Journal of Spectroscopy
Volume 2019 (2019), Article ID 6967984, 12 pages
https://doi.org/10.1155/2019/6967984

Research Article

Rapid Identification of Nine Easily Confused Mineral Traditional Chinese Medicines Using Raman Spectroscopy Based on Support Vector Machine

Jing Ming,¹ Long Chen,1,2 Yan Cao,¹ Chi Yu,¹ Bi-Sheng Huang,¹ and Ke-Li Chen¹

¹Key Laboratory of Ministry of Education on Traditional Chinese Medicine Resource and Compound Prescription, Hubei University of Chinese Medicine, Wuhan, Hubei 430065, China
²Xiangyang Central Hospital, Xiangyang, Hubei 441021, China

Correspondence should be addressed to Bi-Sheng Huang; hbsh1963@163.com and Ke-Li Chen; kelichen@126.com

Received 6 June 2018; Revised 22 September 2018; Accepted 25 October 2018; Published 23 January 2019

Academic Editor: Feride Severcan

Copyright © 2019 Jing Ming et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Mineral traditional Chinese medicines (TCMs) are natural minerals, mineral processing products, and some fossils of animals or animal bones that can be used as medicines. Mineral TCMs are a characteristic part of TCMs and play a unique role in the development of TCMs. Mineral TCMs are usually identified according to their morphological properties such as shape, color, or smell, but it is difficult to separate TCMs that are similar in appearance or smell. In this study, the feasibility of using Raman spectroscopy combined with support vector machine (SVM) for rapid identification of nine easily confused mineral TCMs, i.e., borax, gypsum fibrosum, natrii sulfas exsiccatus, natrii sulfas, alumen, sal ammoniac, quartz, calcite, and yellow croaker otolith, was investigated. Initially, two methods, characteristic intensity data extraction and principal component analysis (PCA), were performed to reduce the dimensionality of spectral data. The identification model was subsequently built by the SVM algorithm. The 3-fold cross validation (3-CV) accuracy of the SVM model established based on extracting characteristic intensity data from spectra pretreated by first derivation was 98.61%, and the prediction accuracies of the training set and validation set were 100%. As for the PCA-SVM model, when the spectra pretreated by vector normalization and the number of principal components (NPC) is 7, the 3-CV accuracy and prediction accuracies all reached 100%. Both models have good performance and strong prediction capacity. These results demonstrate that Raman spectroscopy combined with a powerful SVM algorithm has great potential for providing an effective and accurate identification method for mineral TCMs.

1. Introduction

Mineral traditional Chinese medicines (TCMs) are an important part of TCMs. Nine commonly used mineral TCMs, namely, borax, gypsum fibrosum, natrii sulfas exsiccatus, natrii sulfas, alumen, sal ammoniac, quartz, calcite, and yellow croaker otolith, have great differences in chemical compositions and pharmacological action. For instance, alumen, which is widely used in China for treating hemorrhoids, eczema, and scabies, has therapeutic functions including removing dampness to relieve itching, stopping bleeding, and preventing diarrhea [1]. Natrii sulfas is used to clean away heat, relax the bowels, and treat periappendicular abscesses [2]. Borax is the main ingredient of the famous Chinese patent medicine, Musk hemorrhoids ointment [1]. Sal ammoniac has some degree of toxicity and should be used with caution [3].

However, these nine mineral TCMs are of high similarity in color, lustre, shape, texture, and cut surface character. The characteristics of these nine mineral TCMs were shown in Table 1. It can be seen that most of them are odorless, white or colorless, transparent to translucent, and irregular in shape. Furthermore, mineral TCMs are usually used in powder form, which would make them more easily confused with each other. Traditionally, mineral TCMs are mainly identified relying upon the visual recognition and physicochemical methods. The former is subjective and may lead to unreliable results, and the latter are time-consuming. Therefore, a faster and more reliable detection technique is required.

Table 1: Characteristics of nine mineral TCMs.

