Classification results of GA-SVM. Type
Predicted Group Membership Inner Mongolia
Shanxi
Shaanxi
Correct
Accuracy (%)
Mean Predicted Accuracy (%)
Train Set Inner Mongolia(30) Shanxi (30) Shaanxi (30)
30 0 0
0 30 0
0 0 30
30 30 30
100% 100% 100%
100%
Test Set Inner Mongolia(15) Shanxi (15) Shaanxi (15)
13 1 0
1 14 0
1 0 15
13 14 15
86.66% 93.33% 100%
93.33%
mistaken as Shanxi. With GA-SVM, two samples of Inner Mongolia were wrongly identified as Shanxi and Shaanxi, respectively; one sample of Shanxi was mistaken for Inner Mongolia. In the case of PSO-SVM, one sample of Inner Mongolia was mistaken for Shanxi and one sample of Shanxi was mistaken for Inner Mongolia.
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Table 4 Classification results of PSO-SVM. Type
Predicted Group Membership Inner Mongolia
Shanxi
Shaanxi
Correct
Accuracy (%)
Mean Predicted Accuracy (%)
Train Set Inner Mongolia (30) Shanxi (30) Shaanxi (30)
30 0 0
0 30 0
0 0 30
30 30 30
100% 100% 100%
100%
Test Set Inner Mongolia(15) Shanxi (15) Shaanxi (15)
14 1 0
1 14 0
0 0 15
14 14 15
93.33% 93.33% 100%
95.56%
In contrast with GA-SVM, PSO-SVM has faster convergence and higher prediction accuracy. Compared with Grid Search-SVM, although Grid Search-SVM has faster convergence, but PSO-SVM is much more accurate. Considering the three methods jointly, it is apparent that PSO-SVM is more suitable for the identification of different origins of Scutellaria baicalensis. 4. Conclusions In this paper, terahertz time domain spectroscopy combined with SVM has been applied to establish a classifier for different origins of Scutellaria baicalensis. In order to achieve dimensionality reduction and feature extraction for the original absorption spectra, principal component analysis is used first. The classifier is established by SVM based on the result of the PCA. GA and PSO are introduced to optimize the parameter of SVM. Experimental results demonstrate that THz-TDS combined with the proposed PSO-SVM model can achieve an effective identification of different origins of Scutellaria baicalensis, at a 95.56% identification rate. The proposed approach improves the identification accuracy of different origins of Scutellaria baicalensis, and it can also be used for other Chinese herbal medicines. Thus the new approach is expected to be widely applicable in quality control of Chinese herbal medicine, based on an accurate determination of its origin. Conflict of interest The author declare that there is no conflict of interests regarding the publication of this paper. Acknowledgements This work was supported by the Ministry of Science and Technology of the People's Republic of China (2015CB755401); National Natural Science Foundation of China (61705120); Innovation Program of Shandong Academy of Sciences; Department of Science & Technology of Shandong Province (2016GGX101010, 2017GGX10108); Youth Science Funds of Shandong Academy of Sciences (2017QN0015) References [1] T. Shimizu, N. Shibuya, Y. Narukawa, N. Oshima, N. Hada, Synergistic effect of baicalein, wogonin and oroxylin A mixture: multistep inhibition of the NF-kB signalling pathway contributes to an anti-inflammatory effect of Scutellaria root flavonoids, J. Nat. Med. 72 (2017) 181–191. [2] B.P. Gaire, J. Song, S.H. Lee, H. Kim, Neuroprotective effect of four flavonoids in the root of Scutellaria baicalensis Georgi, Planta Med. 78 (2012) 1128–1129. [3] H. Jang, B. Jeong, S.R. Bhandari, Y. Cho, Y. Lee, Genetic and environmental variations in baicalin, baicalein and wogonin contents in Scutellaria baicalensis, Planta Med. 77 (2011) 1390. [4] Z. Li, M.D. Liu, S.X. Ji, The identification of the origin of Chinese wolfberry