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Quantitative Analysis of Phenolic Acids and Flavonoids in Cuscuta chinensis Lam. by Synchronous Ultrasonic-Assisted Extraction with Response Surface Methodology.

1. Introduction

Traditional Chinese medicines (TCMs) occupy incomparable position in the pharmaceutical industry due to their extensive activity for preventing and treating diseases. Because of their clinic application and the contribution for drug discovery, TCMs have been drawn widespread attention in the world [1]. Only one or very limited kind of constituent was stipulated as a marker for the quality control of herb according to the authoritative Chinese Pharmacopoeia 2015. However, hundreds of constituents could be extracted from a single herb, which may exert various pharmacological functions and diverse bioactivities [2]. On account of the intimate connection with pharmacological activity, the quality of TCMs is increasingly strict and more components in TCMs should be identified and quantified grimly.

As a commonly used TCM, cuscutae semen, dried fruits of Cuscuta chinensis Lam., is widely distributed in China. This annual parasitic herb is parasitic on the legume, compositae, chenopodiaceae, and other herbs commonly, such as Artemisia lavandulaefolia DC, Lespedeza chinensis G. Don, and Vicia cracca. It has been employed in various clinical applications, including female infertility, preventing abortion, male reproductive system disease, chyluria, and chloasma faciei. [3] The extensive modern pharmacological studies have indicated that Cuscuta chinensis Lam. could decrease the apoptosis of cardiomyocytes [4], exhibit antifibrotic effect [5], suppress the inflammatory response [6], improve sexual potency, prevent abortion, and enhance liver and kidney conditions [3]. Phytochemical compounds of Cuscuta chinensis

Lam. incorporate flavonoids, phenolic acids, volatile oils, hydroquinones, lignans, fatty acids, resin glycosides, steroids, polysaccharides, and alkaloids. By reason of the highest proportion in the chemical components of Cuscuta chinensis Lam., flavonoids and phenolic acids are two kinds of major bioactive compounds isolated from this TCM. A few results of studies suggested that some Cuscuta chinensis Lam. extracts could exert various pharmacological activities, for instance, flavonoids, phenolic acids, and phenolic compounds acting as free-radical scavengers could be responsible for antioxidative activity [3, 7].

A few analytical methods of quality control for Cuscuta chinensis Lam. were applied, such as HPLC-UV [8-10], high-performance capillary electrophoresis (HPCE) [11], and thin-layer chromatography (TLC) [12]. However, these practical methods either have very low sensitivities or were simply applied for evaluating limited kinds of active constituents such as flavonoids and polysaccharides. Due to the characteristics of multicomponent and multitarget of TCMs, it is difficult to comprehensively reveal the quality of TCMs via determining the few compounds merely. Therefore, it is necessary to establish a more precise analytical method of determining multiple compounds for assessing the quality of TCMs.

More useful information and optimum experimental conditions could be achieved by a good design and suitable model of experiment. Recently, uniform design (UD), which was shown to be a promising experimental design method and more effective than orthogonal design (OD) relying on numerical experiments, was reportedly used in the field of chemometrics, sciences, pharmaceutics, engineering, and manufacturing [13-18]. Response surface methodology (RSM) could be used to improve and optimize the complex experimental processes as a collection of statistical and mathematical techniques [19]. The various parameters and their interactions could be evaluated efficiently by this data analysis technology reducing the experimental group number [20, 21]. Uniform design underlined the uniformity of space filling in the experimental domain and the largest possible number of levels for each factor among all experimental designs [22]. UD can be simply used as a criterion to obtain better orthogonal designs.

The method in this study was developed to simultaneously determine sixteen flavonoids and phenolic acids of Cuscuta chinensis Lam. in different origins using liquid chromatography tandem mass-mass spectrum (HPLC-MS/ MS) in the multiple reaction monitoring (MRM) acquisition mode for comprehensive quality control of Cuscuta chinensis Lam. To our knowledge, this is the first ever method identified to detect and quantify sixteen active components (Figure S1 in Supplementary Materials) of Cuscuta chinensis Lam. extracted via optimum conditions combined UD coupled with RSM and OD experiment. Moreover, the proposed method will be possible to lay the material basis for the evaluation and control of the quality of TCMs.

2. Experimental

2.1. Chemicals and Reagents. Four reference compounds including chlorogenic acid, caffeic acid, quercetin, and catechin were purchased from the National Institute for the Control of Pharmaceutical and Biological Products. Neochlorogenic acid, cryptochlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, isochlorogenic acid C, p-coumaric acid, gallic acid, hyperin, isoquercitrin, campherol, rutin, isorhamnetin, astragalin, and apigenin were purchased from Chengdu Must Bio. Sci. and Chengdu Desite BioTechnology Co., Ltd (Chengdu, China). Deionized water used for sample preparations and buffer solutions was purified by a Milli-Q Academic ultrapure water system (Millipore, Milford, MA, USA). Acetonitrile and methanol were purchased from Merck (Germany). Formic acid was purchased from Anaqua Chemicals Supply (ACS). All other chemicals were of analytical grade.