Mineral TCMs
Shape
Color
Diaphaneity
Tenacity
Lustre
Odor
Taste

Borax [4]
Irregular, massive
Colorless to white
Transparent to translucent
Brittle
Vitreous
Odorless
Slightly salty, slightly sweet

Gypsum fibrosum [1]
Fibrous masses, tabular, or irregularly massive
White, gray white, or pale yellow
Translucent
Flexible
Silky
Odorless
Bland

Natrii sulfas exsiccatus [1]
Powder
White
—
—
—
Odorless
Salty

Natrii sulfas [1]
Prismatic, oblong, irregularly shaped, or granular
Colorless or off-white
Transparent or translucent
Brittle
Vitreous
Odorless
Salty

Alumen [1]
Irregular massive or granular
Colorless or yellowish-white
Transparent or translucent
Brittle
Vitreous
Odorless
Sour, slightly sweet, very astringent

Sal ammoniac [5]
Naturally occurring sal ammoniac are irregular platy crystals, and the synthetic one is cuboid
Colorless, white, pale gray, yellowish-white, or brown
Transparent or translucent
Brittle
Vitreous
Sulfury
Salty, bitter

Quartz [4]
6-sided prism or coarse granular aggregation with sharp edges
White or gray white
Translucent to opaque
Hard
Greasy
Odorless
Bland

Calcite [5]
Rhombohedral flat masses, rhombohedral prism
White, pale yellow, or pale red
Transparent to translucent
Brittle
Vitreous
Odorless
Tasteless

Yellow croaker otolith [4]
Oblong, ovoid, trigonous. One end is rounded, and the other is pointed.
White
Opaque
Brittle
Porcellaneous
Odorless
Bland, slightly astringent

Raman spectroscopy is a nondestructive analytical tool that has long been employed for the analysis of minerals since the discovery of the Raman effect. Raman spectroscopy measures inelastic light scattering and is a vibrational spectroscopic technique that can provide arrays of fingerprint assignments of the physical vibration mode. Such information could be used to characterize differences among different materials. In recent years, the technique has been investigated extensively in the field of TCMs because of the advantages such as rapid, convenient, nondestructive, and less sample consumption. Many studies have been reported for identification of TCMs using Raman spectroscopy. In these studies, Raman spectroscopy was successfully applied to authentication of raw materials [6–8], characterization of adulterants [9, 10], detection of counterfeits [11, 12], and geographical origin identification of TCMs [13, 14]. These studies obtained good performance, indicating the excellent discriminative ability of using Raman spectroscopy techniques in TCMs. At present, spectral technologies such as near-infrared spectroscopy and Raman spectroscopy combined with different types of chemometric algorithms such as principal component analysis (PCA) [15], support vector machine (SVM) [16, 17], and artificial neural network (ANN) [18–20] have been successfully used for the qualitative and quantitative analysis of TCMs. Therefore, an identification model was established based on Raman spectroscopy combined with SVM to realize the rapid and accurate identification of nine easily confused mineral TCMs.

2. Materials and Methods

2.1. Sample Collection

A total of 108 batches of samples were collected from Chinese medicine markets of Bozhou in Anhui Province, Yuzhou in Henan, Yulin in Guangxi, Xi'an in Shanxi, and Zhangshu in Jiangxi as well as Jointown Pharmaceutical Group Co., Ltd and Mayinglong pharmaceutical Group Co., Ltd, respectively. Among the samples, there were 14 batches of gypsum fibrosum, 10 batches of alumen, 14 batches of sal ammoniac, 10 batches of quartz, 10 batches of natrii sulfas, 12 batches of natrii sulfas exsiccatus, 14 batches of calcite, 12 batches of yellow croaker Otolith, and 12 batches of borax. All samples were identified to be authentic by X-ray diffraction (XRD) and chemical method according to the Chinese Pharmacopoeia (ChP 2015 I).

