Bao-ming Huangab,
Ting-bo Chenab,
Sheng-yuan Xiaocd,
Qing-lin Zhae,
Pei Luob,
Ying-ping Wangd,
Xiu-ming Cuif,
Liang Liuab and
Hua Zhou*ab
aFaculty of Chinese Medicine, Macau University of Science and Technology, H837a, Block H, Taipa, Macau, P. R. China. E-mail: hzhou@must.edu.mo; huazhou2009@gmail.com; Fax: +853 28825886
bState Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau, P. R. China
cSchool of Life Science, Beijing Institute of Technology, Beijing, P. R. China
dInstitute of Special Wild Economic Animal and Plant Science, Chinese Academy of Agricultural Science, Changchun, P. R. China
eJiangxi University of Traditional Chinese Medicine, Nanchang, P. R. China
fKunming University of Science and Technology, Kunming, P. R. China
First published on 4th October 2017
White ginseng, red ginseng, notoginseng and American ginseng are commonly called ginseng herbs and are frequently used as traditional Chinese medicine (TCM) and dietary supplements in China. Although their functions are evidently different, the discrimination of the four varieties still remains a challenge, especially for the deeply processed products which have lost their botanical morphological features and share similar chemical profiles. The chemometric approach is now the frequently used pattern recognition method for botanical origin discrimination. In the present study, OPLS-DA was employed to discriminate the four varieties using 19 major ginsenosides as variables, which were accurately measured by using UHPLC-TOF/MS. A clear separation of the four varieties was observed in the OPLS-DA score plot. The constructed model proved to be able to discriminate 10% of adulteration. Furthermore, this model showed high accuracy in authenticating the botanical origin of the deeply processed ginseng products. The results also showed that adulteration could be identified for some ginseng herb products in the market.
Though the four varieties originate from the same genus, their usages and properties are evidently different. For example, it has been reported that the white ginseng had no influence on the action of warfarin,2,3 which is an anticoagulant with a narrow safety margin usually used in the prevention of thrombosis and thromboembolism. However, American ginseng could significantly influence warfarin's anticoagulant activity.4 Therefore, the discrimination of the four varieties is crucial for the safety of the consumers. Nevertheless, the discrimination of the four varieties remains a challenge due to the similar morphological appearance and the chemical fingerprint. The discrimination is even harder when the raw herb is deeply processed into products. The DNA barcoding approach is an ideal method for discrimination of different species, however, the white ginseng and red ginseng are different processed forms of the same species, therefore, there is no gene marker that could be employed for the discrimination of these two varieties. Ginseng saponins (collectively called ginsenosides in this research) are the major active components in the four varieties, but the frequently used QC marker including the ginsenoside Rg1, Rb1, Re, and Rd are abundant in all the four varieties.5–8 Though American ginseng has one unique marker named pseudoginsenoside F11 (ref. 9) for inspection, the other three varieties are hardly to discover their corresponding unique markers. Therefore, the discrimination of the four varieties was still dependent on the direct comparison of the content of multiple markers.
Recently, the chemometric approach is frequently used to distinguish the biology origin of the herbal substance. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) is a newly developed supervised method specific for the dataset with clear pre-defined class membership. The key point to apply the chemometric approach is to find the significant variables to establish a discriminant model. Conventionally, the data array for chemometric analysis is the untargeted chemical profiling measured by the hyphenated techniques such as liquid chromatography coupled with diode array detector (LC-DAD), and LC coupled with mass spectrometry (MS),10 or the direct profiling method such as nuclear magnetic resonance (NMR).11 The LC-MS based approach has been reported effective in the discrimination of the geographic origin of ginseng,10 and the NMR based method has been applied in an even more complicated issue, the discrimination of the age.11 However, the variables used in these reported methods included too many unidentified compounds, which significantly increased the uncertainty for the QC and would significantly reduce the reproductivity of the method. Furthermore, the profiling method was dependent on many compounds e.g. sucrose, maltose, glucose, malate, and glutarate,11 which are not the certified active constituent in ginsengs. Technically, it has certain advantage to use the reported active and commercial available compound to establish a QC method for the commercial products. As we all known, no official standard yet adopted the untargeted profiling method for the quality control of ginseng. Recently, it has been reported that, the discrimination of American ginseng and its related products from the other three varieties was carried out based on four identified variables coupled with the PCA model,12 i.e., the ratios of Rb1/Rg1 and Rg1/Re, the contents of pseudoginsenoside F11 and Rf. However, the separation of the four varieties was not complete in the PCA score plot, where individual clusters located closely to at least one of the other clusters and showed certain degree of overlap. The major reason of the unsatisfied discrimination of this reported method is the improper selection of variable.