2.2. Herbal Plant. Various batches of Cuscuta chinensis Lam. were purchased from various provinces in China. The authenticity of Cuscuta chinensis Lam. species were identified by Professor Lin Ma (Tianjin University of Traditional Chinese Medicine), and the voucher specimens were deposited at Tianjin University of Traditional Chinese Medicine. The Cuscuta chinensis Lam. was smashed into powder using a pulverizer (Zhongcheng Pharmaceutical Machinery) and dried at 40[degrees]C. Then, the powders were passed over 50 meshes, which were prepared for the following tests.

2.3. Preparation of Standard Solutions and Samples. All standard solutions were individually dissolved in methanol at a stock concentration of 1 mg x [mL.sup.-1]. A series of mixed standard solutions were diluted with methanol in different concentrations. The stock solutions of catechin and gallic acid were also dissolved and diluted with methanol to a final concentration of 25 [micro]g x [mL.sup.-1] and 1 [micro]g x [mL.sup.-1], respectively. All of the solutions were stored at 4[degrees]C until analysis.

Powdered sample (0.1 g) adding 10 [micro]L of 25 [micro]g x [mL.sup.-1] catechin and 1 [micro]g x [mL.sup.-1] gallic acid as internal standards were suspended in 45% ethanol-water (10 mL) and extracted in an ultrasonic bath for 120 min. Then, the sample was centrifuged at 14,000 x g for 10 min. The supernatant was transferred and filtrated through a 0.22 [micro]m membrane prior to injection. 1 [micro]L of the solution was injected for analysis. The schematic diagram of the extraction method is shown in Figure 1.

2.4. Preparation of Quality Control Samples. Quality control (QC) samples of chlorogenic acid, caffeic acid, quercetin, neochlorogenic acid, cryptochlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, isochlorogenic acid C, pcoumaric acid, hyperin, isoquercitrin, campherol, rutin, isorhamnetin, astragalin, and apigenin were prepared at low, medium, and high concentration levels by dissolving appropriate mixed standard solutions in methanol, respectively.

2.5. HPLC Condition and MS Condition. Qualitative analysis of the samples was performed using an Agilent HPLC 1200 system (Agilent Technologies, USA) coupled to an API 3200 triple quadrupole instrument (Agilent Corporation, CA, USA) with an electrospray ionization (ESI) source (Concord, Ontario, Canada). An Agilent Eclipse Plus C18 column (1.8 [micro]m, 4.6 mm x 150 mm) was equipped with a security guard Agilent C18 column (5 [micro]m, 2.1 mm x 12.5 mm). The mobile phase for the developed method consisted of acetonitrile (solvent A) and 0.05% aqueous formic acid in water (solvent B). The method employed a stepwise linear gradient as follows: 15%-19% solvent A at 0-3 min, 19%-20% solvent A at 3-9 min, 20%-30% solvent A at 9-12 min, 30%-48% solvent A at 12-12.5 min, 48%-52% solvent A at 12.5-14 min, 52%-54% solvent A at 14-17 min, 54%-60% solvent A at 17-18.5 min, and then 60%-81% solvent A at 18.5-20 min. The column was set at 35[degrees]C. In addition, the injection volume and low rate were 3 [micro]L and 3mL x [min.sup.-1], respectively.

The mass spectrometer was operated in the negative ion mode with curtain gas (CUR) of 45 psi, collision gas (CAD) of 5 psi, ion spray voltage (IS) of -4500 V, capillary temperature of 700[degrees]C, ion source gas 1 (GS1) of 40 psi, and ion source gas 2 (GS2) of 60 psi. The instrument was used in the tandem MS mode, by using the experiment of multiple reaction monitoring (MRM). A tandem mass spectrometry experiment that allows the selective isolation of the precursor ion in Q1, its subsequent fragmentation in a collision cell, and the final monitoring of a selected product ion in Q3 was done for the analysis of sixteen analytes and internal standards as shown in Table 1. The other parameters of eighteen compounds including declustering potential (DP), entrance potential (EP), collision energy (CE), collision cell exit potential (CXP), dwell time (DT), and retention time (RT) are also listed in Table 1.

3. Results and Discussion

3.1. Optimization of Extraction Procedure

3.1.1. Optimization of Extraction by Uniform Design Coupled with Response Surface Methodology. As for the efficient extraction of active compounds including phenolic acids and flavonoids from Cuscuta chinensis Lam., some parameters which influenced the extraction efficiency were optimized. In this study, ethanol was chosen as the extraction solvent. In order to obtain the optimal extraction condition, the relationship among concentration of extraction solvent ([X.sub.1]), extraction time ([X.sub.2]), and ratio of liquid to solid ([X.sub.3]\) was researched by uniform design (U12 (12 x 6 x 6)) (Table S1 in Supplementary Materials). The quadratic polynomial step-by-step regression method and data were analyzed by Uniform Design Version 3.00 software. A model was given as below to predict the response variables:

Y = [b.sub.0] + [b.sub.1][X.sub.1] + [b.sub.2][X.sub.2] + [b.sub.3][X.sub.3] + [b.sup.2.sub.1][X.sup.2.sub.1] + [b.sup.2.sub.2][X.sup.2.sub.2] + [b.sup.3.sub.2][X.sup.3.sub.2] + [b.sub.1][b.sub.2][X.sub.1][X.sub.2] + [b.sub.1][b.sub.3][X.sub.1][X.sub.3] + [b.sub.2][b.sub.3][X.sub.2][X.sub.3], (1)

where Y is the predicted dependent variable; [b.sub.0] is a constant that fixes the response at the central point of the experiment; [b.sub.1], [b.sub.2], and [b.sub.3] are the regression coefficients for the linear effect terms; [b.sub.1][b.sub.2], [b.sub.1][b.sub.3], and [b.sub.2][b.sub.3] are the interaction effect terms; and [b.sup.2.sub.1], [b.sup.2.sub.2], and [b.sup.2.sub.3] are the quadratic effect terms, respectively [23]. Firstly, the extraction options for flavonoids ([Y.sub.1]) were statistically analyzed, and the predicted model developed for flavonoids ([Y.sub.1]) was as follows:

[mathematical expression not reproducible] (2)

According to the analysis of variance, the model with a good coefficient of determination (R = 0.95) was not significant (F-value 8.845, P > 0.05) which implied that the three factors ([X.sub.1], [X.sub.2], and [X.sub.3]) had no influence to the extraction efficiency of flavonoids. The extraction efficiency for phenolic acids was significantly influenced by the concentration of the extraction solvent (P < 0.05). On increasing the temperature from 20 to 120[degrees]C (45% ethanol), the phenolic acids content increased by about 0.106 g x [g.sup.-1]. In addition, on increasing the concentration of ethanol from 40% to 45%, the phenolic acids content increased by about 0.136 g x [g.sup.-1]. Further increasing the concentration of ethanol, the phenolic acids content was decreasing (Figure 2(a)). The extraction conditions for phenolic acids ([Y.sub.2]) were statistically analyzed and the predicted model established for phenolic acids ([Y.sub.2]) is shown as follows:

[mathematical expression not reproducible] (3)

After the three revisions of the regression equation and eliminating nonsignificant items (F-value< F-critical value, P > 0.05), the predicted model established for phenolic acids ([Y.sub.2]) was modified as follows:

Y = -0.415 + 1.33[e.sup.-2] [X.sub.1] + 2.58[e.sup.-3][X.sub.3] + 1.34[e.sup.- 3][x.sup.2.sub.1]


- 7.57[e.sup.-5][X.sub.1][X.sub.2] - 3.54[e.sup.-5][X.sub.1][X.sub.3] - 3.00[e.sup.-5] [X.sub.2][X.sub.3].

On the basis of the elimination consequence ([X.sup.2.sub.2]), [X.sub.2], and ([X.sup.2.sub.3]) were eliminated which indicated that the effect of extraction time ([X.sub.2]) and ratio of liquid to solid ([X.sub.3]) was not significant for the extraction for phenolic acids. Last but not the least, the effect of extraction parameters for total flavonoids and phenolic acids was conducted by RSM and developed a predicted model. The extraction efficiency for the total contents was influenced by the concentration of extraction solvent (P < 0.05). The total contents of flavonoids and phenolic acids ranged from 0.180 g x [g.sup.-1] to 0.420 g x [g.sup.-1], when the temperature increased from 20[degrees]C to 120[degrees]C (45% ethanol). In addition, there is a rising trend for the total contents of flavonoids and phenolic acids from 40% ethanol to 45% ethanol. Otherwise, the trend was changed when the concentration of ethanol declined continuously (Figure 2(b)). The predicted model established for total flavonoids and phenolic acids ([Y.sub.3]) is shown as follows:

[mathematical expression not reproducible] (5)

After quadruplicate revision of the regression equation and eliminating nonsignificant items (F-value< F-critical value, P > 0.05), the predicted model established for total flavonoids and phenolic acids ([Y.sub.3]) was modified as follows:

Y = -0.218 + 1.00[e.sup.-2][X.sub.1] + 1.24[e.sup.-3][X.sub.3] - 6.45[e.sup.-5] [X.sup.2.sub.1]


- 1.54[e.sup.-5][X.sub.1][X.sub.2] - 2.43[e.sup.-5][X.sup.1][X.sup.3] + 1.77[e.sup.-5][X.sup.2][X.sub.3].

The items including ([X.sup.2.sub.2]), [X.sub.2], and [X.sub.3] were eliminated (F-value< F-critical value, P > 0.05), which revealed that extraction time ([X.sub.2]) and ratio of liquid to solid ([X.sub.3]) were not significant factors in the extraction procedure.

Synthesizing the RSM analysis and the predicted model, 10 mL 45% ethanol-water and ultrasonic extraction for 120 min at room temperature were selected as the optimum extraction method.

