# Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics

Authors: Haiyan Fu; Qiong Shi; Liuna Wei; Lu Xu; Xiaoming Guo; Ou Hu; Wei Lan; Shunping Xie; Tianming Yang
Journal: Journal of Spectroscopy
 (2019)

---

## Abstract

Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism ofAngelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future.


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## Body

## 1. Introduction

Herbal medicines are of effective pharmacological functions, low toxicity, and less side effects to human body, so they have been widely used all over the world [1–3]. However, herbal medicines with different geographical origins have different chemical compositions and pharmacological activities [4, 5]. In addition, the processing of herbal medicines often removes morphological properties of species, and some herbal medicines at high cost are often the subject of fraudulent practices by replacing them with ones at low cost [6, 7], which may lead to an unfair competition in the pharmaceutical and harm the interest of consumers. Thus, the quality analysis method of herbal medicines to distinguish the origins is an important concern for consumers [8–10]. Traditional methods such as high-performance liquid chromatography and mass spectroscopy are time-consuming, expensive, and laborious and have to be performed by highly trained technicians [11, 12]. Therefore, a rapid, more accurate, and sensitive identification method is required to determine herbal medicines.Most studies focused on specific pharmacological ingredients in herbal medicines; however, the pharmacological activity of herbs is the result of the interaction of all ingredients rather than specific ingredients. Therefore, the specific ingredients could not be used as a proper criterion for characterization of the overall quality of the herbs [13, 14]. Fourier transform mid-infrared (MIR) [15] and near-infrared (NIR) [16, 17] techniques are efficient tools for studying food and pharmaceutical quality control because of their fast and nondestructive analytical characteristics. For example, Zhu et al. used FT-IR and 2DCOS-IR methods to discriminate the cultivated Codonopsis lanceolata in different ages [18], and in the research done by Gayo and Hale, near-infrared spectroscopy was applied to detect and quantify the species authenticity in Crabmeat [19]. By studying the characteristic information of the spectra, different types of samples can be accurately distinguished. Nonetheless, the information obtained from by NIR spectra may be difficult to interpret directly because of the highly overlapped spectra. Although MIR spectra provide some significant differences of spectral peaks, they do not give abundant chemical and structural information of samples like NIR spectra. Therefore, establishing effective and robust chemometric methods has been extensively concerned [20, 21]. For example, Woo’s team used Mahalanobis distance and discriminant PLS2 combined with NIR spectroscopy to discriminate herbal medicines according to geographical origins, but there are only two different classes from different geographical origins [22]. Frizon et al. used the PLS in determination of total phenolic compounds in yerba mate and predicted total phenolics with associated errors of 12% [23]. Liu et al. studied on the differentiation of the root of various ginseng by FT-IR and two-dimensional correlation IR spectroscopy, and the cluster analysis demonstrated that the three kinds of ginseng can be distinguished clearly from each other but with an exception [24]. PCA is a multivariate statistical technique that reduces the multidimensionality of data while minimizing information loss [25]. LDA can establish linear transformations to find the best boundary and achieve maximum separation between classes by constructing discriminant functions [26]. From another aspect, as a powerful pattern recognition method, PLSDA has successfully been applied to solve classification problems in many scientific fields [27, 28]. Furthermore, a global model with moving window partial least-squares (MWPLS) [29, 30] like other variable selection methods, MWPLSDA was successfully applied to spectra interval selection for calibration problems, and desirable results were obtained [31]. A subset of the whole wavelengths to develop the calibration model, the wavelengths carrying serious heteroscedastic noises, and especially the spectral ranges contaminated by external factors are excluded from the model, and wavelength ranges sensitive only to the chemical compositions of the samples are selected to develop a simplified yet stable calibration model.Sometimes, it is difficult to discriminate the origins of herbal medicines only through the pattern recognition method by single NIR or MIR spectra [32] combined with chemometrics, and it is necessary to extract from the data fusion of NIR and MIR spectroscopy [33]. There is abundant information related to combinatory MIR and NIR spectroscopy coupled with chemometrics for quality control of herbal medicines.In this study, different supervised pattern recognition algorithms including principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) with raw NIR spectra were used to discriminate five different geographical origins ofAngelica dahurica. Moreover, moving window partial least-squares discriminant analysis (MWPLSDA) and the fusion spectra variables evaluate authenticity and adulteration of Corydalis yanhusuo W. T. Wang. The result shows that PLSDA model is of great performance than PCA and LDA in identifying geographical origins of herbal medicines. In addition, the full spectra information fused by NIR and MIR combined with MWPLSDA showed the best ability in determination of authenticity of herbal medicines. This method provides pattern recognition models that can be applied in geographical origin discrimination or authenticity and adulteration recognition at the same time and can further be widely used in various herbal medicines.