To select the suitable variables for the discrimination of the four varieties, the literature study was carried out to find the characteristic marker for individual varieties in this research. Each marker out of the 19 selected markers in this study could represent a certain aspect of the chemical feature of one variety. Therefore, the combinational use of these 19 ginsenosides as markers could provide sufficient systematic variation in the multivariate analysis for the discrimination of the four varieties. In the present study, 19 major ginsenosides (Fig. 1) in the four varieties were selected as the variables for the chemometric analysis and accurately measured by the ultra-high performance liquid chromatography coupled with time-of-flight mass spectroscopy (UHPLC-TOF/MS). The results showed that, a clear separation of the four varieties was observed in the OPLS-DA score plot by using the selected 19 ginsenosides as variables. The authentication result had been cross validated with the DNA sequencing method and this result has been published in our previous study.13
The water contents of the ginseng samples were measured by a moisture balance system (Sartorius MA100, Gottingen, Germany) and accordance with the requirements in Chinese Pharmacopeia (Volume 1, Edition 2015), i.e., white ginseng <12.0%, red ginseng <12.0%, notoginseng <14.0%, and American ginseng <13.0%.
The autosampler was set at 10 °C and the column temperature was maintained at 40 °C. The separation was carried out with acetonitrile (A) and water (B) (both phases contain 0.1% formic acid) at a flow rate of 0.35 mL min−1 with a gradient program as follow: 25–43% A (0–14 min), 43–65% A (14–18 min), 65–80% A (18–20 min), 80–25% A (20–22 min), 25–25% A (22–25 min). The mass spectrometer was operated under the negative ionization mode with ESI (electronic spray ionization) iron source and the following settings: drying gas, N2; gas temperature, 325 °C; gas flow, 8.0 L min−1; nebulizer, 35 psi; VCap, 3500 V; nozzle voltage, 1000 V; fragmentor, 175 V. The data was acquired for each sample from m/z 100 to 1700. The molecule mass accuracy and reproducibility was maintained by using the independent reference lock-mass ions (API-TOF Reference Mass Solution Kit, G1969-85001, Agilent Technologies).
The following ions were extracted for the quantitative analysis of different compounds: m/z 845.49 ([M + HCOO]−, ginsenoside Rg1, Rf and pseudoginsenoside F11); m/z 991.55 ([M + HCOO]−, ginsenoside Re and Rd); m/z 829.49 ([M + HCOO]−, ginsenoside Rg2, F2, 20(S) and 20(R)-Rg3); m/z 683.43 ([M + HCOO]−, ginsenoside F1, 20(S) and 20(R)-Rh1); m/z 584.29 ([M + 2HCOO]2−, ginsenoside Rc, Rb2 and Rb3); m/z 977.53 ([M + HCOO]−, notoginsenoside R1); m/z 599.29 ([M + 2HCOO]2−, ginsenoside Rb1); m/z 955.48 ([M − H]−, ginsenoside Ro); and m/z 793.43 ([M − H]−, zingibroside R1).
The recovery test was carried out by spiking the accurate amounts of the reference substance to the ginseng sample, the mixture was then processed as the description under Section 2.6. Three different levels of spiking amounts were carried out for the test. The original amount of ginsenosides in the ginseng sample was defined as the medium spiking level, whereas 50 and 150% of the medium level were defined as the low and high level to spike, respectively. Each level of test was carried out in triplicate. The recovery rate was calculated by eqn (1) below:
Recovery (%) = 100 × (amount determined − original amount)/amount spiked | (1) |
The calibration curves were constructed by plotting the peak area against the concentration of each ginseng saponin. Nineteen ginsenosides were divided into three groups based on the concentration variation in the ginseng samples as described under Section 2.5 to prepare the calibration curves. Each concentration was measured in triplicate. The linearity was verified by correlation coefficients (R2).
