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Comparison of Aurantii Fructus Immaturus and Aurantii Fructus based on multiple chromatographic analysis and chemometrics methods
Accepted Manuscript Title: Comparison of Aurantii Fructus Immaturus and Aurantii Fructus based on multiple chromatographic analysis and chemometrics methods Author: Pei Li Su-Ling Zeng Li Duan Xiao-Dong Ma Li-Li Dou Lan-Jin Wang Ping Li Zhi-Ming Bi E-Hu Liu PII: DOI: Reference:
S0021-9673(16)31277-8 http://dx.doi.org/doi:10.1016/j.chroma.2016.09.061 CHROMA 357933
To appear in:
Journal of Chromatography A
Received date: Revised date: Accepted date:
21-6-2016 2-9-2016 25-9-2016
Please cite this article as: Pei Li, Su-Ling Zeng, Li Duan, Xiao-Dong Ma, Li-Li Dou, Lan-Jin Wang, Ping Li, Zhi-Ming Bi, E-Hu Liu, Comparison of Aurantii Fructus Immaturus and Aurantii Fructus based on multiple chromatographic analysis and chemometrics methods, Journal of Chromatography A http://dx.doi.org/10.1016/j.chroma.2016.09.061 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Comparison of Aurantii Fructus Immaturus and Aurantii Fructus based on multiple chromatographic analysis and chemometrics methods
Pei Li, Su-Ling Zeng, Li Duan, Xiao-Dong Ma, Li-Li Dou, Lan-Jin Wang, Ping Li, Zhi-Ming Bi*, E-Hu Liu*
State Key Laboratory of Natural Medicines, China Pharmaceutical University, No.24 Tongjia Lane, Nanjing 210009, China
*
Corresponding authors. Tel.: +86 25 83271379; fax: +86 25 83271379.
E-mail addresses: [email protected] (E.-H. Liu), [email protected] (Zhiming Bi)
Highlights
A strategy integrating chromatographic and chemometric methods was proposed.
HPLC-variable wavelength detection, SCX-HPLC and GC-MS method were established.
HCA, PCA, one-way ANOVA and PLS-DA were performed to discriminate AFI and AF.
Abstract: To get a better understanding of the bioactive constituents in Aurantii Fructus Immaturus (AFI) and Aurantii Fructus (AF), in the present study, a comprehensive strategy integrating multiple chromatographic analysis and chemometrics methods was firstly proposed. Based on segmental monitoring, a high-performance liquid chromatography (HPLC)-variable wavelength detection method was established for simultaneous quantification of ten major flavonoids, and the quantitative data were further analyzed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). A strong cation exchange-high performance liquid chromatography (SCX-HPLC) method combined with t-test and one-way analysis of variance (ANOVA) was developed to determine synephrine, the major alkaloid in AFI and AF. The essential oils were analyzed by gas chromatography-mass spectrometry (GC-MS) and further processed by partial least squares discrimination analysis (PLS-DA). The results indicated that the contents of ten flavonoids and synephrine in AFI were significantly higher than those in AF, and significant difference existed in samples from different geographical origins. Also, 9 differential volatile constituents detected could be used as chemical markers for discrimination of AFI and AF. Collectively, the proposed comprehensive analysis might be a well-acceptable strategy to evaluate the quality of traditional citrus herbs. Keywords: Aurantii Fructus Immaturus; Aurantii Fructus; Chromatographic analysis; Chemometrics methods
1. Introduction
Aurantii Fructus Immaturus (AFI, Zhishi in Chinese) and Aurantii Fructus (AF, Zhiqiao in Chinese) have been widely used as traditional Chinese medicines for a long time because of their biological activity, rich resources, low toxicity and costs. AFI is the dried immature fruits of Citrus aurantium L. or its cultivars or Citrus sinensis Osbeck, collected from May to June, while AF is the dried ripe fruits of Citrus aurantium L. or its cultivars, gathered from July [1]. Intriguingly, their pharmacological effects and clinical applications are different. AFI is commonly used to dissipate stagnant qi, eliminate sputum and disperse painful abdominal mass, while AF is mostly utilized to improve stagnation of dyspepsia and gastrointestinal function, and reduce chest pain [1]. It has been well claimed that the efficacy of herbal medicines is significantly correlated with the chemical composition and the contents of active compounds in herbs. In previous literatures, flavonoids, alkaloids and volatile oil were considered to be the major bioactive constituents in AFI and AF [2-5]. The abundant citrus flavonoids, especially O-diglycosyl flavanones and polymethoxyflavones (PMFs) [3, 6], have various medicinal benefits including antidepressant [2], anticancer, antioxidant, antiviral, vasoprotective, and anti-inflammatory activities [7-13]. The alkaloids in AFI and AF, like synephrine and N-methyltyramine are commonly used in food supplements aimed at reducing body weight or improving general performance
[14, 15]. The volatile oils in AFI and AF, are reported to have pharmacological activities like antimicrobial, anticancer, antimicrobial and antifungal [16-19]. Publications about AFI and AF are mainly focused on the identification and quantification of several flavonoids or alkaloids by high-performance liquid chromatography-photodiode
array
detector
(HPLC-DAD)
method
or
high-performance liquid chromatography- mass spectrometry (HPLC-MS) [20-27], none of them involved comprehensive chemical comparison using characteristic chemical compounds or a discrimination model between these two herbal medicines. In
the
present
study,
a
comprehensive
strategy
integrating
multiple
chromatographic analysis and chemometrics methods was firstly proposed to compare the flavonoids, alkaloids and essential oil in AFI and AF. A HPLC-variable wavelength detection method was established for simultaneous quantification of ten major flavonoids, and chemometrics methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were then performed to compare and discriminate the AFI and AF samples. Synephrine, the major alkaloid in AFI and AF, was analyzed and compared by a strong cation exchange-high performance liquid chromatography (SCX-HPLC) method coupled with t-test and one-way analysis of variance (ANOVA). Furthermore, gas chromatography-mass spectrometry (GC-MS) was applied for identification of essential oils in AFI and AF, and partial least squares discrimination analysis (PLS-DA) was utilized to find the potential chemical markers for discriminating these two herbal medicines.