based on infrared spectral technology and the artificial neural network, Spectrosc. Spectral. Anal. 36 (2016) 720–723. [5] R. Xiao, Z.F. Yuan, C.Y. Wang, L.T. Zhang, C.H. Wang, Fingerprints of Scutellaria baicalensis from different habitats by HPLC, Chin. Tradit. Herbal Drugs 5 (2005) 743–747. [6] D.D. Huang, W.W. He, L. Jin, Distinguish schisandrae sphenantherae fructus from different producing areas based on electronic nose technology, Chin. J. ETMF 23 (2017) 22–26. [7] M.P. Dinca, A. Leca, D. Apostol, M. Mernea, O. Calborean, Transmission THz time domain system for biomolecules spectroscopy, J. Optoelectron. Adv. Mater. 12 (2010) 110–114. [8] T. Chen, Z. Li, W. Mo, Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition, Spectrochim. Acta A 106 (2013) 48–53. [9] H.J. Shin, S.W. Choi, G. Ok, Qualitative identification of food materials by complex refractive index mapping in the terahertz range, Food Chem. 245 (2018) 282–288. [10] Z. Chen, Z. Zhang, R. Zhu, Y. Xiang, Y. Yang, Application of terahertz time-domain spectroscopy combined with chemometrics to quantitative analysis of imidacloprid in rice samples, J. Quant. Spectrosc. Radiat. Transf. 167 (2015) 1–9. [11] Z. Yan, D. Hou, P. Huang, B. Cao, G. Zhang, Terahertz spectroscopic investigation of L-glutamic acid and L-tyrosine, Meas. Sci. Technol. 19 (2008) 015602. [12] Y. Ma, Q. Wang, L. Li, PLS model investigation of thiabendazole based on THz spectrum, J. Quant. Spectrosc. Radiat. Transf. 117 (2013) 7–14. [13] J. Choi, S.Y. Ryu, W.S. Kwon, K.S. Kim, S. Kim, Compound explosives detection and component analysis via terahertz time-domain spectroscopy, J. Opt. Soc. Korea 17 (2013) 454–460. [14] J. Zhang, W. Li, H.L. Cui, C. Shi, X. Han, Nondestructive evaluation of carbon fiber reinforced polymer composites using reflective terahertz imaging, Sensors
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Original research article
Reliable origin identification of Scutellaria baicalensis based on terahertz time-domain spectroscopy and pattern recognition
T
⁎
Jie Lianga, Qijia Guoa, Tianying Changa,b, , Ke Lib, Hong-Liang Cuia,c a b c
College of Instrumentation & Electrical Engineering, Jilin University, Jilin 130061, China Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250014, China Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
A R T IC LE I N F O
ABS TRA CT
Keywords: Terahertz time-domain spectroscopy (THzTDS) Principal component analysis Support vector machines Particle swarm optimization Scutellaria baicalensis
An effective approach for identification of the origin of Scutellaria baicalensis, an essential member of the family of Chinese herbal medicine and known to be an effective anti-inflammatory, is proposed based on terahertz time-domain spectroscopy (THz-TDS) and pattern recognition. Terahertz absorption spectra of Scutellaria baicalensis collected from its main growth areas in China, including Inner Mongolia, Shanxi and Shaanxi are investigated using the proposed method, in the frequency range from 0.2 to 1.7 THz. To reduce the dimensionality of the original spectral data and extract useful features of the data, principal component analysis is employed. The matrix of the selected principal component scores is fed into a classification model established by support vector machines. We use the particle swarm optimization to optimize the parameters of the classification model to achieve an identification rate of 95.56% for the samples, demonstrating that terahertz time-domain spectroscopy combined with particle swarm-support vector machines approach can be efficiently utilized for automatic identification of the origin of Scutellaria baicalensis.