2.2. Instruments and Software

The instruments used for this project consisted of an XPertPro X-ray diffraction instrument (PANalytical Company) and a Portable i-Raman 475–785H (B&W Tek) spectrometer. The portable Raman spectrometer was connected to a BAC150B (B&W Tek) Raman sampling accessory by a 150 cm optical fiber probe BAC102-785E (B&W Tek). The instrument was equipped with a CleanLaze (B&W Tek) laser emitting at 785 nm with continuously adjustable power from 0 to 420 mW and a thermoelectrically cooled charged-coupled detector (CCD), covering a spectral range of 65–2700 cm⁻¹ at a resolution of approximately 3.5 cm⁻¹.

The software used in this study included BWSpec4™ spectral data-acquisition software, OPUS 7.5 spectrum analysis software (Bruker), IBM SPSS Statistics 19 (SPSS Statistics/IBM Corp), MATLAB R2014b (The MathWorks Inc.), and libsvm toolbox (libsvm-3.1, Faruto Ultimate).

2.3. Raman Spectra Collection

Each sample was crushed into powder and then passed through a 100-mesh sieve; 2 g of each sample powder was put into a specimen cup on the stage of the sampling accessory. The spectra have been recorded in the range of 70–2695 cm⁻¹. The laser output power was 100%. The integration time of each spectrum was adjusted to provide a better Raman signal. The measurements of each sample were replicated three times. The average value of the three spectra was selected as the spectrum information for analyzing.

2.4. Spectra Pretreatment Method

In the process of collecting sample spectrum, the original Raman spectra are often subjected to factors that are unrelated to the test sample properties, such as sample autofluorescence and variation in the measurement condition. These factors will lead to baseline drift and instability. Therefore, it is very necessary to conduct suitable spectrum pretreatment. In this study, the separate pretreatment methods vector normalization (VN), first derivation (FD), second derivation (SD), and combined pretreatment methods VN + FD and VN + SD were employed by OPUS in an effort to optimize model performance.

2.5. Spectral Data Compression Method

The high dimension of Raman spectral space will result in computational complexity and inefficiency in optimization and implementation of the SVM algorithms. It is necessary to extract spectra feature and compress spectral data in order to create effective and robust SVM model.

Principal component analysis (PCA) is a common dimension reduction method of spectrum, which can transform a number of possibly correlated variables (spectrum matrix) into a smaller number of variables called principal components (PCs). The new variables after transformation are not related with each other, which can eliminate the overlapped part of information [21]. Moreover, these new variables include the most informative dimensions of the original variables and as can as possible without losing useful information. In this research, PCA was conducted in MATLAB 2014b to reduce dimension of original spectra and pretreated spectra, respectively.

The other method to compress spectral data in this research is extracting intensity data of characteristic peaks from complete spectra. The detailed method is as follows. The Raman shifts of strong Raman peaks of each mineral TCM were selected, and then the intensity data at these Raman shifts in every sample spectra were extracted as input variables for the SVM model instead of the complete spectra. As shown in Figure 1, one Raman peak was transformed into two peaks after being pretreated with FD, which is equal in intensity and opposite in direction. One Raman peak was transformed into three peaks after being pretreated with SD: two of which were toward y-axis positive direction, and one towards y-axis negative direction. In consideration of reducing the amount of data, when selecting characteristic peaks, we selected only the peaks toward the y-axis positive direction for the spectra pretreated with FD and selected only the peaks toward the y-axis negative direction for the spectra pretreated with SD.

(a)

(b)

(c)

Figure 1: Raman spectra of natrii sulfas from 800 to 1200 cm⁻¹: (a) original spectra; (b) spectra pretreated by FD; (c) spectra pretreated by SD.

2.6. Support Vector Machine

Support vector machine is a powerful supervised learning algorithm that was first proposed by Vapnik [22] and successfully extended by a number of other researchers in recent years, which is based on the principle of minimization of structural risk in constructing an optimally separating hyperplane that separates different classes of data. In the process, input vectors are mapped to a newly constructed high dimensional space, and then parallel hyperplanes are constructed to maximize the interplane distance which separates the data. Details about the SVM classifier can be found in [23, 24]. In this research, SVM was performed in MATLAB 2014b to build qualitative models.