2. Experimental
2.1. Materials and reagents
A total of 60 batches of commercial samples including 33 batches of AFI and 27 batches of AF were purchased from Jiangsu, Zhejiang, Sichuan, Hebei, Chongqing, Hubei, Shanxi, Tianjin, Hunan and Guangdong Province, China. The sample codes and origin were listed in Supplementary Table 1. The voucher specimens, identified by Associate Professor Zhiming Bi of China Pharmaceutical University, have been deposited in the State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China. The reference standards of eriocitrin (1), narirutin (2), naringin (3), hesperidin (4), neohesperidin (5), didymin (6), poncirin (7), nobiletin (8), tangeretin (9) and synephrine were purchased from Chengdu Must Bio-technology Co., Ltd. (Chengdu, China), and 5-hydroxy-6,7,8,3‟,4‟-pentamethoxyflavone (10) was isolated and purified from Pericarpium Citri Reticulatae by conventional column chromatography coupled with high speed countercurrent chromatography. The structures of 10 reference compounds are shown in Fig. 1, and the purities of them were determined to be higher than 98% by high performance liquid chromatography-diode array detection analysis. A mixture of n-alkanes (C8-C20, Lot: BCBK5370V) was purchased from Fluka (Buchs, Switzerland) to calculate the retention indices (RI) of all the volatile constituents.
The solvents, HPLC grade acetonitrile (ACN) and formic acid were purchased from Merck (Darmstadt, Germany); deionized water (18 MΩ) used for all solutions and dilutions was prepared by distilled water through a Milli-Q system (Millipore, Milford, MA, USA). Other reagents and chemicals are of analytical grade.
2.2. Instrument and Chromatography conditions
2.2.1 Instrument and Chromatography conditions for flavonoids analysis The flavonoids analysis was carried out on an Agilent series 1290 HPLC liquid chromatograph, equipped with a thermostated column compartment, an auto-sampler, a degasser, a quaternary pump and a diode array detector (Agilent Technologies, Palo Alto, CA, USA). Separation was performed on an Agilent ZORBAX Extend-C18 column (4.6 mm × 50 mm, 1.8m, Agilent Technologies, MD, USA). Mobile phase was composed of 0.3% aqueous formic acid (A) and acetonitrile (B) with a gradient elution as follows: 10-17% (B) in 0-2 min, 17% (B) in 2-8 min, 17-19% (B) in 8-10 min, 19% (B) in 10-12 min, 19-30% (B) in 12-16 min, 30-35% (B) in 16-22 min, 35% (B) in 22-23 min, 35-45% (B) in 23-24 min, 45-50% (B) in 24-28 min, 50-80% (B) in 28-30 min. The flow rate was 0.8 mL/min and the column temperature was 30 °C. Different detection wavelengths were performed for different periods of time: 283 nm for 0-19 min and 330 nm for 19-30 min.
2.2.2 Instrument and Chromatography conditions for synephrine analysis
The HPLC system used was a Shimadzu (LC-20AT) liquid chromatograph, equipped with a pump (LC-20AT), an auto-sampler (SIL-20A), a diode array detector (SPD-M20A) and an automatic column temperature control oven (CTO-20A). Separation was conducted on an Agilent ZORBAX 300-SCX column (2.1 mm × 150 mm, 5 m, Agilent Technologies, MD, USA) at 30 °C. The mobile phase consisted of aqueous ammonium dihydrogen phosphate solution (pH 4.9, 8 mM) (A) and acetonitrile (B) using an isocratic elution of 45% (B) in 0-18 min. The flow rate was kept at 0.3 mL/min and the detection wavelength was 224 nm.
2.2.3 Instrument and GC-MS analysis of volatile oils GC-MS analysis of volatile oils was performed on an Agilent 7890B gas chromatography instrument (Agilent Technologies, USA) coupled to an Agilent 5977 Mass Selective Detector (MSD). An HP-5MS capillary column (30 m × 0.25 mm i. d.) coated with a 0.25 m film of 5% phenyl methyl siloxane was used for the separation. High-purity helium was used as the carrier gas, with a flow rate of 1.0 mL/min. The splitting ratio was set at 40: 1, the injection temperature and the interface temperature to 250 °C, standard electronic impact MS source temperature to 230 °C and MS quadrupole temperature to 180 °C. The mass spectra plot was acquired using full scan monitoring mode with a mass scan range of m/z 45-450. The column temperature was set at 50 °C and programmed to rise at 4 °C/min to 90 °C (1 min held), 10 °C/min to 120 °C (1 min held), 3 °C/min to 160 °C (1 min held), 30 °C/min to 200 °C kept for 1 min and finally rising at 15 °C/min to 280 °C (1 min held).
2.3. Preparation of standard solutions Reference standards of ten flavonoids and synephrine were accurately weighed, respectively, then dissolved in methanol to form corresponding standard stock solutions. Standard working solutions were prepared from those stock solutions by further dilution with appropriate volume of methanol, and then stored at 4 °C or -20 °C before use.
2.4 Preparation of sample solutions
2.4.1 Preparation for flavonoids analysis The tested samples were ground into powder and passed through a 60-mesh (0.3 mm) sieve. About 0.5 g sample powder was exactly weighed into a 100 mL conical flask, added 25 mL methanol, weighed and sonicated for 30 min at 100 Hz. After cooled at room temperature, methanol was added to compensate for the lost weight. The extracts were filtered through 0.22m nylon filter membrane and preserved in 4 °C refrigerator before HPLC analysis.
2.4.2 Preparation for synephrine analysis About 0.5 g of finely ground and homogenized samples were accurately weighed. After being soaked for 1 h in 5 mL of 0.1 M HCl, 20 mL of a water: methanol (25: 75, v/v) solution were further added and sonicated for 30 min. When cooled to room
temperature, methanol was added to compensate for the lost weight. All samples were centrifuged at 13,000 rpm/min for 10 min prior to use, and one microliter of the supernatant of AFI or two microliters of the supernatant of AF was injected into the HPLC instrument for analysis.
2.4.3 Preparation for essential oils analysis 31 batches of AFI and AF samples were randomly selected, ground into coarse powder and accurately weighed. Using a standard apparatus set, the essential oils were extracted by steam distillation for 8 h with an amount of distilled water eight times the volume of the sample. Finally, white yellow or brown oil samples were collected, and the extraction yields of essential oil (mL/g) were shown in the Supplementary Table 2. Then they were dried using anhydrous sodium sulphate to remove traces of moisture, and stored in the dark at -20 °C prior to analysis. About 1 L of each essential oil solutions (dissolved in n-hexane as 1/250) and a series of n-alkanes (C8-C20) were separately analyzed by GC-MS.
2.5. Data analysis
PCA and HCA were carried out based on the quantification of ten flavonoids compounds in 60 samples, using SIMCA-P 14.1 (Umetrics, Sweden) and IBM SPSS Statistics 19 (IBM, Chicago) Software, respectively. When the target compounds were not detected or the contents of them less than the quantitation limit in the samples, the
values of such elements were considered to be 0. T-test and one-way ANOVA were employed with GraphPad Prism 6 (GraphPad Software, California) for analyzing the content of synephrine in 60 samples to discriminate the two herbal medicines. The relative contents of the 17 major volatile components in 31 batches of AFI and AF samples selected were used as input data for PCA and PLS-DA analyses. The program for PLS-DA was performed on SIMCA-P 12.