1. Introduction Scutellaria baicalensis (SB) is a widely used Chinese herbal medicine known to be an effective anti-inflammatory [1]. In China, it is regarded as a very important herb with high medicinal value [2]. Scutellaria baicalensis is a labiatae perennial herb, whose main planting areas are Inner Mongolia, Shanxi and Shaanxi provinces in China. Due to the difference of soil and climatic conditions in different places, the content of the chemical active ingredients contained in Scutellaria baicalensis vary substantially, strongly impacting the curative effect of Scutellaria baicalensis in clinical application and prescription compatibility [3]. Therefore, reliable identification of the origin of Scutellaria baicalensis is imperative so as to provide a basis for the selection of Scutellaria baicalensis clinical herbs and improve the quality of Chinese herbal medicine. At present, the main methods employed to identify the origin of Chinese herbal medicine are infrared spectroscopy [4], high performance liquid chromatography (HPLC) [5], and electronic nose technology [6]. Li et al. identified the origin of Chinese wolfberry by infrared spectrum and artificial neural network; Xiao et al. employed the HPLC to distinguish the fingerprints of Scutellaria baicalensis from different origins; Huang et al. distinguished the Schisandrae Sphenantherae Fructus from different producing areas based on electronic nose technology. Although these methods identify the origin of Chinese herbal medicine successfully, each of these methods has drawbacks, such as low detection sensitivity, long detection time, or low resolution. In view of the
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Corresponding author at: College of Instrumentation & Electrical Engineering, Jilin University, Jilin 130061, China. E-mail address: [email protected] (T. Chang).
https://doi.org/10.1016/j.ijleo.2018.08.050 Received 6 June 2018; Accepted 14 August 2018 0030-4026/ © 2018 Elsevier GmbH. All rights reserved.
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shortcomings of the above detection methods, we need a new method applied to the identification of the origin of SB. Terahertz (THz) radiation is located in the spectral region 0.1–10 THz (3 mm–30 μm in wavelength) between traditional microwave and far infrared of the electromagnetic spectrum. Compared with conventional spectroscopic techniques, such as infrared spectrum and X-ray, THz spectroscopy has the unique advantages of low photon energy, which does not destroy the molecular structure by ionization; sensitivity to polar molecules; and resonant coupling to the rotational, vibrational energy levels of many molecules and the weak interaction amongst them [7]. Owing to these advantages, THz technology has important application value in biomolecule identification [8], food analysis [9,10], medicine analysis [11–13], and non-destructive testing [14,15]. Terahertz time-domain spectroscopy (THz-TDS) is a coherent detection technique with an excellent signal-to-noise ratio (SNR) and high dynamic range [16]. It can directly measure the amplitude and phase information of the terahertz wave passing through the sample, and obtain the refractive index and absorption coefficient about the sample by Fourier transform without the need for the Kramers–Kronig dispersion relationship [17]. THz-TDS has been demonstrated to be a powerful tool in drug detection, and in such an application it generally performs two separate tasks, namely, the detection of drug components and the qualitative and quantitative analysis of the mixture. Liu et al. used THz-TDS combined with fuzzy recognition to study the components of explosives and illegal drugs [18]; Zhao et al. carried out qualitative and quantitative analysis of lamivudine and zidovudine using THz-TDS [19]. Recently, THz-TDS has begun to be applied to identification of Chinese herbal medicine. These studies mainly focus on the identification of different kinds of Chinese herbal medicines [20,21]. However, the identification of the origin of the same kind of Chinese herbal medicine by THz-TDS has not been reported to date. Because of the minute differences in terahertz spectra of the same kind of the Chinese herbal medicine from different origins, as in the case of Scutellaria baicalensis, it is a technical challenge to identify the origin of Chinese herbal medicine by THz-TDS. With the development and increasing adaptation of pattern recognition technology, support vector machines (SVM) has been applied in THz spectral analysis. Support vector machines (SVM) is based on the VC dimension theory which belongs to the statistical learning theory and structural risk minimum principle [22]. It seeks the best compromise between model complexity and learning ability based on limited sample information, in order to obtain the best generalization ability. It avoids the traditional process from induction to deduction, and achieves efficient “transduction inference” from training samples to forecasting samples [23]. SVM is a robust approach to simplify the classification problem, and the method has been combined with THz-TDS and Raman spectroscopy for the identification of the geographical origin of tea [24] and cancer cells [25,26]. This paper demonstrates an efficient and reliable approach for detecting and identifying Scutellaria baicalensis from different origins, employing THz-TDS and SVM with particle swarming optimization (PSO). A classification model based on the absorption spectrum of the sample is established by combining support vector machine (SVM) with particle swarm optimization (PSO). The proposed method, called PSO-SVM hereafter, is compared with other approaches including the support vector machine combined with grid search (Grid Search-SVM) and the support vector machine combined with genetic algorithm (GA-SVM). The results showed that PSO-SVM outperforms the other methods and is, thus, an attractive potential identification tool for the automatic determination of the geographical origin of Scutellaria baicalensis. 