The lower limit of quantification (LLOQ) and the lower limit of detection (LLOD) were defined as the concentration with the basic response of the signal-to-noise (S/N) ratio of 10 and 3, respectively.
The matrix effect of the ginseng herb extract was evaluated by spiking the known amount of the 19 CRSs into the ginseng herb extract and served as the mixed sample. In this experiment, the ginseng herb extract served as the matrix. The same amount of the CRSs spiked into the ginseng herb extract was dissolved in methanol as the reference solution to compare the signal deviation to the mixed sample. The matrix effect was calculated using the eqn (2) below:
Matrix effect (%) = (target signal in mixed sample − target signal in matrix)/target signal in reference × 100% | (2) |
The resolution of the OPLS-DA model was evaluated by the capability in separating a series of designated samples mixed with white ginseng and American ginseng. The mixed samples were produced in the following manner:
Mixed sample = white ginseng × α + American ginseng × (1 − α) | (3) |
Data was expressed as the mean ± standard deviation (SD). Statistical analysis was performed using the SPSS 11.5 software. Significance was determined by one-way analysis of variance (ANOVA) followed by Tukey's multiple comparison test. Data were considered statistically significant if the p value was less than 0.05.
The challenge of simultaneous determination of the nineteen saponins is to separate the several groups of isomers i.e., (1) ginsenosides Rc, Rb2, and Rb3, (2) ginsenosides Rg1, Rf, and pseudoginsenoside F11, (3) ginsenosides Re and Rd, (4) ginsenosides F1 and Rh1, (5) ginsenosides F2, Rg2, and Rg3, especially the two groups of epimers i.e., 20(S/R) ginsenoside Rh1 and 20(S/R) ginsenoside Rg3. The methods previously reported were time consuming (approximately 60 minutes).21,22 In this study, the nineteen ginsenosides could be separated within 20 minutes (Fig. 2) due to the use of a 1.7 μm particle column and the gentle gradient (<1.5% organic phase increasing per min) optimized for epimers separation.
Compound name | Precision | Matrix effect (inhibition, %) | LLOQ and LLOD | Repeatability (%) | Recovery (average ± SD, %) | ||
---|---|---|---|---|---|---|---|
Intra-day (%) | Inter-day (%) | LLOQ (ng μL−1) | LLOD (ng μL−1) | ||||
Rg1 | 1.42 | 3.80 | 3.10 | 0.08 | 0.04 | 1.67 | 101.94 ± 5.01 |
Re | 1.2 | 3.43 | 4.06 | 0.07 | 0.04 | 1.25 | 100.46 ± 4.83 |
F11 | 1.06 | 3.41 | 3.25 | 0.04 | 0.02 | 2.52 | 95.91 ± 4.01 |
Rf | 1.62 | 2.46 | 3.88 | 0.04 | 0.02 | 2.24 | 97.43 ± 4.68 |
20(S)-Rh1 | 0.95 | 2.57 | 2.56 | 0.08 | 0.04 | 0.71 | 96.82 ± 4.46 |
Rg2 | 1.88 | 2.31 | 4.14 | 0.08 | 0.04 | 1.51 | 98.17 ± 4.84 |
Rb1 | 0.80 | 2.25 | 5.03 | 0.06 | 0.03 | 1.01 | 97.64 ± 4.61 |
20(R)-Rh1 | 1.25 | 1.72 | 2.80 | 0.09 | 0.04 | 1.28 | 96.82 ± 4.46 |
Rc | 1.56 | 4.7 | 4.13 | 0.08 | 0.04 | 1.61 | 96.92 ± 3.28 |
F1 | 0.79 | 2.03 | 2.61 | 0.08 | 0.03 | 0.35 | 102.91 ± 4.41 |
Ro | 1.09 | 2.89 | 5.09 | 0.07 | 0.04 | 1.86 | 95.36 ± 4.65 |
Rb2 | 2.48 | 5.21 | 2.50 | 0.09 | 0.04 | 3.16 | 103.18 ± 2.00 |
Rb3 | 2.21 | 4.63 | 3.51 | 0.08 | 0.03 | 3.40 | 104.27 ± 4.72 |
Rd | 1.19 | 1.94 | 5.08 | 0.09 | 0.05 | 0.92 | 95.72 ± 5.66 |
F2 | 0.73 | 1.67 | 2.25 | 0.10 | 0.05 | 1.73 | 97.52 ± 3.67 |
20(S)-Rg3 | 2.16 | 2.35 | 1.55 | 0.08 | 0.04 | 2.13 | 103.53 ± 4.65 |
Zingibroside R1 | 1.02 | 2.22 | 2.35 | 0.04 | 0.02 | 3.67 | 97.28 ± 6.01 |
20(R)-Rg3 | 0.95 | 2.60 | 2.11 | 0.12 | 0.04 | 1.05 | 99.43 ± 8.64 |
Notoginsenoside R1 | 3.11 | 4.81 | 1.18 | 0.03 | 0.02 | 3.07 | 101.29 ± 4.00 |