3. Results and discussion
3.1 Flavonoids comparison of AFI and AF
3.1.1. Optimization of extraction conditions In order to make a comparison on the content of flavonoids in AFI and AF, the extraction method (dipping, ultrasound, refluxing), time (30, 60, 90 min) and solvents (water, methanol, methanol-water (1:3, 1:1, 3:1, v/v), ethanol, ethanol-water (1:1, 3:1, v/v), water-saturated n-butanol) were optimized. Compared with refluxing extraction, ultrasound technique provided similar peak area of principal peaks but shorter extraction time. Thus, ultrasound extraction was selected in this study (the supplementary table 3). For extraction solvents, the results achieved indicated that different solvents significantly affected the extraction efficiencies. By comparing the extraction efficiency of ten flavonoids and the simplicity of the method, ultrasonic
extraction with pure methanol was eventually selected.
3.1.2. Optimization of HPLC conditions A segmental monitoring method based on HPLC-variable wavelength detection was performed for simultaneous quantification of ten flavonoids in AFI and AF samples. The UV detector was monitored at 283 nm for flavanone O-glycosides (eriocitrin (1), narirutin (2), naringin (3), hesperidin (4), neohesperidin (5), didymin (6), poncirin (7)) at 0-19 min and 330 nm for polymethoxyflavones (nobiletin (8), tangeretin (9), 5-hydroxy-6,7,8,3‟,4‟-pentamethoxyflavone (10)) at 19-30 min (Supplementary Fig. 1). In order to achieve a good separation of the analytes, different flow rate (0.5, 0.6, 0.7, 0.8 and 0.9 mL/min) and column temperature (20, 25, 30, 35 and 40 °C) were examined. Furthermore, formic acid (0.1%, 0.2% and 0.3%, v/v) was added into the mobile phase to improve the peak shape and restrain the peak tailing. As a result, a flow rate of 0.8 mL/min, a column temperature of 30 °C, the detection wavelength of 283 nm (0 ~ 19 min) and 330 nm (19 ~ 30 min) and mobile phase system (0.3% aqueous formic acid (A) and acetonitrile (B)) provided good separation and strong UV absorption for most constituents in AFI and AF. The typical LC chromatograms are shown in Fig. 2.
3.1.3 Method validation The proposed HPLC-DAD method was validated by determination of linearity, limit of detection (LOD), limit of quantification (LOQ), precision, stability,
repeatability and recovery. For the calibration curves, seven or eight working solution concentrations were analyzed. Between the corresponding peak areas and the concentrations, a good linear relationship was obtained. LOD and LOQ were determined by injecting serial diluted standard working solutions and taking generated peaks with signal-to-noise ratio of 3 and 10 as criteria, respectively. The precision of the method was evaluated by the determination of intra- and inter-day variances. The intra-day variability test was analyzed at three different concentration levels (low, medium, high) with six replicates at each level on the same day, whereas the inter-day variability test was conducted in duplicates on the consecutive three days. To confirm the stability, the same sample was stored at 4 °C and room temperature and evaluated by replicate injection at 0, 2, 4, 8, 12 and 18 h. For the repeatability test, six replicates of the same samples were extracted and analyzed. Recovery was used to further evaluate the accuracy of the method by calculating the mean recoveries of the analytes. Three different concentration levels (approximately equivalent to 0.5, 1.0 and 1.5 times of the concentration of the target compounds in sample) of the standard solutions were added with three parallels at each level. The relative standard deviations (RSDs) were calculated as the measures of them. As shown in Supplementary Table (4-6), all the calibration curves showed good linearity (r2 >0.9993) over the wide concentration ranges. The RSD values of intra-day precisions and inter-day precisions were less than 3.73% and 4.69%, respectively. The stability study demonstrated that the sample was stable within 18 h at room temperature. The overall recoveries of all the compounds were ranged from
87.2% to 103.7% with RSD values less than 2.37%. The verified method was precise and accurate for the simultaneous determination of the ten flavonoids.
3.1.4. Quantitative analysis of ten flavonoids in AFI and AF samples A total of 60 batches of different herbal samples were collected and analyzed by the method described above. The quantitative results are presented in Table 1. It was found that the total contents of ten analytes in AFI (the mean value was 222.24 mg/g) were significantly higher than those in AF (the mean value was 83.89 mg/g). The content disparity might be explained by the different collection periods of AFI and AF, which caused the differences in the accumulation of secondary metabolites. The data also indicated that both naringin and neohesperidin were the predominant compounds in most AFI and AF samples. For AFI samples from Sichuan and Hunan province, narirutin and hesperidin were abundantly present, while neohesperidin and poncirin were almost not detected. For some batches of AF samples from Sichuan and Yunnan province, the content of narigin was significantly higher than other compounds analyzed. Moreover, it should be noted that the contents of three PMFs were very low in AFI and AF, even could not be quantified or detected in several samples. Besides, the total flavonoids contents of samples from Jiangxi were relatively higher compared with other provinces. The content distributions of ten flavonoids in 60 samples were displayed in Supplement Fig.2.
3.1.5 Discrimination of AFI and AF based on chemometrics
To provide more information about the chemical differences of AFI and AF, HCA and PCA were conducted based on the contents of ten bioactive flavonoids [28-29]. In HCA, a method named as ward linkage was applied, and square euclidean distance was selected as a measurement for analysis. As shown in Fig. 3, AF1-AF27 could be included in A cluster, AFI1-AFI21 and AFI32- AFI33 in B cluster and AFI22-AFI31 in the C cluster. They, in turn, represent AF samples, AFI samples from Jiangxi, Hubei, Anhui and Yunnan province, and AFI samples from Sichuan, Hunan and Zhejiang province, respectively. In PCA, as displayed in the score scatter 3D plot (Fig. 4) and the loading scatter 3D plot (Supplementary Fig. 3), the first, second and third principal components described 39.9%, 29.1% and 17.9% of the variability in the original observations, respectively, and the first three principal components (PC1, PC2, PC3) accounted for 86.9% of total variance. The PCA results were consistent with HCA analysis, 60 sample dots were classified into three sample groups, corresponding to AFI samples (AFI1-AFI21, AFI32-AFI33), AFI samples (AFI22-AFI31) and AF samples (AF1-AF27), respectively. Besides, dots present in each group were relatively nearer to each other, indicating a closer relationship among them. From the loading scatter plot, it could be observed that most of the variables have made considerable contributions in samples differentiation, and the variables located near to each other had the same effect on similarity and dissimilarity of different samples. The chemometrics analysis results indicated the contents of the flavonoids in AFI and AF were significantly different, and the samples from different geographical origins could be discriminated based on quantitative analysis.