2. Experiments and methods 2.1. Sample preparation Scutellaria baicalensis extract in powder from different origins, namely, Inner Mongolia, Shanxi, Shaanxi, are obtained from Shandong Institute of Traditional Chinese Medicine. Before sample preparation, all samples were gently ground and dried in DZF6020 vacuum drying oven at 40℃ for about four hours to remove water. Samples were pressed into tablets about 13 mm in diameter and 0.9 mm thickness under a pressure of 2 MPa. Special care was exercised to make sure that the surfaces of the tablets are smooth and parallel. A total of 135 tablet samples were prepared, with 45 for each origin. 2.2. Experimental setup The FICO THz time-domain spectroscopy system (Zomega Inc., USA) is employed in this work. The key performance indicators include: time delay from 0 to 110 ps with 0.05 ps resolution, effective spectral range of 0.1–2 THz with 11 GHz resolution, and average output power of 10–100 nW. The dynamic range is greater than 70 dB. The system has two modes, transmission and reflection, and it is configured in transmission mode in this work. The schematic diagram of the THz-TDS system is shown in Fig. 1. The terahertz beam path (red dotted line frame in Fig. 1) is enclosed in a box which is purged with dry nitrogen in order to reduce the interference of water-vapor in the air. The relative humidity of the experimental system is less than 1% and the environmental temperature is kept at around 23℃. 2.3. Data processing The physical model developed by Dorney and Duvillaret was employed to extract the THz optical parameters [27,28]. In this ∼ (ω) = n (ω)−ik (ω) , where n (ω) is the real index of refraction and k (ω) is the extinction model, material complex refractive index is n coefficient. The time domain signals from sample and reference are Esample (t ) and Eref (t ) , respectively, and their frequency domain counterparts are Esample (ω) and Eref (ω) .The system response function of the sample is given as 8
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Fig. 1. Experimental setup for transmission THz time domain spectroscopy (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
H (ω) =
Esample (ω) Eref (ω)
= ρ (ω) exp ⎡−iϕ (ω) ⎤ ⎥ ⎢ ⎦ ⎣
(1)
Where ρ (ω) is the amplitude ratio of the sample signal to the reference signal; ϕ (ω) is the phase difference between the sample signal and the reference signal; ω is angular frequency. The refractive index n (ω) , extinction coefficient k(ω), and absorption coefficient α(ω) can be expressed as
n(ω) =
ϕ (ω) c +1 ωd
(2)
4n(ω) ⎫ k(ω) = In ⎧ ⎨ (ω)[n(ω) + 1]2 ⎬ ρ ⎩ ⎭ α (ω) =
(3)
2k (ω) ω 2 4n (ω) = ln { } c d ρ (ω)[n (ω) + 1]2
(4)
Where c is the vacuum speed of light; d is the sample thickness. 3. Results and discussion The absorption spectra of Scutellaria baicalensis from Inner Mongolia, Shanxi, Shaanxi are obtained, as shown in Fig. 2. Two obvious absorption peaks centered about 1.068 THz and 1.664 THz are observed from the absorption curves, i.e., the samples from different origins share the same absorption peaks. As a result, such spectral features alone cannot be used directly to identify the samples of different origins. In order to distinguish the absorption spectra of samples from different origins, we employ patternrecognition method. Specifically, we use SVM to establish the classification model on the origin of Scutellaria baicalensis. In order to improve the prediction accuracy of the classification model, it is necessary to analyze all spectral data by principal component analysis (PCA) [29,30] before SVM. PCA is a classical data processing method exploited to reduce the dimensionality and remove overlapping information of the data set, through which, features of the data can be accentuated by its principal components. In practice, principal components are uncorrelated and arranged in descending order of variance. When the cumulative variance contribution rate of the first k principal components is more than 85%, the first k principal components can instead of the original data set. In the present work, a total of 135 absorption spectra in the spectral range between 0.2–1.7 THz, consisting of 45 from each origin, are used as the initial input for PCA. Three principal components (PC1, PC2, PC3) are obtained with a distribution of 65.23%, 15.82% and 9.45%, respectively. The total contribution rate of the three principal components is up to 90.5%, thus the first three principal components are deemed to largely represent the spectral features of the original absorption spectra. A three-dimensional map of the first three principal components is depicted in Fig. 3. After performing the PCA, a new data matrix (135 × 3) which replaces the original spectral data matrix is fed into the SVM to establish the classification model. In SVM, the data set is divided into two categories, a train category and a prediction category. The
Fig. 2. THz absorption spectra of Scutellaria baicalensis of different origins. 9
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Fig. 3. Three-dimensional map of the first three principal components PC1, PC2, PC3.