The linearity of each ginsenoside was established within a range covering its content in the real samples. The method displayed good linearity (R2 ≥ 0.9989) for all the ginsenosides analyzed over the concentration range selected for quantification (Table 2). The effect of the matrix on the ionization was tested by comparing the peak intensities of a mixed sample solution to that of reference solution of ginsenosides. The matrix of ginseng extract caused slight decrease of the detector responses of the 19 ginsenosides (ranging from 1.20% to 3.96%, Table 1). The quantities of the ginsenosides in real samples were calculated based on the calibration curves established using pure solution of the CRSs. The RSD of the repeatability of the method was less than 4.0% (n = 6), which indicated good stability for sample determination. The recoveries of all the ginsenosides ranged from 95.36 to 104.27% (Table 1), indicating the acceptable accuracy of the constructed method.
Compound name | Linear regression | R2 | Linear range (ng) |
---|---|---|---|
Rg1 | y = 180![]() ![]() |
0.9990 | 0.21–53.19 |
Re | y = 56![]() ![]() |
0.9990 | 0.14–111.50 |
F11 | y = 59![]() |
0.9993 | 0.08–6.11 |
Rf | y = 39![]() ![]() |
0.9996 | 0.08–60.42 |
20(S)-Rh1 | y = 1![]() ![]() ![]() |
0.9996 | 0.15–11.40 |
Rg2 | y = 74![]() ![]() |
0.9991 | 0.15–77.15 |
Rb1 | y = 20![]() ![]() |
0.9996 | 1.29–118.64 |
20(R)-Rh1 | y = 151![]() ![]() |
0.9997 | 0.17–12.90 |
Rc | y = 12![]() ![]() |
0.9990 | 1.20–90.24 |
F1 | y = 139![]() ![]() |
0.9998 | 0.21–16.26 |
Ro | y = 36![]() ![]() |
0.9995 | 0.89–89.06 |
Rb2 | y = 19![]() ![]() |
0.9990 | 0.72–57.80 |
Rb3 | y = 14![]() |
0.9998 | 0.79–11.91 |
Rd | y = 50![]() ![]() |
0.9993 | 0.18–45.44 |
F2 | y = 62![]() ![]() |
0.9993 | 0.20–15.61 |
20(S)-Rg3 | y = 59![]() ![]() |
0.9998 | 0.16–12.55 |
Zingibroside R1 | y = 60![]() ![]() |
0.9989 | 0.74–37.26 |
20(R)-Rg3 | y = 120![]() ![]() |
0.9996 | 0.24–18.29 |
Notoginsenoside R1 | y = 28![]() ![]() |
0.9990 | 1–100 |
Over all, a quick and accurate method for simultaneously quantification of the 19 ginsenosides was developed in the present research; the precision, accuracy, linearity, and sensitivity of the method meet the requirement in ICH guidance documents.15
F11 was a unique ginsenoside in American ginseng while ginsenoside Rf was absent (Fig. 3), which was the critical chemical feature for American ginseng.9 Ginsenosides Re and Rg1 showed a characteristic ratio approximate 10 (Re/Rg1) in American ginseng.24 This ratio was less than or approximate to 1 in the other three herbs.21,22 The ratio of Rb3/Rb2 was larger than 1 in American ginseng, while the ratio was less than 1 in the other three herbs.5,21,23 The 20(R)-Rh1 was not detected in American ginseng, and was detected in one white ginseng sample, 9 notoginseng samples (0.03 ± 0.05 mg g−1), and in all red ginseng samples (0.06 ± 0.02 mg g−1). Therefore, the 20(R)-Rh1 was also a potential marker for the discrimination of American ginseng from other three ginseng herbs. In previous reports, the combination of 20(S/R)-Rh1 (ref. 21 and 25) was used for the quality assessment of American ginseng. But the present study showed that the distributions of the 20(S) and 20(R)-Rh1 in the four varieties were different. The 20(S)-Rh1 could not be detected in American ginseng as well as the 20(R)-Rh1,8 but notoginseng showed the highest content of 20(R)-Rh1 (0.18 ± 0.05 mg g−1) even higher than that in the red ginseng (0.04 ± 0.01 mg g−1). Therefore, the 20(S) and 20(R)-Rh1 were also potential markers for the discrimination of the four varieties.