3.2 Alkaloid comparison of AFI and AF
3.2.1. Optimization of extraction conditions and HPLC conditions Synephrine is a highly water-soluble sympathomimetic amine, and could not be easily retained on C18 stationary phase. In order to determine synephrine in AFI and AF, a SCX column, which has specificity for alkaloids, was selected in this study based on the principle of high performance ion exchange chromatography. To obtain appropriate extraction and good chromatographic behavior for synephrine, different extraction solvents (Supplementary Fig. 4A), extraction time (Supplementary Fig. 4B), extraction methods and mobile phase system were examined and compared. The results demonstrated that the optimum extraction of synephrine from AFI and AF were as follows: About 0.5 g of sample was weighed accurately. After being soaked for 1 h in 5 mL of 0.1 M HCl, 20 mL of 75% methanol were further added and sonicated for 30 min. The isocratic eluent of 55% A (8 mM ammonium dihydrogen phosphate aqueous solution) and 45% B (acetonitrile) at a flow rate of 0.3 ml/min with the column temperature of 30 °C resulted in a satisfactory separation of synephrine in a short analysis time. The typical LC chromatograms are shown in Fig. 5.
3.2.2. Quantitative analysis of synephrine in AFI and AF 3.2.2.1 Method validation
The linearity of calibration curve was measured by analyzing eight working stock solutions. The stability was carried out at room temperature and analyzed at 0, 2, 4, 8, 12 and 24 h within one day, respectively. The LOD, LOQ, precision, repeatability and recovery were determined as the same as the method described above in 3.1.3. The calibration curves exhibited good linearity (r2 > 0.9999) with a relatively wide range of concentrations (from 1.1 g/mL to 540 g/mL). The LOD and LOQ were less than 0.185 g/ml and 0.417g/ml, respectively. The intra- and inter-day variations (RSDs) for synephrine were less than 2%. The repeatability presented as RSD was 2.54%, and the stability was 3.56%. The recoveries varied between 96.1% and 103.5% with RSDs less than 5%. The above data (Supplementary Table 7-8) were considered to be satisfactory for subsequent analysis of all the samples.
3.2.2.2 Quantitative analysis of synephrine in AFI and AF samples The SCX-HPLC method described above was applied to determine synephrine, the predominant alkaloid in 60 batches of different AFI and AF samples. The quantitative results are shown in Table 2. To compare the content of synephrine in AFI and AF, t-test was firstly carried out to discriminate these two medicinal herbs (Fig. 6A), then one-way ANOVA followed by Tukey's multiple comparisons test with two tailed P value < 0.05 as significant was used to evaluate the difference in AFI or AF of different geographical origins (Fig. 6B, 6C). The results demonstrated that the contents of synephrine in AFI were significantly higher than those in AF. And p-value of 0.0001 suggested that there was
a dramatic variation of synephrine between AFI and AF. Moreover, the synephrine content in AFI from Sichuan and Hunan province was slightly higher than from Jiangxi province, revealing that a significant difference also occurred in AFI of different sources. However, the differences of synephrine content were not significant among the AF samples. The developed method for analysis of synephrine could be helpful for discrimination and classification of AFI and AF samples, as well as facilitating their rational medical use.
3.3 volatile oils comparison of AFI and AF
3.3.1. GC-MS analysis of the essential oils from AFI and AF All the samples were analyzed as the GC-MS method described in 2.2.3. The typical total ion chromatograms (TIC) of AFI and AF are shown in Fig. 7. The volatile components were identified by comparison of their mass spectra with those recorded in the National Institute of Standards and Technology (NIST) mass spectral library and further confirmed by comparing their retention indices with published literatures [30-34] and NIST11 library. The relative contents of the volatile constituents were calculated using the area normalization method (the peaks whose area beyond 10000 were labelled and automatically integrated). The quantitative and qualitative results of the essential oils in AFI and AF are shown in Supplementary Table 9.
3.3.2. Comparison of volatile oil in AFI and AF
The Supplementary Table 2 showed the global yields of volatile oil in AFI and AF. On average, the total volatile oil content in AFI from Jiangxi province was significantly higher (0.94%) than AF (0.47%). In this work, 63 compounds were identified in AFI and 62 compounds identified in AF. Most of them belonged to monoterpenes and sesquiterpenes. As shown in the Supplementary Table 8, the most predominant volatile constituent was D-limonene (57.4% on average in AFI and 67.2% in AF, respectively), followed by γ-terpinene (12.2% in AFI and 8.2% in AF, respectively), linalool, o-cymene, germacrene D, β-myrcene, β-pinene, α-pinene, trans-β-ocimene, terpinolene, α-terpineol, and δ-Cadinene, the mean amounts of which were higher than 0.5%. Other compounds, such as cis-linaloloxide, terpinen-4-ol, caryophyllene, α-terpinene and β-elemene, occurred at a relatively low proportion between 0.1% ~ 0.5%. Besides, some trace components such as cis-p-Menth-2-en-1-ol, thymol methyl ether and α-cubebene were only identified in AFI, while 1,3,8-p-Menthatriene, p-Menth-1-en-9-al, geranyl acetate and elemol were only found in AF. The results indicated that variation in chemical compositions occurred during the period of growth.
3.3.3 Discrimination of AFI and AF based on chemometrics Although most compounds presented in the essential oils of AFI and AF are the same, their contents are different. Here, 17 main common constituents (Table 3), the contents of which were higher than 0.1% in almost all batches of AFI and AF samples, were selected and entered into analysis.
PCA was firstly employed to visualize the classification trends. As shown in the supplementary figure 5, the discrimination of AFI and AF, and the difference in AFI or AF of different geographical origins were not significant. The PLS-DA method was subsequently applied, which uses class information to maximize the separation between classes and minimize the distance between intragroup clustering. It can enable the reduction of large data into PC1, PC2 or PC3 with relative score plots showing clear separation among samples. As shown in Fig. 8, samples could be clearly classified as one of two groups corresponding to AFI and AF. Furthermore, it was found that there were notable differences among AFI and AF of different geographical origins, but these intragroup differences lacked abundant proof. Even though, the results confirmed that the volatile compounds in AFI and AF samples were different in terms of the values of fitting parameters (Q2=0.767; R2Y=0.878), which indicated that the PLS-DA model had a good predictive capability and a significant explanatory power on the discrimination of the samples. Moreover, the variable important for the projection (VIP) values revealed that α-Terpinene, β-Elemene,
D-Limonene, γ-Terpinene, Terpinen-4-ol,
β-Pinene,
trans-β-Ocimene, o-Cymene, and cis-Linaloloxide, whose values beyond 1, contributed significantly to the intergroup differences. Table 3 shows that, AFI contains more β-Pinene, α-Terpinene, β-Elemene, γ-Terpinene, trans-β-Ocimene, Terpinen-4-ol and γ-Terpinene, but less D-Limonene and cis-Linaloloxide compared with AF. These components (VIP>1) might serve as potential chemical markers for discrimination of AFI and AF.