## 2. Material and Methods

### 2.1. Collection of Raw Materials

A total of 50Angelica dahurica samples from five geographical origins (Hebei, Anhui, Yunnan, Zhejiang, and Sichuan) were purchased from the Derentang pharmacy, and each region included 10 batches. Besides, two kinds of authentic Corydalis yanhusuo W. T. Wang (Zhejiang) were purchased from the Derentang pharmacy and the Kangderuiqi flagship store, while three kinds of adulterations Corydalis decumbens (Thunb.) Pers., Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.) were, respectively, collected from Anhui, Jiangsu, and Fujian, and the aforementioned five samples for identification of adulteration were collected in 10 batches.


### 2.2. Apparatus

The following apparatuses were used: Nicolet 6700 FT-IR, OMNIC 8.2 spectral collecting software (Thermo Fisher Scientific Inc., USA); Antaris II FT-NIR spectrometer, RESULT 3.0 spectral collecting software (Thermo Electron Co., USA); DZF-6021 vacuum oven (Shanghai YIHENG Technical Co., Ltd); and FW135 herbal grinder (Tianjin Taisite Instrument Co., LTD).


### 2.3. Methods of Sample Measurement and Data Preprocessing by NIR and MIR

All samples used in NIR were crushed with the grinder, sieved into fine powders by a 200 mesh sieve, then vacuum-dried at 60°C for 24 hours, and stored in a dryer spare. The sample powder was placed directly into the quartz cup, and the air background was subtracted. Spectra were collected by integrating sphere diffuse reflectance with the collecting region at 4000–10000 cm−1 and a resolution of 8 cm−1. Data processing was performed using the average of the five measured spectra for each sample. In total, 250 spectra from different geographic origins (5 samples × 10 batched × 5 measurements) were obtained. And 250 spectra were discriminated for the authenticity and adulteration of Corydalis yanhusuo W. T. Wang.


### 2.4. Method of Chemometrics

PCA, LDA, PLSDA, and MWPLSDA methods were written and performed through a Matlab 2010a (MathWorks, Natick, MA. USA). All preprocessing in those chemometrics only used the original spectra. PLSDA is based on the simultaneous decomposition response matrix and the class matrix extraction factor. By arranging the extraction factors in order of their correlation, the virtual vectors are encoded to represent different classes, wherein the virtual vectorfj encoded for the jth element is 1. The other elements are 0 for the jth class, and then each column of the response matrix is associated with the class matrix. The principle of moving window partial least-squares discriminant analysis (MWPLSDA) is that a suitable window moves along the full spectral interval according to our past study [34, 35]. In MWPLSDA, a suitable window of width H is constructed and moved along the entire spectrum to select useful wavelength intervals, and then the selected spectral spacing is used to construct the PLSDA model. The principle of MWPLSDA is based on the virtual setting of a window, which contains the number of variables from the first wavelength to the end of (i + H − 1) wavelength. A series of submatrices are obtained continuously by moving the window. According to the variables in the moving window, a series of PLS submodels are constructed. Then, according to the principle of least residual square (SSR), the interval of measurement matrix with smaller classification error and latent variable is selected as the final MWPLSDA model.


## 2.1. Collection of Raw Materials

A total of 50Angelica dahurica samples from five geographical origins (Hebei, Anhui, Yunnan, Zhejiang, and Sichuan) were purchased from the Derentang pharmacy, and each region included 10 batches. Besides, two kinds of authentic Corydalis yanhusuo W. T. Wang (Zhejiang) were purchased from the Derentang pharmacy and the Kangderuiqi flagship store, while three kinds of adulterations Corydalis decumbens (Thunb.) Pers., Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.) were, respectively, collected from Anhui, Jiangsu, and Fujian, and the aforementioned five samples for identification of adulteration were collected in 10 batches.