Radial basis function (RBF) was adopted as the kernel function of SVM due to its strong ability in processing nonlinear problems but also is the standard kernel applied in the majority of SVM applications [25]. RBF has two important parameters: the soft margin parameter C and kernel function parameter [26]. The values of both parameters should be determined during the model optimization process. Common optimization methods include grid-search method (GS), particle swarm optimization algorithm (PSO), and genetic algorithm (GA). PSO was derived from the simulation of the birds in finding foods [27]. In the PSO system, each alternative solution is regarded as a “particle,” multiple particles coexist and collaborate with each other (approximately like the foraging of birds), and each particle “flies” to a better position in the space of problems according to its own “experience” or the optimal “experience” of adjacent particle, so that the optimal solution can be searched. GA is an operation based on biological natural selection and genetic mechanism, in which selection, exchange, and catastrophe are considered as the operation method. With continuous genetic iterations, variables with good target values are retained, and thus, the optimal result can be eventually realized [28].

3. Results and Discussion

The normalized, baseline corrected average spectra of each medicine are shown in Figure 2, and all observed Raman bands were summarized in Table 2.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 2: The normalized, baseline corrected average spectra of each medicine: (a) gypsum fibrosum; (b) alumen; (c) sal ammoniac; (d) quartz; (e) natrii sulfas; (f) natrii sulfas exsiccatus; (g) calcite; (h) yellow croaker otolith; (i) borax.

Table 2: Peak positions and assignments of Raman bands.

Mineral TCMs
Chemical composition
Raman shifts (cm⁻¹)
Assignment
References

Gypsum fibrosum
CaSO₄·2H₂O
92∼210
Lattice modes
[29]

415, 494
ν₂ SO₄ bending

619, 672
ν₄ SO₄ bending

1009
ν₁ SO₄ stretching

1137
ν₃ SO₄ asymmetric stretching

Alumen
KAl(SO₄)₂·12H₂O
86∼191
Lattice modes
[30]

443, 461
ν₂ SO₄²⁻ symmetric bending

617
ν₄ SO₄²⁻ asymmetric bending

969, 990
ν₁ SO₄²⁻ symmetric stretching

1097, 1133
ν₃ SO₄²⁻ asymmetric stretching

Sal ammoniac
NH₄Cl
93∼167
Lattice modes
[31]

1402
ν₄ NH₄ bending

1708
ν₂ NH₄ bending

Quartz
SiO₂
128∼264
Lattice modes
[32]

355, 395, 410, 464
Si-O bending

696, 795, 808
Si-O-Si symmetric stretching

1161
Si-O asymmetric stretching

Natrii sulfas
Na₂SO₄·10H₂O
169, 225
Lattice modes
[33]

450
ν₂ SO₄²⁻ symmetric bending

620, 633, 648
ν₄ SO₄²⁻ asymmetric bending

989
ν₁ SO₄²⁻ symmetric stretching

1100∼1160
ν₃ SO₄²⁻ asymmetric stretching

Natrii sulfas exsiccatus
Na₂SO₄
80∼160
Lattice modes
[34]

452, 466
ν₂ SO₄²⁻ symmetric bending

620, 633, 648
ν₄ SO₄²⁻ asymmetric bending

993
ν₁ SO₄²⁻ symmetric stretching

1101, 1131, 1152
ν₃ SO₄²⁻ asymmetric stretching

Calcite
CaCO₃
154, 280
Lattice modes
[35, 36]

712
CO₃ν₄ symmetric deformation

1086
CO₃ν₁ symmetric stretching

1437
CO₃ν₃ asymmetric stretching

Yellow croaker otolith
CaCO₃
150, 178, 202
Lattice modes
[37]

704
CO₃²⁻ν₄ symmetric deformation

1083
CO₃²⁻ν₁ symmetric stretching

Borax
Na₂B₄O₇·10H₂O
82∼317
Lattice modes
[38]

352, 386
ν₂ BO₄ symmetric bending

462, 487
ν₄ BO₄ asymmetric bending

576
ν₁ BO₄ symmetric stretching

761
ν₂ BO₃ symmetric bending

950
ν₁ BO₃ symmetric stretching

1035
ν₃ BO₄ asymmetric stretching

1338
ν₃ BO₃ asymmetric stretching

3.1. Sample Sets Partitioning

Each spectrum was assigned a label from 1 to 9 according to its name, and each spectrum companied by its label becomes a sample for modeling. Four samples were selected randomly from each label as validation set samples (a total of 36 samples), and the rest of 72 samples were used as training set samples (shown in Table 3). The training set is used to build the identification model, while the validation set is used for validation.