Flavonoids, alkaloids and volatile oil are the major bioactive constituents in AFI and AF. As described above, we established three different methods to discriminate AFI and AF. Any one of them was independent of each other and complementary. Based on the contents of ten flavonoids and synephrine, the developed identification model could be used to differentiate AFI and AF samples, and the difference in samples from different geographical origins could also be found out by the models. In addition, the AFI and AF samples could be distinguished by the method according to the relative contents of essential oils. All the developed methods are reliable, simple and feasible, and can be applied to evaluate the quality of traditional citrus herbs.
4. Conclusion
In this paper, the flavonoids, alkaloids and essential oils in AFI and AF were analyzed by the developed HPLC-variable wavelength detection, SCX-HPLC and GC-MS method, respectively. The established methodology displayed acceptable levels of linearity, precision, repeatability and accuracy. And chemometrics analysis (HCA, PCA, t-test, one-way ANOVA and PLS-DA) were performed to discriminate the two herbal medicines based on the quantitative data. All results indicated that AFI and AF samples could be discriminated from each other, and significant difference existed in samples from different geographical origins. The proposed method could be
employed for quality control of AFI and AF, and the comparative results will facilitate better understanding of their different traditional uses.
Acknowledgments
The authors greatly appreciate financial support from National Natural Science Foundation of China (81473343, 81673569), Program for New Century Excellent Talents in University (NECT-13-1034), “Six Talent Peaks Program” of Jiangsu Province of China (2013-YY-001) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Legend to figures
Fig. 1. Chemical structures of 11 reference substances (ten flavonoids and one alkaloid) Fig. 2. The typical HPLC chromatograms of AFI and AF. The peak numbers are in accordance with the flavonoid compound numbers in Fig.1. Fig. 3. Dendrograms of hierarchical cluster analysis for 60 samples based on the content of ten flavonoids compounds Fig. 4. PCA score scatter plot of 60 samples. 1 corresponding to AFI samples (AFI1-AFI21, AFI32-AFI33), 2 corresponding to AFI samples (AFI22-AFI31), and 3 corresponding to AF samples (AF1-AF27) Fig. 5. The typical LC chromatograms of AFI and AF. (1. Synephrine) Fig. 6. T-test and one-way ANOVA of 60 samples based on the content of synephrine. (JX: Jiangxi; SC: Sichuan; HN: Hunan; Other: other provinces) Fig. 7. GC-MS typical total ion chromatograms of the AFI and AF samples Fig. 8. PLS-DA score scatter plot of 31 samples based on seventeen volatile oils. (A: Jiangxi, B: Sichuan, C: Hunan, D: Jiangxi, E: Sichuan and Hunan)
Tables
Table 1 The content of ten flavonoids in AFI and AF samples (mg/g, n = 3). Sample
1
2
3
4
5
6
7
8
9
10
Sample
1
2
3
4
5
6
7
8
9
10
AFI1
1.76
11.92
97.47
62.38
110.12
0.83
3.55
1.08
0.70
0.19
AFI31
0.51
28.27
21.91
104.14
19.16
5.55
0.54
0.50
0.23
0.06
AFI2
1.74
11.91
90.27
60.93
113.36
0.80
3.34
1.11
0.72
0.21
AFI32
0.91
14.01
98.57
8.38
74.16
0.17
3.97
0.70
0.45
0.06
AFI3
1.53
10.91
24.45
93.98
46.38
1.51
0.14
0.92
0.61
0.18
AFI33
2.56
8.30
94.35
8.58
91.21
0.14
0.73
0.55
0.20
0.07
AFI4
2.04
11.25
113.51
27.37
98.22
0.41
4.17
1.52
1.03
0.23
AF1
0.23
3.42
43.33
1.93
31.61
-
3.05
0.63
0.25
0.06
AFI5
0.78
1.09
132.83
1.05
169.55
-
1.64
0.83
0.42
0.08
AF2
0.61
6.44
67.06
3.45
31.83
0.20
5.42
1.52
1.07
0.12
AFI6
1.40
10.19
92.94
32.25
116.41
0.59
4.99
1.22
0.76
0.18
AF3
0.11
1.85
40.21
0.95
29.18
-
2.82
0.54
0.22
*
AFI7
1.49
10.54
75.50
51.57
71.57
1.15
1.27
0.76
0.38
0.13
AF4
0.08
6.91
37.96
4.91
23.74
0.21
1.81
0.43
0.19
-
AFI8
2.04
13.85
102.08
60.97
113.88
0.85
3.78
1.07
0.70
0.20
AF5
0.46
5.78
59.53
2.10
27.43
0.08
4.32
1.15
0.93
0.10
AFI9
1.09
26.73
59.06
88.17
87.23
3.52
2.48
0.47
0.24
-
AF6
0.28
3.79
47.05
2.41
31.33
0.06
2.92