## 2.2. Apparatus

The following apparatuses were used: Nicolet 6700 FT-IR, OMNIC 8.2 spectral collecting software (Thermo Fisher Scientific Inc., USA); Antaris II FT-NIR spectrometer, RESULT 3.0 spectral collecting software (Thermo Electron Co., USA); DZF-6021 vacuum oven (Shanghai YIHENG Technical Co., Ltd); and FW135 herbal grinder (Tianjin Taisite Instrument Co., LTD).


## 2.3. Methods of Sample Measurement and Data Preprocessing by NIR and MIR

All samples used in NIR were crushed with the grinder, sieved into fine powders by a 200 mesh sieve, then vacuum-dried at 60°C for 24 hours, and stored in a dryer spare. The sample powder was placed directly into the quartz cup, and the air background was subtracted. Spectra were collected by integrating sphere diffuse reflectance with the collecting region at 4000–10000 cm−1 and a resolution of 8 cm−1. Data processing was performed using the average of the five measured spectra for each sample. In total, 250 spectra from different geographic origins (5 samples × 10 batched × 5 measurements) were obtained. And 250 spectra were discriminated for the authenticity and adulteration of Corydalis yanhusuo W. T. Wang.


## 2.4. Method of Chemometrics

PCA, LDA, PLSDA, and MWPLSDA methods were written and performed through a Matlab 2010a (MathWorks, Natick, MA. USA). All preprocessing in those chemometrics only used the original spectra. PLSDA is based on the simultaneous decomposition response matrix and the class matrix extraction factor. By arranging the extraction factors in order of their correlation, the virtual vectors are encoded to represent different classes, wherein the virtual vectorfj encoded for the jth element is 1. The other elements are 0 for the jth class, and then each column of the response matrix is associated with the class matrix. The principle of moving window partial least-squares discriminant analysis (MWPLSDA) is that a suitable window moves along the full spectral interval according to our past study [34, 35]. In MWPLSDA, a suitable window of width H is constructed and moved along the entire spectrum to select useful wavelength intervals, and then the selected spectral spacing is used to construct the PLSDA model. The principle of MWPLSDA is based on the virtual setting of a window, which contains the number of variables from the first wavelength to the end of (i + H − 1) wavelength. A series of submatrices are obtained continuously by moving the window. According to the variables in the moving window, a series of PLS submodels are constructed. Then, according to the principle of least residual square (SSR), the interval of measurement matrix with smaller classification error and latent variable is selected as the final MWPLSDA model.