3.1.2. Optimization of Extraction by Orthogonal Design. After applying uniform design, which narrowed effectively the range of extraction conditions, some of the sophisticated tests should be investigated in succession by orthogonal design in order to obtain more efficient consequence. OD experiment was used to further optimize the extraction condition as an effective design method with small experimental range and less examined factors and levels. During this OD procedure, the experiment points for both training set and prediction set were concentrated in only a certain region derived from the results of UD coupled with RSM.

Firstly, the OD was designed to confirm the effects of various factors for extraction efficiency of flavonoids, including concentration of ethanol (A: 35% ([A.sub.1]), 40% ([A.sub.2]), 45% ([A.sub.3])), extraction time (B: 80 min ([B.sub.1]), 100 min ([B.sub.2]), 120 min ([B.sub.3])), ratio of sample to solvent (C: 100 g x [mL.sup.-1] ([C.sub.1]), 120 g x [mL.sup.-1] ([C.sub.2]), 125 g x [mL.sup.-1] ([C.sup.3])), and D as the void item. When concentration of ethanol, extraction time, and ratio of sample to solvent was set to 45%, 120 min, and 125 g x [mL.sup.-1], respectively, the best extraction efficiency could be obtained for analyzing flavonoids (Table S2 in Supplementary Materials). According to the result of the ANOVA test, ff-value of extraction time exceeded ff-critical values which meant this factor had significance in this extraction procedure. However, there was no significance in the other factors because their ff-values were under ff-critical value. Considering the above results and economy, 45%, 120 min, and 100 g x [mL.sup.-1] were chosen as concentration of ethanol, extraction time and ratio of sample to solvent for extracting flavonoids, respectively. Then, the following OD was carried out to investigate the effect of factors for extracting phenolic acids solely. The results displayed that the best extraction efficiency could be acquired for phenolic acids while concentration of ethanol, extraction time, and ratio of sample to solvent were set to 45%, 120 min, and 120 g x [mL.sup.-1], respectively (Table S3 in Supplementary Materials). But, the ANOVA test of this OD showed that the ratio of sample to solvent in this part had no significance. Thus, best extraction factors for phenolic acids were chosen as 45% ethanol, 120 min of extraction time, and 120 g x [mL.sup.-1] of sample to solvent. The last but not the least, further OD was applied to verify the optimum conditions for total phenolic acids and flavonoids. The results revealed that the extraction efficiency was best with 45% ethanol, 120 min of extraction time, and 100 g x [mL.sup.-1] of sample to solvent (Table S4 in Supplementary Materials). The following ANOVA test also indicated that the concentration of ethanol and extraction time were significant factors, whereas the ratio of sample to solvent was not significant as the ff-value was under the ff-critical value. By comprehensive consideration of the cost of production and the extraction efficiency of phenolic acids and flavonoids, 100 g x [mL.sup.-1] of sample to solvent, 45% ethanol-water, and ultrasonic extraction for 120 min at room temperature was selected as the optimum extraction method. This consequence revealed that the best pronounced condition could be obtained by OD experiment particularly and the optimum condition conducted by OD was the same with that obtained by UD.

3.2. Optimization of Chromatographic Conditions

3.2.1. Optimization of HPLC Condition. To obtain good separation and stable peak retention value, the factors of the HPLC-ESI-MS/MS method was optimized in detail for analyzing the active compounds of Cuscuta chinensis Lam. The factors concerning the separation of sixteen components comprised the type of acid (formic acid, acetic acid, and ammonium formate), the concentration of acid (0.01%, 0.05%, and 0.1%), column temperature (25[degrees]C, 35[degrees]C, and 45[degrees]C), and flow rate (0.3 mL x [min.sup.-1], 0.4mL x[min.sup.-1], and 0.5 mL x [min.sup.-1]). The effect of each factor was studied by orthogonal experiment design L9 ([3.sup.4]). Taking into account centered migration time and good peak response, hyperoside was selected as a reference to find the optimum HPLC condition, which is a main active component of Cuscuta chinensis Lam. The results (Table S5) showed that acetonitrile-0.05% formic acid aqueous solution in a step linear gradient, column temperature at 35[degrees]C, and 0.5 mL x [min.sup.-1] flow rate should be finally set for the qualitative analysis of active compounds.

3.2.2. Optimization of MS Condition. A tandem mass spectrometry approach was employed to obtain a quantitative determination of sixteen active markers and two internal standards. In order to increase the sensitivity of detection and improve the response value, optimization experiments of ion source parameters including CUR (30psi, 35psi, 40psi, 45psi), CAD (3psi, 5psi, 7psi, 9psi), IS (-2500 V, -3000 V, -3500 V, -4000 V, -4500 V), GS1 (25psi, 30psi, 35psi, 40psi, 45psi), GS2 (30psi, 40psi, 50 psi, 60 psi), and TEM (300[degrees]C, 400[degrees]C, 500[degrees]C, 600[degrees]C, 700[degrees]C) were carried out. Consequently, the optimum ion source parameters were achieved when CUR was 45 psi, CAD was 5 psi, IS was -4500 V, GS1 was 40 psi, GS2 was 60 psi, and TEM was 700[degrees]C.