0.56
0.34
0.04
AFI10
0.69
23.97
102.61
13.72
83.32
0.20
5.49
0.74
0.47
0.05
AF7
0.06
3.05
53.38
4.07
47.85
0.10
2.85
0.25
0.06
-
AFI11
3.90
8.73
89.69
14.40
155.79
0.13
0.36
0.83
0.47
0.18
AF8
0.69
5.46
75.50
0.95
34.84
0.13
6.71
2.24
1.39
0.16
AFI12
2.36
8.98
158.13
10.15
125.23
0.10
5.32
1.63
1.06
0.29
AF9
0.58
4.60
44.83
2.62
32.08
0.09
1.49
0.38
0.16
0.05
AFI13
2.47
12.64
169.53
3.05
86.53
0.17
5.30
2.02
1.42
0.30
AF10
0.27
6.19
26.66
18.16
20.78
1.59
0.47
0.30
0.05
*
AFI14
1.89
7.36
136.06
13.18
173.46
0.30
4.18
1.50
0.91
0.30
AF11
0.29
4.37
34.65
9.29
20.88
0.71
0.25
0.24
0.04
*
AFI15
1.53
7.90
144.10
12.04
152.52
0.21
6.55
1.81
1.09
0.25
AF12
0.37
3.58
25.98
4.28
18.28
0.18
0.16
0.19
0.03
-
AFI16
1.56
17.21
83.29
76.32
44.79
2.22
0.19
0.50
0.23
0.13
AF13
0.31
10.04
43.05
6.29
26.50
0.12
3.56
0.75
0.41
0.04
AFI17
1.73
14.95
56.33
83.19
53.28
1.88
0.34
0.77
0.53
0.20
AF14
0.47
3.83
43.86
1.67
28.05
-
0.22
0.25
0.09
*
AFI18
2.51
9.23
57.63
73.48
97.27
1.18
0.26
1.06
0.60
0.18
AF15
0.39
5.60
53.49
2.99
40.27
*
0.71
0.20
0.06
-
AFI19
1.62
12.87
35.61
93.56
49.02
1.86
0.16
1.09
0.73
0.19
AF16
0.75
5.04
62.59
1.90
33.90
*
4.70
1.53
0.98
0.12
AFI20
3.70
7.33
91.31
15.15
144.83
0.33
0.68
0.32
0.14
AF17
0.54
5.21
54.50
0.32
20.81
*
3.14
1.27
0.94
0.10
AFI21
1.05
8.32
98.38
0.50
1.73
0.43
0.14
0.07
AF18
0.72
4.68
53.17
2.01
39.42
*
0.47
0.35
0.13
0.05
AFI22
0.05
6.71
-
84.89
AFI23
0.40
34.95
-
119.60
-
4.17
-
6.33
3.60
1.02
AF19
0.35
3.55
37.95
3.33
19.94
0.12
2.98
1.69
0.96
0.10
-
6.59
-
0.50
0.27
0.10
AF20
0.38
6.71
55.93
4.20
44.25
0.08
1.25
0.27
0.13
0.04
AFI24
0.24
11.45
102.37
-
4.92
-
0.81
0.07
0.06
AF21
0.49
3.13
48.02
0.36
24.52
0.05
3.11
1.79
1.14
0.14
AFI25
0.46
40.16
-
113.76
-
7.38
-
0.10
*
-
AF22
0.59
3.77
43.15
2.27
36.47
-
0.53
0.31
0.15
0.04
AFI26
0.28
31.73
-
108.84
-
9.71
-
1.23
0.47
0.14
AF23
0.11
-
44.60
-
-
0.14
0.39
-
-
-
AFI27
0.44
30.04
-
108.26
-
5.41
-
0.12
*
-
AF24
0.13
0.19
48.71
0.40
0.11
0.19
0.35
*
-
0.02
AFI28
0.08
20.82
0.02
105.55
-
7.57
-
1.05
0.13
0.06
AF25
0.59
4.87
50.93
4.27
34.72
0.07
0.69
0.38
0.20
0.05
AFI29
0.48
33.86
0.02
112.88
-
5.88
-
0.17
0.01
-
AF26
*
-
41.86
*
0.28
0.10
-
-
-
AFI30
0.12
10.42
3.16
93.11
*
4.19
-
1.49
0.77
0.24
AF27
0.08
0.65
18.75
*
-
0.15
0.08
*
-
24.02
- not detected * under limit of quantification
12.35
68.51
* 14.36
Table 2 The content of synephrine in AFI and AF samples (mg/g, n = 3). Sample
Mean ± SD
Sample
Mean ± SD
Sample
Mean ± SD
Sample
Mean ± SD
AFI1
5.68 ± 0.10
AFI16
3.46 ± 0.09
AFI31
4.68 ± 0.06
AF13
1.17 ± 0.00
AFI2
6.25 ± 0.01
AFI17
6.22 ± 0.05
AFI32
3.33 ± 0.03
AF14
0.21 ± 0.00
AFI3
5.25 ± 0.05
AFI18
4.14 ± 0.03
AFI33
1.79 ± 0.07
AF15
0.50 ± 0.02
AFI4
5.40 ± 0.09
AFI19
5.34 ± 0.01
AF1
1.27 ± 0.02
AF16
1.03 ± 0.01
AFI5
6.484 ± 0.06
AFI20
2.54 ± 0.01
AF2
1.92 ± 0.05
AF17
1.83 ± 0.01
AFI6
5.39 ± 0.03
AFI21
0.98 ± 0.01
AF3
1.09 ± 0.02
AF18
0.29 ± 0.00
AFI7
7.76 ± 0.06
AFI22
12.28 ± 0.11
AF4
1.66 ± 0.02
AF19
1.34 ± 0.01
AFI8
2.71 ± 0.02
AFI23
6.06 ± 0.06
AF5
2.42 ± 0.02
AF20
0.43 ± 0.00
AFI9
5.92 ± 0.07
AFI24
4.60 ± 0.04
AF6
1.30 ± 0.01
AF21
1.41 ± 0.01
AFI10
2.30 ± 0.01
AFI25
7.81 ± 0.06
AF7
1.64 ± 0.01
AF22
0.19 ± 0.00
AFI11
3.37 ± 0.02
AFI26
5.97 ± 0.03
AF8
2.23 ± 0.02
AF23
*
AFI12
7.27 ± 0.03
AFI27
6.46 ± 0.03
AF9
0.10 ± 0.01
AF24
*
AFI13
5.42 ± 0.03
AFI28
6.52 ± 0.05
AF10
0.41 ± 0.01
AF25
0.22 ± 0.00
AFI14
7.50 ± 0.04
AFI29
8.98 ± 0.07
AF11
0.21 ± 0.00
AF26
*
AFI15
4.71 ± 0.03
AFI30
7.00 ± 0.02
AF12
0.19 ± 0.00
AF27
*
* under limit of quantification
Table 3 The relative contents of 17 main common compounds in AFI and AF samples. α-Pine
β-Pine
β-Myrce
α-Terpinen
o-Cymen
D-Limone
trans-β-
γ-Terpinen
cis-Linalolox
Terpin
ne
ne
ne
e
e
ne
Ocimene
e
ide
olene
Terpinen
α-Terpine
β-Eleme
Caryophy
Germacre
δ-Cadine
-4-ol
ol
ne
llene
ne D
ne
AFI1
1.69
2.16
0.88
0.53
4.03
45.51
4.19
18.91
0.75
1.36
9.75
0.42
0.48
1.09
0.31
3.48
0.31
AFI3
1.56
2.02
0.73
0.52
4.05
35.94
4.62
18.12
1.46
1.76
15.17
0.54
0.72
1.8
0.38
4.97
0.43
AFI6
1.4
1.76
0.86
0.46
4.13
48.07
3.68
16.44
1.15
1.53
9.31
0.51