Though the content of notoginsenoside R1 in the red ginseng and white ginseng was 50 to 100 folds lower than that in notoginseng (12.51 ± 5.82 mg g−1), the notoginsenoside R1 was not unique in the notoginseng. The signal intensity of OA type ginsenosides in notoginseng including Ro and zingibroside R1 was approximately 30 folds lower than the lower limit of the linear range, which could not cover the trace amount of the OA type ginsenosides in notoginseng by using a practical injection volume. The OA type ginsenosides in notoginseng were therefore excluded in the further discussion. The content of zingibroside R1 in the other three varieties ranged from 0.01 to 0.02 mg g−1, but not detected in notoginseng. This was the first report of accurately quantification of the zingibroside R1 in the ginseng herbs and then applied for the discrimination of notoginseng from other three varieties. The result showed that, the zingibroside R1 could be useful for the discrimination of notoginseng from the other three varieties due to the significant different content. The content of the three PPD type isomers Rc, Rb2, and Rb3 was merely approximately 0.05% of the total amount of ginseng saponins in notoginseng, which was much lower than that in the American ginseng (9.85%), white ginseng (21.66%) and red ginseng (32.24%). Therefore, the low content of the three isomers (Rc, Rb2, and Rb3) could also be one feature of notoginseng.
The direct comparison of the ginsenosides among the four varieties showed that, the F11 was the only unique marker for the discrimination of American ginseng from the other three. Though several markers such as the 20(S/R)-Rh1, notoginsenoside R1, and zingibroside R1 showed special distribution in the four varieties but not unique in any of the four.
Furthermore, due to the limited quantity of samples collected in present study, it was too risky to define a certain content of a marker as a threshold to exclude or include a sample for one variety. Therefore, the similarity among the four varieties made it difficult to discriminate by the direct comparison of the markers, particularly for the white ginseng and red ginseng. The difference was only from the relative content of the secondary ginsenosides, which occupied 17.32% of the total content of the ginsenosides in red ginseng, whereby 15.34, 3.16, and 2.86% in white ginseng, notoginseng, and American ginseng (Fig. 4H). The relative content of the secondary ginsenoside was also approximate between the white ginseng and red ginseng. It was too difficult to discriminate these two varieties by the direct comparison of the markers. Current studies have revealed that pattern recognition of multiple markers could be a feasible way for discrimination, and therefore the multivariate analysis is then used to discriminate the four ginseng herbs.
The model was conducted using 7-fold cross-validation in this research, with the cross-validation parameters R2 and Q2 describing the goodness of fit and the predictive ability of the model, respectively. The R2 and Q2 close to 1 indicated the OPLS-DA model is excellent fitted. The constructed model was performed by considering four classes (107 samples), resulting in three predictive and six orthogonal components (overall goodness of fit: R2X = 0.865, R2Y = 0.944, Q2 = 0.929). The model explained 86.5% of the total variance of the data array, R2Y > 0.9 indicated an excellent fitted model, and Q2 > 0.9 indicated the excellent predictive ability. The outlier is the plot out of the ellipse, which is defined as the Hotelling's T2 range 95% confidence. No serious outlier was observed. To validate the constructed model, 50% of the samples from each class were excluded randomly and then served as the test samples (Fig. 5B–E). The results showed that even up to 50% of the samples of each class were excluded from the training set, the constructed model could identify these test samples accurately. The results indicated that, the constructed model was greatly fitted and it was strong enough for the further application to identify the deeply processed products.