Table 3: Sample sets.

Name
Label
Total
Training set
Validation set

Gypsum fibrosum
1
14
10
4

Alumen
2
10
6
4

Sal ammoniac
3
14
10
4

Quartz
4
10
6
4

Natrii sulfas
5
10
6
4

Natrii sulfas exsiccatus
6
12
8
4

Calcite
7
14
10
4

Yellow croaker otolith
8
12
8
4

Borax
9
12
8
4

Total
—
108
72
36

3.2. Spectral Data Compression

Because Raman peaks are distinct and sharp, the intensity data of characteristic peaks could be regarded as characteristic input variables for the SVM model instead of the complete spectra. In order to investigate the feasibility of this method, cluster analysis of the extracted intensity data of training set samples was performed based on the between-groups linkage method and square Euclidean distances by SPSS. The correction rate of cluster analysis based on the extracted intensity data of original spectra and pretreated spectra was compared in order to determine the optimal pretreatment method.

The correction rate of cluster analysis based on complete spectra, the intensity data of original spectra, and intensity data of pretreated spectra is shown in Table 4. The correction rate based on complete spectra was just 61.11%. The value improved significantly based on the intensity data. Above all, the correction rate based on intensity data extracted from spectra pretreated by FD was highest (90.28%), and the corresponding sensitivity and specificity values of each mineral TCM are shown in Table 5. It can be seen that the results of cluster analysis based on intensity data extracted from spectra pretreated by FD were good. Figure 3 shows the selected strong peaks of spectra pretreated by FD and the corresponding dendrogram. There are seven samples clustered falsely, which are mainly alumen and natrii sulfas. Both of them are sulfate mineral TCMs, so their Raman spectra are of high similarity.

Table 4: The correction rates of cluster analysis based on complete spectra and intensity data extracted from original spectra and pretreated spectra (%).

Label
Complete spectra
Intensity data extracted from original spectra and pretreated spectra

Original spectra
VN
FD
SD
VN + FD
VN + SD

1
90 (9/10)
90 (9/10)
100
100
100
100
100

2
0 (0/6)
50 (3/6)
100
66.67 (4/6)
66.67 (4/6)
100
100

3
100
100
100
100
100
100
100

4
83.3 (5/6)
100
100
100
100
100
100

5
0 (0/6)
83.33 (5/6)
50 (3/6)
50 (3/6)
33.33 (2/6)
50 (3/6)
33.33 (2/6)

6
100
87.5 (7/8)
100
100
100
100
100

7
40 (4/10)
60 (6/10)
30 (3/10)
100
100
70 (7/10)
70 (7/10)

8
37.5 (3/8)
75 (6/8)
75 (6/8)
75 (6/8)
75 (6/8)
25 (2/8)
25 (2/8)

9
62.5 (5/8)
100
100
100
100
100
0 (0/8)

Total
61.11
83.33
83.3
90.28
88.89
86.11
70.83

Table 5: The sensitivity and specificity of cluster analysis based on intensity data extracted from spectra pretreated by FD.

Label
Sensitivity (%)
Specificity (%)

1
100
100

2
66.67
100

3
100
96.77

4
100
100

5
50
100

6
100
100

7
100
100

8
75
100

9
100
100

(a)

(b)

Figure 3: (a) Peaks selected from spectra pretreated by FD. (b) The dendrogram (samples which were clustered falsely).

The result of cluster analysis shows the feasibility of the spectral compression method based on extracting intensity data. However, some samples whose Raman spectra are of high similarity, such as alumen and natrii sulfas, cannot be differentiated by cluster analysis. Therefore, the SVM algorithm was introduced to establish an identification model on the basis of extracted intensity data.