former contains 90 sets of spectral data randomly selected from the tree origins, with 30 sets from each, the remaining 15 sets of data in each origin make up the latter, prediction category. The performance of the classification model depends primarily on the penalty parameter C and the Radial Basis Function (RBF) kernel parameter g. In order to achieve the desired classification effect, it is very important to select the appropriate model parameters. Grid search is applied to optimize the parameter C and g firstly, which establishes a Grid-search SVM model. The isoline and result of SVM parameter selection by Grid search are shown in Figs. 4 and 5. In order to improve the prediction accuracy of the classification model, better optimization algorithms should be used to optimize the parameters C and g. Genetic algorithm (GA) and particle swarm optimization (PSO) are introduced to search for the best combination of parameters (C and g). Both GA and PSO belong to the class of heuristic algorithms. GA is inspired by the change in inherited characteristics of living organisms over successive generations, which starts the search process from a randomly generated initial population bi (i = 1,2, ⋯, n) . GA encodes all the data of a search space into a simple string called a chromosome, which is usually of a fixed length. Each chromosome has a fitness value. GA is commonly suitable for solving optimization problems [31,32]. The process of optimizing SVM parameters by GA is as follows: Step 1: Initialize the population to produce individuals (C, g) and compute the fitness value of each individual Step 2: Use selection, crossover and mutation to manage the group of the current generation and select several individuals with the best adaptation to next generation Step 3: Use crossover and mutation to manage the group of the current generation and produce the group of the next generation. Step 4: Step 2 to Step 3 is repeated several times until the required criterion is satisfied. New population progresses by taking the solutions from the preceding populations. PSO is inspired by the dynamics of social interaction amongst group of animals. It starts with a group of particles represented a possible optimization scheme, and then searches the optimal solution by iteration. Each particle is characterized by two properties, i.e., position (x) and velocity (v), respectively. The x refers to the distance between the particles and the ideal optimum solution. The v represents the movement of the particle in the solution space [33]. In each iteration, the particle updates itself by tracking two “extremes”, which is p* and g*. The p* represents the personal best position of a particle through the iterations of the optimization, while g* represents the best position achieved by the whole swarm [34]. When the two optimal values are found, the particles update their new position and speed according to the following formula:
Fig. 4. Isoline of SVM parameter selection. 10
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Fig. 5. Result of SVM parameter selection.