A clear separation of the four classes was observed in Fig. 5A, the notoginseng and the American ginseng located in the end of the t[1] and t[2] direction, respectively. Both the white and red ginseng located near the center of the model plane and stayed close to each other but had no sample overlapping. As can be seen, the white ginseng and red ginseng could be separated only in the t[1] direction but completely overlapped in the t[2] direction. In the model plane, the samples stayed close indicating the positive correlated (similar). It can be concluded that even the red ginseng was processed in high temperature for the decomposition of the primary ginsenoside, the decomposition of the primary ginsenoside was not sufficient to create complete new chemical species. The chemical stability within the P. ginseng led to the similarity of the white ginseng and red ginseng. The OPLS-DA score plot showed that, the white ginseng was different from red ginseng, but not significant as the difference observed in the other two species.
Nine ginsenosides mainly responsible (VIP > 1.0) for the separation of the four varieties in the OPLS-DA including the ginsenoside Rc, pseudoginsenoside F11, ginsenoside Re, notoginsenoside R1, ginsenoside Rd, 20(R)-Rh1, Rb1, F1, and 20(S)-Rh1 (VIP ranged from 1.38 to 1.01). The clustering in the score plot was majorly caused by the significant different content of the VIP compounds in the four varieties. For notoginseng, the ginsenoside Rc was extremely lower while the notoginsenoside R1 was extremely higher than in the other three varieties, in addition to the significant higher content of the 20(S)-Rh1 and Rb1. For American ginseng, it had the unique maker pseudoginsenoside F11, the significant higher content of Re and no 20(R)-Rh1. But for the white ginseng and red ginseng, there was no unique marker or significantly different marker between these two varieties. Therefore, the pattern recognition method was necessary for the discrimination of the red and white ginseng due to their high similarity.
The result showed that AGT-1 and 3 (Fig. 6A and C) were mixture of American ginseng and P. ginseng, whereas AGT-2 (Fig. 6B) was a pure white ginseng sample. The white ginseng and red ginseng products showed no adulteration by other species. These results were highly consistent to the DNA sequencing approach13 (ESI data, Table S-2†). In the adulterated samples, the chemical features of different varieties could be observed simultaneously. In AGT-1 and 3, the presence of F11, Rb3/Rb2 >1 are the chemical feature of the American ginseng, whereas the chemical features of P. ginseng, the ginsenoside Rf, was also observed. But the AGT-3 located at the edge of the white ginseng cluster indicated the low abundance of the American ginseng in the product (Fig. 6C). Except the RGT-1 (Fig. 6I), the RGT-2 and RGT-3 (Fig. 6J and K) located in the white ginseng cluster indicated the adulteration of white ginseng in the red ginseng products or the red ginseng used in RGT-2 and RGT-3 were not processed enough to obtain enough secondary ginsenosides. However, AGT-4 (Fig. 6D) showed different discrimination results between DNA sequencing and OPLS-DA. AGT-4 contained only P. ginseng based on DNA sequencing, however, a mixture in OPLS-DA. In this sample, both the chemical markers of American ginseng and P. ginseng could be observed: the simultaneously presence of F11 and Rf. According to the literature, the AGT-4 might be a hybrid of American ginseng and P. ginseng and thus contained the F11 and Rf in the same plant.28 The results showed that the OPLS-DA could provide sufficient discrimination accuracy for real ginseng products by the cross validation with the DNA sequencing. Furthermore, the discrimination using OPLS-DA could be used for a rough estimation of the proportion of the foreign materials in the product.
The big difference of the price among the four varieties is the major reason of the intentional adulteration. In the international trade market, the price of the white ginseng from China is approximate 50000 USD per ton, the red ginseng from Korea is 250
000 USD per ton, the American ginseng from Canada is 70
000 USD per ton, and the American ginseng from U.S. is 200
000 USD per ton.1,29–31 Therefore, the adulteration using the low-price varieties could bring huge illegal profit. Therefore, an efficient discrimination method for the four ginseng varieties is necessary. Compared to the DNA sequencing approach, the OPLS-DA approach is easy to operate and more effective for high-throughput analysis. Furthermore, it is also an optimal complement to the DNA sequencing approach for the discrimination of the samples with the same botanical origin, such as the white ginseng and red ginseng.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7ra06812c |
This journal is © The Royal Society of Chemistry 2017 |