3.3. Establishment of SVM Identification Model

Because the clustering correction rate based on intensity data extracted from spectra pretreated by FD was highest, SVM models were established by using the intensity data extracted from spectra pretreated by FD as input variables and the labels as output variables. All the input variables should be normalized firstly. The kernel parameters were optimized by GS, GA, and PSO simultaneously. The model performance was evaluated by 3-fold cross validation (3-CV) of the training set. In the process of optimization, the optimal values of C and were determined when the 3-CV accuracy reached the maximum. After SVM models were established, the training set and validation set were predicted. The prediction ability was evaluated by the prediction accuracies of the training set and validation set.

The modeling results show that the best combination of C and (C = 1,  = 0.5744) was determined by GS, the 3-CV accuracy is 98.61%, and both the prediction accuracy of the training set and validation set are 100%. The optimization process is shown in Figure 4.

Figure 4: The GS optimization process of SVM models.

3.4. Spectral Compression Based on PCA

Spectra data points were compressed from more than 2000 into less than 50 by using the spectral compression method based on extracting intensity data. The other spectral data compression method investigated in this research is PCA. PCA was conducted in MATLAB 2014b to reduce dimension of original spectra and pretreated spectra. The accumulative contribution rates (ACR) of PCs are shown in Figure 5.

Figure 5: ACR of PCs of original spectra and pretreated spectra.

As shown in Figure 5, for original spectra and pretreated spectra, the ACR of the first 6 PCs reached 90%, which means that the first 6 PCs can represent most of the information provided by the complete spectrum basically. Therefore, PCA-SVM models were established using the first 6 PCs of the original spectra and pretreated spectra to investigate the effect of different spectra pretreatment methods to the model performances.

3.5. Establishment of PCA-SVM Identification Model

PCA-SVM models were established using the scores of the first 6 PCs as input variables and the labels as output variables. Table 6 shows the results of PCA-SVM models based on original spectra and pretreated spectra. It can be found that the 3-CV accuracy and the prediction accuracies of the PCA-SVM model based on VN were the highest, so VN is considered as the most suitable pretreatment method for the PCA-SVM model.

Table 6: Results of PCA-SVM models based on different pretreatment methods.

Pretreatment method
NPC
Parameter optimization
C

Accuracy (%)

3-CV
Training set
Validation set

1
2
3
4
5
6
7
8
9
Total
1
2
3
4
5
6
7
8
9
Total

Original
6
GS
9.1896
5.2780
93.06
100
100
100
50
83.33
100
100
100
100
94.44
100
100
100
25
100
100
100
100
100
91.67

VN
6
GS
3.0314
9.1896
98.61
100
100
100
100
83.33
100
100
100
100
98.61
100
100
100
100
100
100
100
100
100
100

FD
6
GA
0.3272
16.5711
93.06
100
100
100
66.67
66.67
100
100
100
87.5
93.06
100
100
100
50
100
100
100
100
100
94.44

SD
6
PSO
1.0047
7.1087
88.89
100
100
100
83.33
100
100
100
100
100
98.61
100
100
100
25
75
100
100
100
100
88.89

VN + FD
6
GA
1.4940
9.4271
95.83
100
100
100
83.33
83.33
100
100
100
100
97.22
100
100
100
100
100
100
100
100
100
100

VN + SD
6
PSO
6.4480
4.2768
94.44
100
100
100
66.67
66.67
100
100
100
100
94.44
100
100
100
100
100
100
100
100
100
100

However, under the pretreatment of VN, the first 6 PCs may not be optimal for modeling. If NPC is too less, the established model would be unable to reflect the relation between sample characteristics and the spectra information, and the phenomenon of “under-fitting” will occur. However, if NPC is too much, the prediction accuracy and generalization ability of the model will be affected, and “over-fitting” will occur. Therefore, model performance should be investigated under a different NPC so as to determine the optimal NPC. In this study, PCA-SVM models were established using the scores of the first 1, 2, 3, …, 8 PCs, respectively, and the results are shown in Table 7.

