Discrimination of Curculigo orchioides Rhizoma and Curculigo glabrescens Rhizoma using stable isotope and mineral element analyses coupled with chemometrics

HPLC characteristic maker

Chemical features can be used to describe and evaluate medicinal materials as a whole. The HPLC method has good precision, sensitivity, and reproducibility, and can be used to quickly and specifically identify different herbs based on the overall chemical composition. The HPLC chromatograms of COR and CGR are illustrated in Fig. 2. The identities of the components were confirmed based on the retention time and ultraviolet spectra (285 nm) of the chemical markers. The main chemical components of COR and CGR were similar. As anticipated, the content of curculigoside (peak 1), the indicator component in COR, was not significantly from that in CGR. Interestingly, CGR contains a unique compound that was detected in the HPLC chromatograms, but was not found in the profile of COR. Therefore, this unique compound was specifically separated and purified, and the structure was identified by modern spectroscopic techniques. It was a novel compound determined to be 5-(3′,4′-dihydroxyphenyl)-1-(4″-hydroxyphenyl) pentane-1,4-dione, 1D and 2D NMR spectra were available at Fig. S1–S5. However, the low content of this compound was not enough to accurately distinguish two plant sources.

Figure 2
figure 2

High-performance liquid chromatography (HPLC) chromatogram of curculigoside, COR and CGR samples. Peak 1: curculigoside.

Variations in stable isotope ratios of COR and CGR

The variations in stable isotopic compositions between COR and CGR were shown in Fig. 3. The mean N% values ​​of COR and CGR samples were 1.898% and 0.720%, the N% values ​​in COR were significantly higher (Fig. 3a). The mean C% values ​​of COR and CGR samples were 40.052% and 39.998%, respectively (Fig. 3b). The mean δfifteenN value of COR was −3.157‰, which was significantly lower than the value of CGR, with the mean value of −0.173‰ (Fig. 3c). The mean δ13C value of COR was −28.678‰, which was significantly higher than the value of CGR, with the mean value of −31.487‰ (Fig. 3d). There were significant differences in the mean value of N%, δfifteenN, and δ13C according to botanic origins (all P< 0.01 from T -test).

Figure 3
figure 3

The relative content of N element (N%, a), C element (C%, b) and nitrogen isotope ratio (δfifteenN, c) and carbon isotope ratio (δ13c, d). Data were expressed as the mean ± SD. (**P < 0.01).

The 3D scatter plot of N%, δfifteenN, and δ13C values ​​was presented in Fig. 4, and it exhibited the excellent ability to predict COR and CGR. On the whole, the COR had a high N% and δ13C value, and a low δfifteenN value, so they gathered at the top section in the 3D graph. However, the CGR, in contrast, mainly appeared at the bottom. The stable isotope ratio shows a good effect in distinguishing different sources of Curculigo Rhizome.

Figure 4
figure 4

3D scatter plot of N%, δfifteenN and δ13C values ​​in COR and CGR.

mineral element analysis

The contents of mineral elements in COR and CGR samples were shown in Table 1. The results appeared significantly different among the two source species, except for B, Mg, K, Ca, Cu, Se, Ba. The K and Ca were the most abundant inorganic elements in COR and CGR. The Li, Al, Mn, Co, Ni, Zn and Cd contents were higher in COR than in CGR, while the concentrations of Na, Ti, Fe, Sr and Mo elements were present at a lower level in the COR samples.

Table 1 Average of mineral element concentrations (μg/g) of 10 COR and 9 CGR samples.