## 3. Results and Discussion

### 3.1. Geographical Origin Discrimination ofAngelica dahurica by NIR

In order to analyze the five different samples more effectively, the classical quick data analysis, and nondestructive analytical technique, NIR was used in the measurement. The average NIR spectra of each group are displayed to reflect the overlay in Figure1. The peaks located at 8319 cm−1 might be associated with the second overtone of C-H, O-H, and N-H stretching modes and those around 6780 cm−1 were caused by the C-H deformation vibration of CH3. Due to the second overtone of the C=O stretching vibration, bands at 5164 cm−1 emerge and the C-H combination and second overtone can be seen at 4200–4300 cm−1. However, owing to the overlaps and the systematic noise in NIR spectra, chemometric methods were required to extract useful information for the recognition of Angelica dahurica samples. Herein, three classical chemical pattern recognition methods using principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) models were associated with virtual coding of original NIR spectral variables of different sample sets. The 250 sample spectra of five different Angelica dahurica samples were randomly divided into a training set and a prediction set (Table 1). The model was built using the training set, the number of latent variables (LVs) was determined to be 5 by eightfold cross-validation using the prediction set, and the discrimination results were analyzed for comparison.Figure 1
The raw NIR spectra of five different origins ofAngelica dahurica (a) and the results by PCA (b), LDA (c), and PLSDA (d).Table 1
A detailed list for the training set and the prediction set of five different kinds ofAngelica dahurica samples.
Groups code
f1
f2
f3
f4
f5
Sample group
1
2
3
4
5
Symbol
·
○
×
＋
∗
PCA, LDA, or PLSDA training set
1st–37th
38th–73rd
74th–105th
106th–138th
139th–171st
PCA, LDA, or PLSDA prediction set
1st–13th
14th–27th
28th–45th
46th–62nd
63rd–79thFirstly, as a common method in the chemical pattern recognition which is mainly used for classification in the analytical processes of Chinese herbal identification, principal component analysis (PCA) is one of the most classic high-dimensional methods, which reduces the high-dimensional data of FTNIR and converts 1557 raw variables into fewer new principal components. PCA used fewer principal component features to represent the original features of the sample by decomposing the sample matrix in the training set and prediction. Based on the PCA technology, the vector scores of the training and prediction sets of the aforementioned samples are reflected in Figure1(b), and all samples from five different geographic origins in the training and prediction sets could not be clearly distinguished, but these samples were with same shape. This phenomenon could be attributed to small differences in the chemical properties reflected in its geographical origin. The results demonstrated that the PCA method can effectively reduce and extract fewer new variables from the original high-dimensional data, but the restoration process also leads to loss some information useful for sample differentiation.Other than looking for the vector space that can best describe the original data like PCA, linear discriminant analysis (LDA) is a linear discriminant function based on input response variables for searching linear transformations and dimensionality reduction. The axes of interest for LDA can maximize the distinction between classes, projecting feature spaces (multidimensional samples in the dataset) into smaller dimensionalk-dimensional subspaces while maintaining information that distinguishes categories. Figure 1(c) shows the vector scores of the first two latent variables based on the LDA model for the training and prediction sets of samples. It clearly distinguished samples from different geographical origins in the training set, while those in the prediction set were not clearly distinguished. The result may be due to some special requirements of the LDA model, of which at least one of the needs to be nonsingular. In addition, when the so-called outlier class dominates in estimating the scattering matrix, the LDA model cannot guarantee that the optimal subspace is found [36]. Furthermore, PLSDA can reduce the effects of multicollinearity between variables, and it can simultaneously decompose the extraction factors of the prediction measurement matrix and the class matrix and arrange them according to the correlation between them. Five different geographical sources of Angelica dahurica are identified based on the maximum virtual coding position of the NIR spectral data. In order to optimize the predictive power of the PLSDA model and simplify the complexity of the PLSDA model, we selected the number of latent variables (LV) as 5 by 8-fold cross-validation. Figure 1(d) shows the plots of dummy codes of the training and prediction sets for five group samples of different geographic origins. Table 1 shows the virtual code attribution maps for the training and prediction sets of the original spectra in the PLSDA model. We encode five sets of samples into f1 (1, 0, 0, 0, 0), f2 (0, 1, 0, 0, 0), f3 (0, 0, 1, 0, 0), f4 (0, 0, 0, 1, 0), and f5 (0, 0, 0, 0, 1), respectively, according to the position of the largest virtual code. As shown in Figure 1(d), all training and prediction samples belonging to all groups of Angelica dahurica by original NIR spectra combined with PLSDA were identified accurately with a perfect recognition rate of 100%. This demonstrated that the PLSDA model successfully discriminates Angelica dahurica samples of different geographic origins. This further revealed that NIR spectroscopy combined with PLSDA method can be used to identify herbal medicines more rapidly, effectively, and reliably than the traditional ones.