0.65
1.13
0.37
3.61
0.39
AFI7
1.24
1.6
0.73
0.32
4.72
48.61
0.76
13.16
0.32
0.84
0.99
0.27
0.31
1.64
0.88
10.07
1.24
AFI8
0.79
0.87
0.74
0.34
3.86
57.35
0.44
11.79
0.26
0.78
1.3
0.56
0.39
1.28
0.79
8.45
0.95
AFI9
1.25
1.22
1.12
0.93
3.13
60.28
2.51
12.59
0.71
1.13
4.87
1.97
0.53
0.28
0.35
2.61
0.27
AFI17
0.6
0.81
0.64
0.25
6.35
54.36
0.36
9.01
0.55
0.84
2.77
0.81
0.78
1.56
0.66
4.58
1.47
AFI22
1.28
0.74
1.1
0.35
3.62
72.66
10.11
0.08
0.76
3.45
0.1
0.91
1.03
-
-
0.21
0.12
AFI23
0.64
0.75
0.71
0.26
4.38
57.81
0.37
10.11
-
0.67
3.45
0.61
0.53
2.02
0.59
1.76
0.98
AFI24
0.71
AFI25
0.65
1.02
1.02
0.57
2.88
66.42
2.07
12.23
0.18
0.84
4
1.81
0.79
0.17
0.15
0.5
0.14
0.65
0.66
0.21
3.52
52.7
0.87
9.31
-
0.63
0.72
0.23
0.7
2.34
0.88
2.55
2.32
AFI26
0.97
0.68
0.94
0.44
2.52
73.82
0.53
8.05
0.52
2.41
0.18
1.38
0.47
0.24
0.24
0.59
0.26
AFI27
1.18
1.67
0.94
0.34
5.54
52.91
3.22
15.51
0.76
3.02
0.23
0.53
1.68
0.44
1.47
0.49
AFI29
1.12
1.19
1.05
0.5
6.05
63.14
1.86
11.48
-
0.71
2.54
0.84
0.39
1.54
0.29
0.6
0.8
AFI30
0.41
0.55
0.7
0.61
3.64
60.92
2.66
15.56
0.13
1.25
3.06
2.08
1.76
0.36
0.34
1.18
0.6
AF2
0.81
0.58
-
0.19
2.43
68.16
0.5
8.21
1.46
1.33
7.28
0.41
1.22
0.37
0.13
1.55
0.57
AF3
0.85
0.76
0.92
0.23
3.42
61.31
1.01
9.26
1.94
1.59
4.61
0.56
1.09
0.57
0.46
3.67
0.77
AF4
0.41
0.66
0.81
0.26
2.78
62.49
1.43
9.5
1.12
1.25
4.26
0.82
1.15
0.52
0.38
3.27
0.64
AF5
0.51
0.44
0.78
0.16
2.65
56.44
0.61
7.74
2.64
1.77
10.22
0.74
2.34
0.66
0.24
2.37
1.07
AF6
0.87
0.74
1
0.27
3.42
63.42
0.78
7.85
1.77
1.42
4.55
0.77
1.2
0.25
0.31
2.31
0.64
AF7
0.83
1.63
0.83
0.18
2.27
67.36
0.58
5.7
0.92
0.82
3.28
0.67
1.24
0.52
0.25
3.62
0.72
AF8
0.85
0.6
1.08
0.17
2.45
77.95
0.36
8.36
0.63
0.79
3.36
0.24
0.58
0.11
-
1.01
0.25
Sample
Linalol
AF15
1.01
0.73
1.15
0.19
1.17
74.28
0.51
8.9
0.53
0.7
5.32
0.17
0.47
0.23
0.09
1.62
0.31
AF16
0.96
1.09
0.97
0.23
3.26
68.15
1.16
9.97
0.21
0.65
2
0.38
0.52
0.52
0.23
3.47
0.59
AF17
1.16
1.04
1.02
0.19
6.1
73.81
0.48
9.33
0.33
0.84
0.32
0.3
0.66
-
0.4
0.75
0.37
AF18
0.97
0.92
0.91
0.19
2.82
66.16
0.44
7.87
0.26
0.53
2.19
0.44
0.69
0.73
0.3
5.9
0.77
AF19
0.77
0.8
0.72
0.2
2.11
57.84
0.43
8.64
0.11
0.47
0.78
0.26
0.41
1.4
0.56
12.83
1.54
AF21
0.93
0.6
1.11
0.23
2.83
62.71
0.42
7.74
1.65
1.55
7.46
0.55
1.56
0.51
0.19
2.26
0.86
AF22
0.91
0.66
1.06
0.22
3.1
66.64
0.61
8.13
1.74
1.55
5.65
0.48
1.53
0.32
0.16
1.62
0.81
AF23
1.08
1.01
2.4
0.14
3.38
75.7
0.08
7.52
0.2
0.46
0.12
0.15
0.24
0.49
0.5
3.55
0.25
AF25
0.77
0.46
1.01
0.19
3.83
65.04
0.49
6.67
2.06
1.77
5.98
0.52
1.95
0.46
0.23
1.44
1.18
“ - ”peak area less than 10000 or not detected. The bolded compound are the volatile oils, whose VIP value >1
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
S0021-9673(16)31277-8 http://dx.doi.org/doi:10.1016/j.chroma.2016.09.061 CHROMA 357933
To appear in:
Journal of Chromatography A
Received date: Revised date: Accepted date:
21-6-2016 2-9-2016 25-9-2016
Please cite this article as: Pei Li, Su-Ling Zeng, Li Duan, Xiao-Dong Ma, Li-Li Dou, Lan-Jin Wang, Ping Li, Zhi-Ming Bi, E-Hu Liu, Comparison of Aurantii Fructus Immaturus and Aurantii Fructus based on multiple chromatographic analysis and chemometrics methods, Journal of Chromatography A http://dx.doi.org/10.1016/j.chroma.2016.09.061 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Comparison of Aurantii Fructus Immaturus and Aurantii Fructus based on multiple chromatographic analysis and chemometrics methods
Pei Li, Su-Ling Zeng, Li Duan, Xiao-Dong Ma, Li-Li Dou, Lan-Jin Wang, Ping Li, Zhi-Ming Bi*, E-Hu Liu*
State Key Laboratory of Natural Medicines, China Pharmaceutical University, No.24 Tongjia Lane, Nanjing 210009, China
*
Corresponding authors. Tel.: +86 25 83271379; fax: +86 25 83271379.
E-mail addresses: [email protected] (E.-H. Liu), [email protected] (Zhiming Bi)
Highlights
A strategy integrating chromatographic and chemometric methods was proposed.
HPLC-variable wavelength detection, SCX-HPLC and GC-MS method were established.
HCA, PCA, one-way ANOVA and PLS-DA were performed to discriminate AFI and AF.