x ik + 1 = x ik + vik + 1
(5)
vik + 1 = vik + α1 r1, i (p* −x ik ) + α2 r2, i (g *−x ik )
(6)
vik
Where x ik and are the position and velocity of the ith particle in the kth iteration, r1, i and r2, i are uniformly distributed parameters of the ith particle, α1 and α2 are acceleration constants. The process of optimizing SVM parameters by PSO is as follows: Step 1: Initialize the locations x i and the velocity vi of the particles. Step 2: Compute the fitness values of each particle and determine g* having the lowest value for the objective function. Step 3: Update the location ( x i ) and velocity (vi ) of each particle according to formula (5) and (6). Step 4: Evaluate objective function at new location x ik + 1 and determine p*. Step 5: Repeat Step 3 to Step 4 for the number of times predefined. Step 6: Update the current g*. Step 7: Repeat Step 1 to Step 6 for the number of times predefined. Compared with GA, the rule of PSO is simpler. Furthermore, PSO does not have “crossover” and “mutation” operations, and it finds the global optimum by following the current best value of the search. Thus PSO has the advantages of easy realization, high precision, and fast convergence. The fitness curves of GA and PSO are shown in Figs. 6 and 7. The optimization results of the parameter C and g by Grid search, genetic algorithm and particle swarm optimization are shown in Table 1. Among these three methods, 3-fold cross-validation is employed, resulting in an optimal solution for C and g, with the values 0.0625 and 0.25, 0.41037 and 21.1401, 1 and 1, respectively. The accuracy of the train set is up to 100% in the three methods. The classification results using Grid-search SVM, GA-SVM and PSO-SVM are shown in Tables 2–4, respectively. The mean predicted accuracy in train set reaches 100% in all cases. But the mean predicted accuracy of the test set is different, being 82.22% for Grid-search SVM, 93.33% for GA-SVM, and 95.56% for PSO-SVM. In Grid-search SVM, two samples of Inner Mongolia were wrongly identified as Shanxi and Shaanxi respectively, two samples of Shanxi were wrongly identified as Inner Mongolia and one is mistaken as Shaanxi respectively, three samples of Shaanxi was wrongly identified which one is mistaken for Inner Mongolia and two are
Fig. 6. Fitness curve of GA. 11
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Fig. 7. Fitness curve of PSO. Table 1 Parameters of Grid Search, GA and PSO. Method
Best C
Best g
Population
Iteration
Cross Validation
Train Set Accuracy
Training Time (s)
Grid Search GA PSO
0.0625 0.41037 1
0.25 21.1401 1
20 20 20
200 200 200
3 3 3
100% (90/90) 100% (90/90) 100% (90/90)
3.3609 7.8978 6.2613
Table 2 Classification results of Grid Search-SVM. Type
Predicted Group Membership Inner Mongolia
Shanxi
Shaanxi
Correct
Accuracy (%)
Mean Predicted Accuracy (%)
Train Set Inner Mongolia(30) Shanxi (30) Shaanxi (30)
30 0 0
0 30 0
0 0 30
30 30 30
100% 100% 100%
100%
Test Set Inner Mongolia(15) Shanxi (15) Shaanxi (15)
13 2 1
1 12 2
1 1 12
13 12 12
86.66% 80% 80%
82.22%
Table 3 Classification results of GA-SVM. Type
Predicted Group Membership Inner Mongolia
Shanxi
Shaanxi
Correct
Accuracy (%)
Mean Predicted Accuracy (%)
Train Set Inner Mongolia(30) Shanxi (30) Shaanxi (30)
30 0 0
0 30 0
0 0 30
30 30 30
100% 100% 100%
100%
Test Set Inner Mongolia(15) Shanxi (15) Shaanxi (15)
13 1 0
1 14 0
1 0 15
13 14 15
86.66% 93.33% 100%
93.33%
mistaken as Shanxi. With GA-SVM, two samples of Inner Mongolia were wrongly identified as Shanxi and Shaanxi, respectively; one sample of Shanxi was mistaken for Inner Mongolia. In the case of PSO-SVM, one sample of Inner Mongolia was mistaken for Shanxi and one sample of Shanxi was mistaken for Inner Mongolia.