### 3.2. Authenticity and Adulteration Discrimination ofCorydalis yanhusuo W. T. Wang by NIR and Combinatory of NIR

Herbal medicine processing often removes morphological properties of species, which leads to failure of distinguishing one type from another. For this reason, NIR spectra were used to discriminate the authenticity and adulteration ofCorydalis yanhusuo W. T. Wang. As is shown Figure 2(a), the peaks around 6826 cm−1 were due to the C-H deformation vibration of CH3. Due to the C-H first overtone of –CH2– groups, bands at 5800 and 5600 cm−1 were observed and bands at 5172 cm−1 were the second overtone of the C=O stretching vibration. Furthermore, the C-H combination and second overtone can be seen at 4200–4300 cm−1. The seriously overlapped raw spectra hardly reflect the differences between samples. Thus, PCA technology and LDA and PLSDA models were used to relate the dummy code for the full original and preprocessing spectral variables. 250 sample spectra of two kinds of authenticity, Corydalis yanhusuo W. T. Wang 1 and 2, and three kinds of adulteration, Corydalis decumbens (Thunb.) Pers. (3), Typhonium flagelliforme (Lodd.) Blume (4), and Dioscorea opposita (Thunb.) (5) were randomly divided into the training set and the prediction set (Table 2). However, both PCA technology and LDA model failed to show the correct results in prediction sets for five different groups by NIR (not shown here). Thus, PLSDA was adopted for the identification of authentic Corydalis yanhusuo W. T. Wang.Figure 2
Raw NIR (a) and MIR (b) spectra of two kinds ofCorydalis yanhusuo W. T. Wang, Corydalis decumbens (Thunb.) Pers, Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.).Table 2
A detailed list of two kinds ofCorydalis yanhusuo W. T. Wang, Corydalis decumbens (Thunb.) Pers., Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.)
Groups code
f1
f2
f3
f4
f5
Sample group
1
2
3
4
5
NIR training set
1st–33rd
34th–66th
67th–89th
90th–121st
122nd–154th
NIR prediction set
1st–17th
18th–34th
35th–61st
62nd–79th
80th–96th
Fusion training set
1st–33rd
34th–66th
67th–89th
90th–121st
122nd–154th
Fusion prediction set
1st–17th
18th–34th
35th–61st
62nd–79th
80th–96thIn our work, all training and prediction samples were correctly identified except for the two samples in the training set (34th and 88th) and the two samples in the prediction set (35th and 82nd). The 34th sample in the training set of f2 is incorrectly discriminated as f1, and the 84th sample of f5 is erroneously classified as f2. Furthermore, the 35th sample in the prediction set of f2 is incorrectly assigned as f3, and the 82nd sample in the prediction set belonging to f5 is incorrectly classified as f2. It may account for the useless information of some spectral variables. The total correction rate was 97.94% on the test set in PLSDA models. On the other hand, MIR spectroscopy provides more specific and distinct absorption bands than NIR spectroscopy. As is shown in Figure2(b), the band centered at 2931 cm−1 is due to a stretching vibration of aliphatic C-H in terminal CH3 groups. The strong single peak of the C=O stretching vibration of ketone groups is observed at about 1635 cm−1, whereas the band centered at 1250 cm−1 is due to the antisymmetric stretching vibrations of =C-O-C.In order to better identify the origin of Chinese herbal medicines, we combined the mid-infrared spectrum with the near-infrared spectrum to obtain fusion spectra with more abundant sample information (Figure3). The PLSDA was also applied to relate the dummy code for the full fused spectral variables.Figure 3
Data fusion of NIR and MIR spectroscopy for authenticity or adulteration discrimination ofCorydalis yanhusuo W. T. Wang (a) and the residue line obtained by MWPLSDA for the training sets (b).
(a)
(b)As is shown in Figure3(a), only the 17th sample in the prediction set of f1 was misclassified as f2 in fusion spectra (Table 3). It suggested that fusion spectra of NIR and MIR spectroscopy combined with PLSDA has better use in authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. But, it also failed to get 100% predictive accuracy.Table 3
Forecasted results of NIR-MIR fusion spectra of two kinds ofCorydalis yanhusuo W. T Wang, Corydalis decumbens (Thunb.) Pers., Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.) by PLSDA and MWPLSDA.
Sample sets
Discrimination
Combinatory spectra
PLSDA
MWPLSDA
Training
Accuracy (%)
100
100
Error number
0
0
Prediction
Accuracy (%)
98.97
100
Error number
1
0In MWPLSDA, the appropriate window withH width is constructed, and the useful wavelength range is selected by moving the whole spectrum. Then, all the selected windows are constructed into the PLSDA model. Finally, according to the minimum SSR principle of the MWPLSDA algorithm, the feature differences among the five samples are extracted. As shown in Figure 3(b), when the window size is 20, the optimum number of potential variables in the MWPLSDA model is 12. Figure 3(b) shows that when the number of variables in the fusion spectrum of NIR and MIR is 140-200, 750-930, and 1250-1380, the SSR is the smallest. At this time, MWPLS-DA was benefited in the selection of combined informative fusion spectral regions of 660–950 cm−1, 3550–4400 cm−1, and 5900–6600cm−1 for classification modeling of all samples and yielded the results better than that obtained from a partial least squares-discrimination analysis (PLS-DA) model built by using the whole NIR spectral region. As shown in Figure 4(b) and Table 3, all training and prediction set samples were correctly predicted by 100%. MWPLSDA can improve sample classification accuracy by eliminating useless information variables and noncomponent-related factors.Figure 4
Assigned plots of dummy codes of the training set and prediction set for the raw fusion MIR-NIR spectra in the PLSDA model (a) and the MWPLSDA (b) model.
(a)
(b)