Principal component analysis of COR and CGR

A multivariate evaluation is necessary to improve the overall accuracy of COR and CGR. Based on the chemical analysis of the stable isotope ratios combined with the concentrations of 19 mineral elements, the PCA analysis result was shown in Fig. 5a. The vectors and cumulative contribution of variance of the first three PCs (PC1-3) were shown in Table S3. A three-factor model (the first three PCs with eigenvalues ​​> 1) can explain 88.0% of the total variability in the original data, which showed that the first three PCs can reflect most of the information in the samples. The PC1, PC2 and PC3 contributed 61.0%, 19.8% and 7.2% of the total variance, respectively. The result showed that 10 COR samples clustered together and 9 CGR samples clustered into another category. It was presented that COR and CGR samples can be well distinguished through PCA. Notably, three batches of COR from Yunnan tend to be distinguished from Sichuan.

Figure 5
figure 5

PCA classification result. Scatter plots of COR and CGR samples (a), PCA biplot for component PC1 and PC2 (b).

The PCA biplot of PC1 and PC2 was presented in Fig. 5b. PC1 was mainly correlated with the intensity of N%, δ13C, Li, K, Mn, Co, Cu, Zn, Se, Cd and negatively correlated with δfifteenN, Sr, Mo signal35.36. The intensity of B, Al, Fe, Ni, Ba were important in PC2. COR samples (1–7) from Sichuan were mainly affected by the content of N% and elements Li, K, Mn, Zn, Co, Cd, while the COR from Yunnan (8–10) were isolated. PC1 had a better ability to discriminate COR samples. However, CGR samples (11–19) were clustered with δfifteenN, Ti, Sr, Mo. The classification of CGR was related to the content of these elements and can be distinguished by them. It was found that metabolic activities in plants had a greater impact on the content of δ13C than environmental factors24.26. Therefore, the difference between COR and CGR samples may be due to the different elements accumulated in plant metabolism.

Identification of COR and CGR by OPLS-DA

To further utilize the potential discrimination capability of stable isotope and multielement analysis, OPLS-DA was used to process data related to COR samples and counterfeit CGR samples, and the result was shown in Fig. 6. The authentic COR samples and counterfeit CGR samples were significantly differentiated, indicating that stable isotope ratios and element contents combined with OPLS-DA analysis were an effective method to separate COR and CGR samples. The number of important components is determined by calculating the explained X variance (RtwoX), Y variance (RtwoY), and the predictive ability of cross-validation (Qtwo) 37. The parameters for evaluating the OPLS-DA prediction models were as follows: RtwoX = 0.800, RtwoY = 0.993, Qtwo = 0.991. Generally, the model has the good fitting ability when these values ​​are close to 1.0, the intersection point of Rtwo and Qtwo with the Y-axis should be less than 0.3 and 0.05 respectively, and the difference between Rtwo and Qtwois less than 0.338.39. Therefore, the results have shown that this OPLS-DA model was reliable. Moreover, VIP > 1 was considered as a good identification marker27,34,40and OPLS-DA provided 13 effective potential markers (δfifteenN, Cd, Sr, δ13C, N%, Co, Se, Ti, Zn, Li, Cu, Mn, K) for determining the authenticity of COR samples and counterfeit CGR samples (Fig. S7). Notably, the three COR from Yunnan were also separated from COR samples from Sichuan based on their stable isotope ratios and element contents by the OPLS-DA model. The results indicated that stable isotope ratios combined with element contents might have the potential capability to predict the geographic origin of CurculigoRhizome. Based on these advantages, stable isotope ratios and element contents combined with OPLS-DA analysis is an excellent method of discriminating COR and CGR samples.

Figure 6
figure 6

OPLS-DA classification result. Score plots showing the classification of authentic COR and counterfeit CGR samples.

Classification of Curculigo Rhizoma using LDA

To check the reliability of the classification model, LDA was performed using a cross-validation procedure to calculate the classification and probability of the COR and CGR samples23.28. The cross-validation result was displayed in Table 2. The LDA model gave a good classification rate (100%) and cross-validation rate (100%), COR and CGR were successfully identified. Thus, the predictive model performed well, LDA analysis combined with stable isotope and elements could be used to discriminate the two source species of CurculigoRhizome.

Table 2 Classification of COR and CGR samples based on discriminant analysis.

Leave a Comment