3.3. Selection of Internal Standard. In order to avoid the error caused by sample consistency and sample discrimination effect, internal standard should be added into the analytical sample solution. Considering the chemical property of analytical compounds and the interaction of retention time between analytes and internal standard, two kinds of compounds were selected as internal standard, covering catechin and gallic acid. Catechin and gallic acid were used as internal standards for the eight flavonoids and eight phenolic acids, respectively, because their chemical properties were similar with these two kinds of analytes and they would not impact the peaks of the targets in the chromatogram.

3.4. Method Validation

3.4.1. Specificity. Figure 3 shows the chromatograms of sixteen active components of Cuscuta chinensis Lam. in the MRM acquisition mode. The results of HPLC-MS/MS analysis of these markers demonstrated good shape of peaks, and no interfering peaks presented in the sample for analysis at the migration times of either analytes or internal standards.

3.4.2. Linearity, Limits of Detection, and Repeatability. The calibration curve consisted of six concentration levels. The calibration graph was constructed by adding 10 [micro]L of 25 [micro]g x [mL.sup.-1] catechin and 1 [micro]g x [mL.sup.-1] gallic acid as internal standard. Sixteen plotted calibration curves and correlation coefficients (r > 0.999) confirmed that the curves were linear in the concentration ranges of each component. The limit of detection (LOD) and limit of quantification (LOQ) were considered as the concentrations of the compound that produced a signal-to-noise (S/N) ratio of 3 and 10, respectively. LODs and LOQs of each component ranged from 0.001 ng x [mL.sup.-1] to 14 ng x [mL.sup.-1] and 0.005 ng x [mL.sup.-1] to 34.9 ng x [mL.sup.-1], respectively (Table S6). The repeatability was evaluated by preparing the same sample with the same optimized extraction method (n = 6). The repeatability RSDs of sixteen targets were not more than 2.9%.

3.4.3. Precision and Accuracy. Precision and accuracy were obtained by evaluating quality control samples containing sixteen active ingredients at low, medium, and high concentrations with respect to a calibration graph prepared each day (n = 6). The results of intraday and interday precision and accuracy are illustrated in Table S7. The accuracies of sixteen active components for both intraday and interday were within the range of 87.8%-120.7%. The RSDs for both intraday and interday were below 13.9%. These results indicated that the present method was accurate, reliable, and reproducible.

3.4.4. Stability. The stability of sixteen active components at ambient temperature was assessed by analyzing quality control samples at low, medium, and high concentrations over 48 h storage at 4[degrees]C. The data of stability studies presented in Table S7 showed that RSDs of stability of sixteen active components were below 15.4%, when they were determined at 0 h, 2 h, 4 h, 6 h, 8 h, 12 h, 24 h, and 48 h, respectively. The results demonstrated that all of the components have good stability within 48 h of storage at the concentrations studied and this analytical method could be used to determine these compounds.

3.4.5. Recovery. The recoveries were determined for the purpose of evaluation of precision and accuracy of the extraction method. The extraction recoveries of sixteen components were evaluated after extraction from Cuscuta chinensis Lam. to an equivalent amount of the standard solution including chlorogenic acid, cryptochlorogenic acid, neochlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, isochlorogenic acid C, caffeic acid, hyperin, isoquercitrin, quercetin, campherol, p-coumaric acid, isorhamnetin, rutin, astragalin, and apigenin (n = 6). The mean recoveries of sixteen active components determined in the range of 89.9%--112.7% and RSDs were below 11.4% (Table S6). The results suggested that the extraction method showed good precision and accuracy.

3.5. Method Application. The proven ultrasonic-assisted extraction method coupled with HPLC-MS/MS method was applied to quantify sixteen active components in 31 batches of Cuscuta chinensis Lam. from different origins. The data of quantitative analysis illustrated that the total content of phenolic acids and flavonoids in Cuscuta chinensis Lam. from different origins varied significantly as shown in Tables 2 and 3. The total content of phenolic acids of Cuscuta chinensis Lam. from Chongqing province was relatively higher than others, which was the lowest from Yunnan. The highest total content of flavonoids of Cuscuta chinensis Lam. of these samples was determined from Henan province, and the lowest was assessed from Zhangshu.

4. Conclusion

In this research, an optical extraction condition was performed to get high yields of multiple active compositions by combining uniform design coupled with RSM and orthogonal design experiment. The HPLC-MS/MS procedure has been proposed to simultaneously determine sixteen active components present in different batches of Cuscuta chinensis Lam. with this effective extraction method. The developed method has been successfully applied to analyze the contents of phenolic acids and flavonoids using catechin and gallic acid as internal standards. The results demonstrated that this method offered excellent selectivity and sensitivity. Meanwhile, the use of HPLC-MS/MS could quantify phenolic acids and flavonoids and be successfully applied for quality control of Cuscuta chinensis Lam. in different origins. The contents of either phenolic acids or flavonoids of Cuscuta chinensis Lam. from the south of China were generally higher than those from the north of China. Consequently, the proposed HPLCMS/MS method could be taken into consideration for future study of evaluation of quality control 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.