## 3.1. Geographical Origin Discrimination ofAngelica dahurica by NIR

In order to analyze the five different samples more effectively, the classical quick data analysis, and nondestructive analytical technique, NIR was used in the measurement. The average NIR spectra of each group are displayed to reflect the overlay in Figure1. The peaks located at 8319 cm−1 might be associated with the second overtone of C-H, O-H, and N-H stretching modes and those around 6780 cm−1 were caused by the C-H deformation vibration of CH3. Due to the second overtone of the C=O stretching vibration, bands at 5164 cm−1 emerge and the C-H combination and second overtone can be seen at 4200–4300 cm−1. However, owing to the overlaps and the systematic noise in NIR spectra, chemometric methods were required to extract useful information for the recognition of Angelica dahurica samples. Herein, three classical chemical pattern recognition methods using principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) models were associated with virtual coding of original NIR spectral variables of different sample sets. The 250 sample spectra of five different Angelica dahurica samples were randomly divided into a training set and a prediction set (Table 1). The model was built using the training set, the number of latent variables (LVs) was determined to be 5 by eightfold cross-validation using the prediction set, and the discrimination results were analyzed for comparison.Figure 1
The raw NIR spectra of five different origins ofAngelica dahurica (a) and the results by PCA (b), LDA (c), and PLSDA (d).Table 1
A detailed list for the training set and the prediction set of five different kinds ofAngelica dahurica samples.
Groups code
f1
f2
f3
f4
f5
Sample group
1
2
3
4
5
Symbol
·
○
×
＋
∗
PCA, LDA, or PLSDA training set
1st–37th
38th–73rd
74th–105th
106th–138th
139th–171st
PCA, LDA, or PLSDA prediction set
1st–13th
14th–27th
28th–45th
46th–62nd
63rd–79thFirstly, as a common method in the chemical pattern recognition which is mainly used for classification in the analytical processes of Chinese herbal identification, principal component analysis (PCA) is one of the most classic high-dimensional methods, which reduces the high-dimensional data of FTNIR and converts 1557 raw variables into fewer new principal components. PCA used fewer principal component features to represent the original features of the sample by decomposing the sample matrix in the training set and prediction. Based on the PCA technology, the vector scores of the training and prediction sets of the aforementioned samples are reflected in Figure1(b), and all samples from five different geographic origins in the training and prediction sets could not be clearly distinguished, but these samples were with same shape. This phenomenon could be attributed to small differences in the chemical properties reflected in its geographical origin. The results demonstrated that the PCA method can effectively reduce and extract fewer new variables from the original high-dimensional data, but the restoration process also leads to loss some information useful for sample differentiation.Other than looking for the vector space that can best describe the original data like PCA, linear discriminant analysis (LDA) is a linear discriminant function based on input response variables for searching linear transformations and dimensionality reduction. The axes of interest for LDA can maximize the distinction between classes, projecting feature spaces (multidimensional samples in the dataset) into smaller dimensionalk-dimensional subspaces while maintaining information that distinguishes categories. Figure 1(c) shows the vector scores of the first two latent variables based on the LDA model for the training and prediction sets of samples. It clearly distinguished samples from different geographical origins in the training set, while those in the prediction set were not clearly distinguished. The result may be due to some special requirements of the LDA model, of which at least one of the needs to be nonsingular. In addition, when the so-called outlier class dominates in estimating the scattering matrix, the LDA model cannot guarantee that the optimal subspace is found [36]. Furthermore, PLSDA can reduce the effects of multicollinearity between variables, and it can simultaneously decompose the extraction factors of the prediction measurement matrix and the class matrix and arrange them according to the correlation between them. Five different geographical sources of Angelica dahurica are identified based on the maximum virtual coding position of the NIR spectral data. In order to optimize the predictive power of the PLSDA model and simplify the complexity of the PLSDA model, we selected the number of latent variables (LV) as 5 by 8-fold cross-validation. Figure 1(d) shows the plots of dummy codes of the training and prediction sets for five group samples of different geographic origins. Table 1 shows the virtual code attribution maps for the training and prediction sets of the original spectra in the PLSDA model. We encode five sets of samples into f1 (1, 0, 0, 0, 0), f2 (0, 1, 0, 0, 0), f3 (0, 0, 1, 0, 0), f4 (0, 0, 0, 1, 0), and f5 (0, 0, 0, 0, 1), respectively, according to the position of the largest virtual code. As shown in Figure 1(d), all training and prediction samples belonging to all groups of Angelica dahurica by original NIR spectra combined with PLSDA were identified accurately with a perfect recognition rate of 100%. This demonstrated that the PLSDA model successfully discriminates Angelica dahurica samples of different geographic origins. This further revealed that NIR spectroscopy combined with PLSDA method can be used to identify herbal medicines more rapidly, effectively, and reliably than the traditional ones.


