Figure 1A shows the Base peak chromatogram (BPC) of Leonuri Herba in negative mode. According to TR, Mass, (−)-ESI-MS/MS Fragment Ions and compared with the reference and standards, 49 components were identified or preliminarily identified. There were 2 alkaloids, 18 flavonoids, 7 Terpenoids, 8 aromatic acids and 14 other classes (Table 2).
According to the UHPLC-MS/MS spectrum of 30 batches of samples, the chromatographic peaks could be showed within 24 minutes. Thirty-three of them were the common peaks to each batch of samples (Table 3). The total peak area of each batch of samples accounted for more than 85% of the total peak area and the reproducibility was good, which fulfilled the requirements of fingerprints. Thus, they were determined as a common fingerprint peak. The common fingerprint peak could be determined by the standard retention time. When the retention time was 6.53min, it was leonurine (Figure 1B). Since the peak area of leonurine was relatively big, the peak time was moderate and the shape was good, it was selected as the reference peak. Using Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (2004A edition). Thirty batches of Leonuri Herba from different origins were introduced (Figure 2) and the time width was 0.1min, gaining the control fingerprint by median generation method. The similarity of 30 batches of samples was calculated by the angle cosine method. The result showed the similarity between each fingerprint and the control fingerprint was less than 0.90, indicating there was a big difference in each origin including Hubei, Guangdong, Henan, Anhui, Yunnan, Zhejiang and Sichuan. Thus, principal component and hierarchical cluster analysis should be further carried put to clarify the relationship between origins and quality.
Results of hierarchical clustering analysis (HCA)
The analysis showed the samples could be roughly clustered into Henan and Anhui as a big group; Yunnan and Sichuan as a big group; Guangdong and Hubei (at seedling stage) as a big group; Hubei (at mature stage) as a small group. This proved the components were similar if the origins are close. However, many factors such as harvest time, proportion of medical parts, soil, environment, and water were different, resulting in significant differences in chemical components of Leonuri Herba in different counties but same provinces. Thus, it is necessary to find quality markers in different origins to clarify the relationship between habitat and quality. From the chemical composition clustering analysis, as shown in Figure 3, the first component was leonurine. The second and third classes were quercetin-3-O-robinoside, rutin and 4',5-dihydroxy-7-methoxyflavone. The fourth class was quercetin-3-O-β-D-glucopyranoside, tiliroside and hyperoside. The fifth class was lavandulifolioside, salicylic acid, syringic acid and 2'''-syringylrutin. The sixth to eighth classes were daidzein, phytol, and apigenin. Other classes also included kaempferol, quercetin, tryptophan, 7α(H)-eudesmane-4,11(12)-diene-3-one-2β-hydroxy-13-β-D- glucopyranoside, etc. These different classes of components could be used to distinguish the quality of Leonuri Herba and as a basis for quantitative analysis.
Leonurine(C1), 4',5-dihydroxy-7-methoxyflavone(C3), rutin (C4), hyperoside(C11), apigenin(C15), quercetin(C16), kaempferol (C17) and salicylic acid (C30) were selected for further quantitative analysis of the indicator components.
Results of principal component analysis（PCA）
The common fingerprint peak area of Leonuri Herba fingerprints from different origins and batches were used as the source data of PCA by using factor analysis in SPSS 20.0. The results of SPSS analysis indicated that the first principal component should integrate the information of C1, C2, C3, C18, C21, C28, C29, C30 compounds. Thus, the chemical composition differences of different origins and batches of Leonuri Herba were mainly reflected to these chemical components. The results showed that the content of C1 (Leonurine), C3 (4',5-dihydroxy-7-methoxyflavone) and C30 (Salicylic acid) in Henan samples is higher, which was consistent with the cluster analysis results. The matrix coefficient of main components 1-8 in Leonuri Herba are shown in Figure 4. In Figure 4A, PCA divided Leonuri Herba from different origins into four parts: Henan, Anhui as a large category; Yunnan, Sichuan as a large category; Guangdong, Hubei (at seedling stage) as a large category; Hubei (at mature stage) as a large category, which were consistent with the results of cluster analysis, verifying the reliability of cluster analysis. Interestingly, these four groups were highly consistent with the collection locations of these samples (Figure 4B), which demonstrated that the chemical components of different samples are heavily inﬂuenced by growing area.
Comprehensive evaluation analysis
The relationship formula between the principal component load matrix U, the factor load matrix A and the eigenvalue λ was Ui=A/ SQRT (λi). By calculating the variables, eight eigenvectors U1-8 were obtained, and the expressions of the 8 principal components could be obtained, as follows.
Normalizing the original variables and using SPSS to compute variables and calculate the principal componentsY1, Y2, Y3, Y4, Y5, Y6, Y7 and Y8. Taking the variance contribution rate corresponding to each principal component as the weight, the principal component scores and the corresponding weights were linearly weighted to construct a comprehensive evaluation function of different habitats and batches of Leonuri Herba :
The comprehensive evaluation scores of Leonuri Herba from different origins and batches were calculated form the above formula are shown in Table 4. The higher of the score, the better of the sample quality of origins and batch. The comprehensive score was greater than 0. The results showed that batch S8 (from Hubei at seedling stage) had the highest comprehensive score, followed by batch S4 (from Guangdong at seedling stage), S15 (Henan) S10 (from Hubei at seedling stage) and S13 (Henan). The above results showed that the quality of three batches at seedling stage was better than the others. The quality of Leonuri Herba produced in Henan is relatively good when compare to other origins.
Result of Quantitative Analysis
The LOD was calculated according to the signal-to-noise ratio of 3:1; the LOQ was calculated according to the signal-to-noise ratio of 10:1, and the results were shown in (Additional file 2：Table S1). The precision test results showed that the precision of the instrument was good; the stability test results showed that the test solution had a good stability within 24 hours after preparation; the repeatability test results showed that the method had a good repeatability; the results of the sample recovery rate showed that the accuracy was good (Additional file 3：Table S2).. The results of 30 batches of Leonuri Herba from different origins are shown in Table 5. The detailed content trends of eight analytes in the 30 samples from different origins are exhibited in Figure 5. In Figure 5A, the results showed that YMC from Henan had the highest content in Leonurine (C1), 4',5-dihydroxy-7-methoxyflavone(C3), rutin (C4) and hyperoside(C11). However, the content of rutin(C4) in Zhejiang was the highest. For apigenin(C15), quercetin(C16), kaempferol (C17) and salicylic acid (C30), Hubei, Henan and Sichuan had similar result (Figure 5B). By adding all the eight analytes together, the total content of Henan is the highest (Figure 5C).
In summary, the quality of Leonuri Herba at seedling stage is better than the mature ones. Apart from the quality of medicinal part, Leonuri Herba from different origins are mainly clustered into four categories: Henan and Anhui are grouped together; Yunnan and Sichuan are grouped together; Guangdong and Hubei (at seedlings stage) are grouped together; Hubei (at mature stage) is grouped as one type. The comprehensive evaluation analysis showed that the quality of Leonuri Herba at seedling stage was good. And the quality of Leonuri Herba in Henan was relatively good when compare to other origins. However, the quality difference between stems and leaves from different origins and different growing stage is not very clear. Further studies should be conducted to address this issue. Laser microdissection combined with chromatographic analysis could be one of the powerful tool to investigate the chemical composition distribution and change in different growing stages of Leonuri Herba .This may also help to unify the standard of Leonuri Herba so as to decrease the confusion in markets.