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Table 4 Classification results of PSO-SVM. Type
Predicted Group Membership Inner Mongolia
Shanxi
Shaanxi
Correct
Accuracy (%)
Mean Predicted Accuracy (%)
Train Set Inner Mongolia (30) Shanxi (30) Shaanxi (30)
30 0 0
0 30 0
0 0 30
30 30 30
100% 100% 100%
100%
Test Set Inner Mongolia(15) Shanxi (15) Shaanxi (15)
14 1 0
1 14 0
0 0 15
14 14 15
93.33% 93.33% 100%
95.56%
In contrast with GA-SVM, PSO-SVM has faster convergence and higher prediction accuracy. Compared with Grid Search-SVM, although Grid Search-SVM has faster convergence, but PSO-SVM is much more accurate. Considering the three methods jointly, it is apparent that PSO-SVM is more suitable for the identification of different origins of Scutellaria baicalensis. 4. Conclusions In this paper, terahertz time domain spectroscopy combined with SVM has been applied to establish a classifier for different origins of Scutellaria baicalensis. In order to achieve dimensionality reduction and feature extraction for the original absorption spectra, principal component analysis is used first. The classifier is established by SVM based on the result of the PCA. GA and PSO are introduced to optimize the parameter of SVM. Experimental results demonstrate that THz-TDS combined with the proposed PSO-SVM model can achieve an effective identification of different origins of Scutellaria baicalensis, at a 95.56% identification rate. The proposed approach improves the identification accuracy of different origins of Scutellaria baicalensis, and it can also be used for other Chinese herbal medicines. Thus the new approach is expected to be widely applicable in quality control of Chinese herbal medicine, based on an accurate determination of its origin. Conflict of interest The author declare that there is no conflict of interests regarding the publication of this paper. Acknowledgements This work was supported by the Ministry of Science and Technology of the People's Republic of China (2015CB755401); National Natural Science Foundation of China (61705120); Innovation Program of Shandong Academy of Sciences; Department of Science & Technology of Shandong Province (2016GGX101010, 2017GGX10108); Youth Science Funds of Shandong Academy of Sciences (2017QN0015) References [1] T. Shimizu, N. Shibuya, Y. Narukawa, N. Oshima, N. Hada, Synergistic effect of baicalein, wogonin and oroxylin A mixture: multistep inhibition of the NF-kB signalling pathway contributes to an anti-inflammatory effect of Scutellaria root flavonoids, J. Nat. Med. 72 (2017) 181–191. [2] B.P. Gaire, J. Song, S.H. Lee, H. Kim, Neuroprotective effect of four flavonoids in the root of Scutellaria baicalensis Georgi, Planta Med. 78 (2012) 1128–1129. [3] H. Jang, B. Jeong, S.R. Bhandari, Y. Cho, Y. Lee, Genetic and environmental variations in baicalin, baicalein and wogonin contents in Scutellaria baicalensis, Planta Med. 77 (2011) 1390. [4] Z. Li, M.D. Liu, S.X. Ji, The identification of the origin of Chinese wolfberry based on infrared spectral technology and the artificial neural network, Spectrosc. Spectral. Anal. 36 (2016) 720–723. [5] R. Xiao, Z.F. Yuan, C.Y. Wang, L.T. Zhang, C.H. Wang, Fingerprints of Scutellaria baicalensis from different habitats by HPLC, Chin. Tradit. Herbal Drugs 5 (2005) 743–747. [6] D.D. Huang, W.W. He, L. Jin, Distinguish schisandrae sphenantherae fructus from different producing areas based on electronic nose technology, Chin. J. ETMF 23 (2017) 22–26. [7] M.P. Dinca, A. Leca, D. Apostol, M. Mernea, O. Calborean, Transmission THz time domain system for biomolecules spectroscopy, J. Optoelectron. Adv. Mater. 12 (2010) 110–114. [8] T. Chen, Z. Li, W. Mo, Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition, Spectrochim. Acta A 106 (2013) 48–53. [9] H.J. Shin, S.W. Choi, G. Ok, Qualitative identification of food materials by complex refractive index mapping in the terahertz range, Food Chem. 245 (2018) 282–288. [10] Z. Chen, Z. Zhang, R. Zhu, Y. Xiang, Y. Yang, Application of terahertz time-domain spectroscopy combined with chemometrics to quantitative analysis of imidacloprid in rice samples, J. Quant. Spectrosc. Radiat. Transf. 167 (2015) 1–9. [11] Z. Yan, D. Hou, P. Huang, B. Cao, G. Zhang, Terahertz spectroscopic investigation of L-glutamic acid and L-tyrosine, Meas. Sci. Technol. 19 (2008) 015602. [12] Y. Ma, Q. Wang, L. Li, PLS model investigation of thiabendazole based on THz spectrum, J. Quant. Spectrosc. Radiat. Transf. 117 (2013) 7–14. [13] J. Choi, S.Y. Ryu, W.S. Kwon, K.S. Kim, S. Kim, Compound explosives detection and component analysis via terahertz time-domain spectroscopy, J. Opt. Soc. Korea 17 (2013) 454–460. [14] J. Zhang, W. Li, H.L. Cui, C. Shi, X. Han, Nondestructive evaluation of carbon fiber reinforced polymer composites using reflective terahertz imaging, Sensors
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