## 3.2. Authenticity and Adulteration Discrimination ofCorydalis yanhusuo W. T. Wang by NIR and Combinatory of NIR

Herbal medicine processing often removes morphological properties of species, which leads to failure of distinguishing one type from another. For this reason, NIR spectra were used to discriminate the authenticity and adulteration ofCorydalis yanhusuo W. T. Wang. As is shown Figure 2(a), the peaks around 6826 cm−1 were due to the C-H deformation vibration of CH3. Due to the C-H first overtone of –CH2– groups, bands at 5800 and 5600 cm−1 were observed and bands at 5172 cm−1 were the second overtone of the C=O stretching vibration. Furthermore, the C-H combination and second overtone can be seen at 4200–4300 cm−1. The seriously overlapped raw spectra hardly reflect the differences between samples. Thus, PCA technology and LDA and PLSDA models were used to relate the dummy code for the full original and preprocessing spectral variables. 250 sample spectra of two kinds of authenticity, Corydalis yanhusuo W. T. Wang 1 and 2, and three kinds of adulteration, Corydalis decumbens (Thunb.) Pers. (3), Typhonium flagelliforme (Lodd.) Blume (4), and Dioscorea opposita (Thunb.) (5) were randomly divided into the training set and the prediction set (Table 2). However, both PCA technology and LDA model failed to show the correct results in prediction sets for five different groups by NIR (not shown here). Thus, PLSDA was adopted for the identification of authentic Corydalis yanhusuo W. T. Wang.Figure 2
Raw NIR (a) and MIR (b) spectra of two kinds ofCorydalis yanhusuo W. T. Wang, Corydalis decumbens (Thunb.) Pers, Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.).Table 2
A detailed list of two kinds ofCorydalis yanhusuo W. T. Wang, Corydalis decumbens (Thunb.) Pers., Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.)
Groups code
f1
f2
f3
f4
f5
Sample group
1
2
3
4
5
NIR training set
1st–33rd
34th–66th
67th–89th
90th–121st
122nd–154th
NIR prediction set
1st–17th
18th–34th
35th–61st
62nd–79th
80th–96th
Fusion training set
1st–33rd
34th–66th
67th–89th
90th–121st
122nd–154th
Fusion prediction set
1st–17th
18th–34th
35th–61st
62nd–79th
80th–96thIn our work, all training and prediction samples were correctly identified except for the two samples in the training set (34th and 88th) and the two samples in the prediction set (35th and 82nd). The 34th sample in the training set of f2 is incorrectly discriminated as f1, and the 84th sample of f5 is erroneously classified as f2. Furthermore, the 35th sample in the prediction set of f2 is incorrectly assigned as f3, and the 82nd sample in the prediction set belonging to f5 is incorrectly classified as f2. It may account for the useless information of some spectral variables. The total correction rate was 97.94% on the test set in PLSDA models. On the other hand, MIR spectroscopy provides more specific and distinct absorption bands than NIR spectroscopy. As is shown in Figure2(b), the band centered at 2931 cm−1 is due to a stretching vibration of aliphatic C-H in terminal CH3 groups. The strong single peak of the C=O stretching vibration of ketone groups is observed at about 1635 cm−1, whereas the band centered at 1250 cm−1 is due to the antisymmetric stretching vibrations of =C-O-C.In order to better identify the origin of Chinese herbal medicines, we combined the mid-infrared spectrum with the near-infrared spectrum to obtain fusion spectra with more abundant sample information (Figure3). The PLSDA was also applied to relate the dummy code for the full fused spectral variables.Figure 3