Authors' Contributions

Jin Li and Kun-ze Du contributed equally to this study.


This research was supported by the National Natural Science Foundation of China (81374050) and Special Program of Talents Development for Excellent Youth Scholars in Tianjin.

Supplementary Materials

Supplementary material contains revised articles on the experiment design for optimization of extraction of analytes from Cuscuta chinensis Lam. and the validation results for the analytical method. Table S1: uniform design ([U.sub.12] (12 x 6 x 6)) for optimization of extraction of phenolic acids and flavonoids from Cuscuta chinensis Lam. Table S2: the results of orthogonal design [[L.sub.9] ([3.sup.4])] for optimizing of extraction of flavonoids from Cuscuta chinensis Lam. Table S3: the results of orthogonal design [[L.sub.9] ([3.sup.4])] for optimizing of extraction of phenolic acids from Cuscuta chinensis Lam. Table S4: the results of orthogonal design [[L.sub.9] ([3.sup.4])] for optimizing the extraction of flavonoids and phenolic acids from Cuscuta chinensis Lam. Table S5: the results of orthogonal design [[L.sub.9] ([3.sup.4])] for optimizing the HPLC conditions based on hyperoside of Cuscuta chinensis Lam. Table S6: the calibration curves, linearity ranges, LODs, LOQs, and recoveries of sixteen compounds in samples (n = 6). Table S7: intraday and interday accuracy and precision and stability of sixteen compounds (n = 6). Figure S1: the chemical structure of sixteen compounds and two internal standards. (Supplementary Materials)


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Kun-ze Du, (1,2) Jin Li, (1) Xinrong Guo, (1,2) Yuhong Li (iD), (1) and Yan-xu Chang (iD) (1,2)

(1) Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China

(2) Tianjin Key Laboratory of Phytochemistry and Pharmaceutical Analysis, Tianjin University of Traditional ChineseMedicine, Tianjin 300193, China

Correspondence should be addressed to Yuhong Li; and Yan- xu Chang;

Received 1 September 2018; Revised 18 October 2018; Accepted 6 November 2018; Published 20 December 2018

Academic Editor: Anna Vallverdu-Queralt

Caption: FIGURE 1: The schematic diagram of ultrasonic-assisted method coupled with HPLC-ESI-MS/MS.

Caption: FIGURE 2: Effect of ethanol concentration and extraction time on the extraction efficiency of phenolic acids (a) and total analytes (b) in Cuscuta chinensis Lam.

Caption: FIGURE 3: Typical chromatograms of LC-MS/MS for analytes and internal standards in Cuscuta chinensis Lam. 1, chlorogenic acid; 2, cryptochlorogenic acid; 3, neochlorogenic acid; 4, isochlorogenic acid A; 5, isochlorogenic acid B; 6, isochlorogenic acid C; 7, caffeic acid; 8, hyperin; 9, isoquercitrin; 10, quercetin; 11, campherol; 12, p-coumaric acid; 13, isorhamnetin; 14, rutin; 15, astragalin; 16, apigenin; IS1, catechin; IS 2, gallic acid.
TABLE 1: Mass spectrometric parameters of analytical compounds
and internal standards.

No.                             Q1      Q3     DP (V)   EP (V)

1        Chlorogenic acid      353.1   190.8    -24       -6
2     Cryptochlorogenic acid   353.2   173.0    -33       -5
3      Neochlorogenic acid     353.1   135.1    -24       -9
4     Isochlorogenic acid A    514.9   353.2    -64       -5
5     Isochlorogenic acid B    515.3   353.3    -67       -9
6     Isochlorogenic acid C    515.4   353.2    -51       -5
7          Caffeic acid        178.9   135.1    -33       -7
8            Hyperin           463.1   300.2    -71       -9
9         Isoquercitrin        463.2   300.0    -65       -8
10          Quercetin          301.2   151.2    -67       -8
11          Campherol          285.2   117.3    -80       -8
12       p-Coumaric acid       163.2   118.7    -36       -8
13         Isorhamnetin        315.3   300.2    -65      -10
14            Rutin            609.3   300.2    -28       -5
15          Astragalin         447.1   284.1    -65       5
16           Apigenin          269.2   117.1    -59       -9
IS1        Gallic acid         169.0   124.9    -32       -6
IS2          Catechin          289.1   123.0    -55       -9

No.                            CE (V)   CXP   Acquisition      RT
                                        (V)   time (second)   (min)

1        Chlorogenic acid       -33     -13        100         4.7
2     Cryptochlorogenic acid    -22     -2         100        4.96
3      Neochlorogenic acid      -46     -2         100        3.29
4     Isochlorogenic acid A     -28     -28        100        15.36
5     Isochlorogenic acid B     -28     -29        100        14.58
6     Isochlorogenic acid C     -21     -29        100        16.16
7          Caffeic acid         -23     -1         100        6.95
8            Hyperin            -42     -22        100        11.73
9         Isoquercitrin         -37     -25        100        12.28
10          Quercetin           -30     -2         100        17.25
11          Campherol           -56     -1         100        18.18
12       p-Coumaric acid        -23     -1         100        10.53
13         Isorhamnetin         -35     -25        100        18.34
14            Rutin             -46     -25        100        16.57
15          Astragalin          -41     -22        100        15.29
16           Apigenin           -54     -1         100        17.9
IS1        Gallic acid          -24     -1         100        2.64
IS2          Catechin           -38     -2         100        4.94

TABLE 2: Contents of phenolic acids in samples from different origins
(ng x [g.sup.-1]).