Table 7: Results of PCA-SVM models with a different NPC.

NPC
Parameter optimization
C

Accuracy (%)

3-CV
Training set
Validation set

1
2
3
4
5
6
7
8
9
Total
1
2
3
4
5
6
7
8
9
Total

8
GS
1
5.2780
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100

7
GA
10.6841
6.3515
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100

6
GS
3.0314
9.1896
98.61
100
100
100
100
83.33
100
100
100
100
98.61
100
100
100
100
100
100
100
100
100
100

5
PSO
16.2224
11.7130
98.61
100
100
100
100
83.33
100
100
100
100
98.61
100
100
100
100
100
100
100
100
100
100

4
GS
256
0.1088
98.61
90
100
100
100
66.67
87.5
100
100
100
94.44
100
100
100
100
75
100
100
100
100
97.22

3
GA
37.7015
136.7847
87.50
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
50
94.44

As shown in Table 7, the best performance and prediction ability of the PCA-SVM model were obtained when the NPC is 7 and optimized by GA. The 3-CV accuracy and the prediction accuracies of the training set and validation set were just reached 100%. Prediction accuracy declined when less PCs were loaded, and the prediction ability of models kept unchanged when more PCs were loaded. The GA parameters-optimized process is shown in Figure 6.

Figure 6: The GA optimization process of the optimal PCA-SVM models.

The models established on the two spectra compression method have good accuracies. Compared with PCA, the spectra compression method based on extracting intensity data has some advantages; for instance, it is easy to learn and to popularize because it avoids complicated calculation. What is more, the extracted Raman shifts and intensity data of Raman peaks could be used to establish Raman spectra database. However, the disadvantage is that the errors of the peak position and intensity may cause the reduction of prediction accuracy.

The overall results indicated that Raman spectroscopy with the SVM method could efficiently identify the mineral TCMs. Compared with other characterization techniques, Raman spectroscopy shows significant advantages. It is more objective and effective than the morphological description method in identifying TCMs that are similar in appearance. It is of high chemical specificity and high accuracy than the chemical method. For an unknown mineral TCM sample, the chemical method requires at least two experiments, and Raman spectroscopy only needs one detection to obtain the identification results. Moreover, since Raman spectra can reflect the structural characteristics of crystals, it can distinguish mineral TCMs with the same chemical composition but a different crystal structure, such as calcite and aragonite whose main components are both CaCO₃. Furthermore, compared with infrared spectroscopy (IR) or near-infrared spectroscopy (NIR), Raman spectroscopy has the advantages of sharp peaks, high resolution, and high specificity and are not susceptible to water or other interference. X-ray diffraction (XRD) is one of the most effective modern technology for identification of mineral TCMs. It is of high accuracy and good specificity, but the requirement of sample preparation is high, and the spectra analysis is complicated. The advantages of nondestructive and no sample pretreatment of Raman spectroscopy make it more convenient and efficient than XRD.

4. Conclusion

The models established using Raman spectroscopy based on the SVM method have excellent performance and strong prediction capacity for identifying these nine easily confused mineral TCMs. It is rapid and convenient to collect spectra of mineral TCMs without sample pretreatment using a portable Raman spectrometer, and the result of identification could be obtained immediately when the spectra of samples were loaded into the SVM model. It means that the SVM model can not only meet the need of rapid inspection in the Chinese medicine markets but also be used in the production process to control the quality of raw materials to ensure the safe use of the medication.

The result shows the feasibility of the SVM algorithm in the rapid identification and quality analysis by Raman spectroscopy. Raman spectroscopy in combination with chemometric algorithms such as PCA, SVM, PSO, and GA has the potential to contribute significantly to the analysis of other mineral TCMs. In the next work, more samples should be used for improving the reliability and applicability of the model. Raman spectra database of mineral TCMs will be created with the increase of samples. Other algorithms will also be investigated in the future to improve the model performance and expand the application of Raman spectroscopy in the field of TCMs.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This study was supported by the Wuhan Special Fund of Biotechnology and New Medicine from Development Action Plan of High-Tech Industry in 2012 (201260523193).

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