Data fusion of NIR and MIR spectroscopy for authenticity or adulteration discrimination ofCorydalis yanhusuo W. T. Wang (a) and the residue line obtained by MWPLSDA for the training sets (b).
(a)
(b)As is shown in Figure3(a), only the 17th sample in the prediction set of f1 was misclassified as f2 in fusion spectra (Table 3). It suggested that fusion spectra of NIR and MIR spectroscopy combined with PLSDA has better use in authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. But, it also failed to get 100% predictive accuracy.Table 3
Forecasted results of NIR-MIR fusion spectra of two kinds ofCorydalis yanhusuo W. T Wang, Corydalis decumbens (Thunb.) Pers., Typhonium flagelliforme (Lodd.) Blume, and Dioscorea opposita (Thunb.) by PLSDA and MWPLSDA.
Sample sets
Discrimination
Combinatory spectra
PLSDA
MWPLSDA
Training
Accuracy (%)
100
100
Error number
0
0
Prediction
Accuracy (%)
98.97
100
Error number
1
0In MWPLSDA, the appropriate window withH width is constructed, and the useful wavelength range is selected by moving the whole spectrum. Then, all the selected windows are constructed into the PLSDA model. Finally, according to the minimum SSR principle of the MWPLSDA algorithm, the feature differences among the five samples are extracted. As shown in Figure 3(b), when the window size is 20, the optimum number of potential variables in the MWPLSDA model is 12. Figure 3(b) shows that when the number of variables in the fusion spectrum of NIR and MIR is 140-200, 750-930, and 1250-1380, the SSR is the smallest. At this time, MWPLS-DA was benefited in the selection of combined informative fusion spectral regions of 660–950 cm−1, 3550–4400 cm−1, and 5900–6600cm−1 for classification modeling of all samples and yielded the results better than that obtained from a partial least squares-discrimination analysis (PLS-DA) model built by using the whole NIR spectral region. As shown in Figure 4(b) and Table 3, all training and prediction set samples were correctly predicted by 100%. MWPLSDA can improve sample classification accuracy by eliminating useless information variables and noncomponent-related factors.Figure 4
Assigned plots of dummy codes of the training set and prediction set for the raw fusion MIR-NIR spectra in the PLSDA model (a) and the MWPLSDA (b) model.
(a)
(b)


## 4. Conclusions

Supervised pattern recognition methods based on PLSDA and MWPLSDA algorithms by NIR and the data fusion of both NIR and MIR has been established to studyAngelica dahurica and to identify the authenticity of Corydalis yanhusuo W. T. Wang. In addition, it was clarified from the results that other than PCA and LDA that can merely have well learning performance and do well in the training sets, the PLSDA model shows good performance in the area of identification of Angelica dahurica or Corydalis yanhusuo W. T. Wang and can be employed in the analysis of the geographical origins of Angelica dahurica and the authenticity or adulteration of Corydalis yanhusuo W. T. Wang. Furthermore, the full spectrum information of NIR and MIR spectroscopy combined with MWPLSDA performed much better than the single NIR spectra or PLSDA model and demonstrated an unparalleled ability of herbal medicine discrimination. This new recognition method provided a promising approach for the identification of herbal medicines widely.



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*Source: 2467185-2019-04-30.xml*