Collecting       Chlorogenic   Cryptochlorogenic   Neochlorogenic
locations           acid             acid               acid

Qingdao            12000.0          1043.0             102.3
Inner Mongolia     20700.0          2570.0             1021.3
Ningxia            7060.0            652.3              97.4
Beijing             567.0            361.0             138.7
Suizhou            6966.7            736.7              91.6
Zhangshu            343.3            175.0              65.0
Guizhou            20466.7           788.7             467.3
Yunnan              196.7            202.3              88.1
Henan              11900.0           501.7              141
Anhui1             8710.0            423.5             101.1
Tianjin            11633.3            --                 --
Xinjiang           9625.0           1640.0             178.5
Jiangsu             603.3             --               143.0
Bozhou             21366.7          2060.0             211.0
Anhui2             15233.3           1900              310.7
Shanxi             5090.0            705.7             145.3
Tangshan            143.5             --               522.5
Liaoning           5746.7             --               215.7
Zhejiang           12500.0          1143.3             110.0
Anhui3             13500.0          1466.7             208.0
Jiangsu            20466.7          2010.0             312.7
Henan              2610.0             --               229.3
Hebei              50500.0          5895.0             851.0
Chifeng            23733.3          2730.0             1293.3
Chongqing          30200.0          5100.0             1780.0
Lingchuan          4995.0            633.3             120.3
Shanxi             16033.3          1305.0             110.0
Shanghai           4086.7             --               101.6
Xuzhou             32266.7          3180.0             896.3
Guangdong          15150.0          1550.0             165.9
Qiyang             16333.3           605.3             256.3

Collecting       Isochlorogenic   Isochlorogenic   Isochlorogenic
locations            acid A           acid B           acid C

Qingdao              536.5            247.0            341.3
Inner Mongolia       3703.3           518.7            1416.7
Ningxia              260.7              --             417.7
Beijing                --               --               --
Suizhou              119.0              --             358.7
Zhangshu              63.6              --             285.7
Guizhou             32633.3           1643.3           6716.7
Yunnan               142.1              --               --
Henan                187.0            375.0            689.0
Anhui1               675.0            288.7            398.7
Tianjin              149.5            266.0            410.5
Xinjiang             212.0            321.3            781.7
Jiangsu                --               --               --
Bozhou               857.0            411.0            757.0
Anhui2               568.7            334.3            968.3
Shanxi                75.3              --               --
Tangshan               --               --               --
Liaoning             102.5              --               --
Zhejiang             165.0              --             385.0
Anhui3               256.7              --             330.3
Jiangsu              1380.0           363.0            720.0
Henan                  --               --               --
Hebei                15150            1795.0           7005.0
Chifeng              3953.3           585.3            1366.7
Chongqing           44633.3           3310.0          13300.0
Lingchuan            453.0              --             487.5
Shanxi               391.0              --             417.7
Shanghai               --               --               --
Xuzhou               5056.7           618.3            1423.3
Guangdong              --               --               --
Qiyang               2816.7           477.7            1090.0

Collecting       Caffeic   phenolic
locations         acid      acids

Qingdao           51.6     14321.7
Inner Mongolia    88.1     30018.1
Ningxia           82.0      8570.1
Beijing            --       1066.7
Suizhou           134.7     8407.4
Zhangshu           --       932.6
Guizhou           213.3    62929.3
Yunnan             --       629.2
Henan             119.0    13912.7
Anhui1            127.5    10724.5
Tianjin            --      12459.3
Xinjiang          141.0    12899.5
Jiangsu            --       746.3
Bozhou            125.7    25788.4
Anhui2            118.0    19433.3
Shanxi            144.3     6160.6
Tangshan           --       666.0
Liaoning          30.1      6095.0
Zhejiang          86.4     14389.7
Anhui3            105.7    15867.4
Jiangsu           141.3    25393.7
Henan             11.6      2850.9
Hebei            2190.0    83386.0
Chifeng           167.7    33829.6
Chongqing        3940.0    102263.3
Lingchuan         70.1      6759.2
Shanxi            140.0    18397.0
Shanghai          19.1      4207.4
Xuzhou            157.0    43598.3
Guangdong         100.2    16966.1
Qiyang            295.0    21874.3

TABLE 3: Contents of flavonoids in samples from different origins
(ng x [g.sup.-1]).

Collecting       Hyperin   Isoquercitrin   Quercetin   Campherol

Qingdao          19233.3      3240.0         49.3       3705.0
Inner Mongolia   34700.0      2276.7         60.1        451.0
Ningxia          31833.3      1600.0         81.5        788.3
Beijing          3983.3        558.0         19.9        91.3
Suizhou          15933.3      3200.0         255.0      3316.7
Zhangshu          732.7        121.7         21.8        166.0
Guizhou          3906.7        660.0         39.7        485.0
Yunnan           2220.0        431.0         17.9        86.7
Henan            30166.7      5500.0         155.0      4113.3
Anhui1           27000.0      3693.3         381.7      2356.7
Tianjin          20166.7      3533.3         119.0      3185.0
Xinjiang         12300.0      3380.0         78.8       5770.0
Jiangsu          6645.0       1226.7         20.0        186.0
Bozhou           23133.3      3723.3         44.2       4576.7
Anhui2           25433.3      4773.3         159.0      4066.7
Shanxi           13133.3       943.0         102.4      2273.3
Tangshan          880.0        209.0         21.9        196.7
Liaoning         13333.3      1400.0         19.6        112.5
Zhejiang         14133.3      2460.0         66.7       2800.0
Anhui3           20200.0      3676.7         54.5       4560.0
Jiangsu          29233.3      4030.0         106.5      2635.0
Henan            32250.0      6116.7         33.0        355.3
Hebei            17666.7       807.0         18.9        431.3
Chifeng          24066.7      1510.0         58.1        98.6
Chongqing        2326.7        472.0         20.1        426.3
Lingchuan        6836.7       1130.0         38.1       1273.3
Shanxi           32066.7      4366.7         116.7      3610.0
Shanghai         25600.0      4180.0         19.5        256.0
Xuzhou           24400.0      1793.3         19.0        268.3
Guangdong        7020.0       1206.7         14.0       1080.0
Qiyang           21666.7      3496.7         66.2       2190.0

Collecting       p-Coumaric   Isorhamnetin   Rutin    Astragalin
locations           acid

Qingdao            464.7          52.6       155.0     10266.7
Inner Mongolia     479.3          49.6       119.0      926.3
Ningxia            754.3          57.1       755.3      3270.0
Beijing            652.3          17.3        63.0      2270.0
Suizhou            779.3         123.0        51.1      7880.0
Zhangshu            89.2          15.1        69.7      320.7
Guizhou            278.0          26.6       3583.3     1890.0
Yunnan             266.0          12.7        46.7      3020.0
Henan              1373.3         99.6        56.7      5416.7
Anhui1             906.0         129.7       201.3      4630.0
Tianjin            724.0          99.7        34.6     10295.0
Xinjiang           592.7         128.1       100.7     26900.0
Jiangsu            690.7          16.1        96.0      6166.7
Bozhou             760.7          62.0       166.3      9186.7
Anhui2             504.7         143.3        48.0     19600.0
Shanxi             637.0          89.3       431.0      4393.3
Tangshan           959.0          15.3        29.9      2590.0
Liaoning           571.0          13.8       286.7      3203.3
Zhejiang           551.7          65.2        56.6      6360.0
Anhui3             445.3          73.9       103.2     10346.7
Jiangsu            588.0          75.1       141.0      8493.3
Henan              1153.3         17.3       322.7     29500.0
Hebei              1136.7         14.0       1316.7     1263.3
Chifeng            359.0          30.1       117.0      134.3
Chongqing          856.7          18.5       4063.3     1500.0
Lingchuan          259.3          36.8        72.0      4990.0
Shanxi             718.0          81.9        52.1      5030.0
Shanghai           458.7          21.7        68.1      5280.0
Xuzhou             446.3          18.8        49.3      986.3
Guangdong          227.3          13.0        91.2      3116.7
Qiyang             590.3          44.5       531.3      4956.7

Collecting       Apigenin     Total
locations                   flavonoids

Qingdao            1.1       37167.7
Inner Mongolia     0.8       39062.8
Ningxia            0.9       39140.7
Beijing            1.0        7656.1
Suizhou            1.3       31539.7
Zhangshu           0.6        1537.5
Guizhou            0.7        10870
Yunnan             0.7        6101.7
Henan              1.5       46882.8
Anhui1             1.6       39300.3
Tianjin            1.0       38158.3
Xinjiang           1.0       49251.3
Jiangsu            1.2       15048.4
Bozhou             1.2       41654.4
Anhui2             0.9       54729.2
Shanxi             0.9       22003.5
Tangshan           0.8        4902.6
Liaoning           1.1       18941.3
Zhejiang           16.4      26509.9
Anhui3             1.2       39461.5
Jiangsu            1.2       45303.4
Henan              1.2       69749.5
Hebei              0.8       22655.4
Chifeng            0.7       26374.5
Chongqing          2.5        9686.1
Lingchuan          0.7       14636.9
Shanxi             1.2       46043.3
Shanghai           1.1       35885.1
Xuzhou             0.7       27982.0
Guangdong          0.7       12769.6
Qiyang             1.5       33543.9
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Article Details
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Title Annotation:Research Article
Author:Du, Kun-ze; Li, Jin; Guo, Xinrong; Li, Yuhong; Chang, Yan-xu
Publication:Journal of Analytical Methods in Chemistry
Date:Jan 1, 2018
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