Abstract: To get a better understanding of the bioactive constituents in Aurantii Fructus Immaturus (AFI) and Aurantii Fructus (AF), in the present study, a comprehensive strategy integrating multiple chromatographic analysis and chemometrics methods was firstly proposed. Based on segmental monitoring, a high-performance liquid chromatography (HPLC)-variable wavelength detection method was established for simultaneous quantification of ten major flavonoids, and the quantitative data were further analyzed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). A strong cation exchange-high performance liquid chromatography (SCX-HPLC) method combined with t-test and one-way analysis of variance (ANOVA) was developed to determine synephrine, the major alkaloid in AFI and AF. The essential oils were analyzed by gas chromatography-mass spectrometry (GC-MS) and further processed by partial least squares discrimination analysis (PLS-DA). The results indicated that the contents of ten flavonoids and synephrine in AFI were significantly higher than those in AF, and significant difference existed in samples from different geographical origins. Also, 9 differential volatile constituents detected could be used as chemical markers for discrimination of AFI and AF. Collectively, the proposed comprehensive analysis might be a well-acceptable strategy to evaluate the quality of traditional citrus herbs. Keywords: Aurantii Fructus Immaturus; Aurantii Fructus; Chromatographic analysis; Chemometrics methods
1. Introduction
Aurantii Fructus Immaturus (AFI, Zhishi in Chinese) and Aurantii Fructus (AF, Zhiqiao in Chinese) have been widely used as traditional Chinese medicines for a long time because of their biological activity, rich resources, low toxicity and costs. AFI is the dried immature fruits of Citrus aurantium L. or its cultivars or Citrus sinensis Osbeck, collected from May to June, while AF is the dried ripe fruits of Citrus aurantium L. or its cultivars, gathered from July [1]. Intriguingly, their pharmacological effects and clinical applications are different. AFI is commonly used to dissipate stagnant qi, eliminate sputum and disperse painful abdominal mass, while AF is mostly utilized to improve stagnation of dyspepsia and gastrointestinal function, and reduce chest pain [1]. It has been well claimed that the efficacy of herbal medicines is significantly correlated with the chemical composition and the contents of active compounds in herbs. In previous literatures, flavonoids, alkaloids and volatile oil were considered to be the major bioactive constituents in AFI and AF [2-5]. The abundant citrus flavonoids, especially O-diglycosyl flavanones and polymethoxyflavones (PMFs) [3, 6], have various medicinal benefits including antidepressant [2], anticancer, antioxidant, antiviral, vasoprotective, and anti-inflammatory activities [7-13]. The alkaloids in AFI and AF, like synephrine and N-methyltyramine are commonly used in food supplements aimed at reducing body weight or improving general performance
[14, 15]. The volatile oils in AFI and AF, are reported to have pharmacological activities like antimicrobial, anticancer, antimicrobial and antifungal [16-19]. Publications about AFI and AF are mainly focused on the identification and quantification of several flavonoids or alkaloids by high-performance liquid chromatography-photodiode
array
detector
(HPLC-DAD)
method
or
high-performance liquid chromatography- mass spectrometry (HPLC-MS) [20-27], none of them involved comprehensive chemical comparison using characteristic chemical compounds or a discrimination model between these two herbal medicines. In
the
present
study,
a
comprehensive
strategy
integrating
multiple
chromatographic analysis and chemometrics methods was firstly proposed to compare the flavonoids, alkaloids and essential oil in AFI and AF. A HPLC-variable wavelength detection method was established for simultaneous quantification of ten major flavonoids, and chemometrics methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were then performed to compare and discriminate the AFI and AF samples. Synephrine, the major alkaloid in AFI and AF, was analyzed and compared by a strong cation exchange-high performance liquid chromatography (SCX-HPLC) method coupled with t-test and one-way analysis of variance (ANOVA). Furthermore, gas chromatography-mass spectrometry (GC-MS) was applied for identification of essential oils in AFI and AF, and partial least squares discrimination analysis (PLS-DA) was utilized to find the potential chemical markers for discriminating these two herbal medicines.
2. Experimental
2.1. Materials and reagents
A total of 60 batches of commercial samples including 33 batches of AFI and 27 batches of AF were purchased from Jiangsu, Zhejiang, Sichuan, Hebei, Chongqing, Hubei, Shanxi, Tianjin, Hunan and Guangdong Province, China. The sample codes and origin were listed in Supplementary Table 1. The voucher specimens, identified by Associate Professor Zhiming Bi of China Pharmaceutical University, have been deposited in the State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China. The reference standards of eriocitrin (1), narirutin (2), naringin (3), hesperidin (4), neohesperidin (5), didymin (6), poncirin (7), nobiletin (8), tangeretin (9) and synephrine were purchased from Chengdu Must Bio-technology Co., Ltd. (Chengdu, China), and 5-hydroxy-6,7,8,3‟,4‟-pentamethoxyflavone (10) was isolated and purified from Pericarpium Citri Reticulatae by conventional column chromatography coupled with high speed countercurrent chromatography. The structures of 10 reference compounds are shown in Fig. 1, and the purities of them were determined to be higher than 98% by high performance liquid chromatography-diode array detection analysis. A mixture of n-alkanes (C8-C20, Lot: BCBK5370V) was purchased from Fluka (Buchs, Switzerland) to calculate the retention indices (RI) of all the volatile constituents.
The solvents, HPLC grade acetonitrile (ACN) and formic acid were purchased from Merck (Darmstadt, Germany); deionized water (18 MΩ) used for all solutions and dilutions was prepared by distilled water through a Milli-Q system (Millipore, Milford, MA, USA). Other reagents and chemicals are of analytical grade.
2.2. Instrument and Chromatography conditions
2.2.1 Instrument and Chromatography conditions for flavonoids analysis The flavonoids analysis was carried out on an Agilent series 1290 HPLC liquid chromatograph, equipped with a thermostated column compartment, an auto-sampler, a degasser, a quaternary pump and a diode array detector (Agilent Technologies, Palo Alto, CA, USA). Separation was performed on an Agilent ZORBAX Extend-C18 column (4.6 mm × 50 mm, 1.8m, Agilent Technologies, MD, USA). Mobile phase was composed of 0.3% aqueous formic acid (A) and acetonitrile (B) with a gradient elution as follows: 10-17% (B) in 0-2 min, 17% (B) in 2-8 min, 17-19% (B) in 8-10 min, 19% (B) in 10-12 min, 19-30% (B) in 12-16 min, 30-35% (B) in 16-22 min, 35% (B) in 22-23 min, 35-45% (B) in 23-24 min, 45-50% (B) in 24-28 min, 50-80% (B) in 28-30 min. The flow rate was 0.8 mL/min and the column temperature was 30 °C. Different detection wavelengths were performed for different periods of time: 283 nm for 0-19 min and 330 nm for 19-30 min.
2.2.2 Instrument and Chromatography conditions for synephrine analysis
The HPLC system used was a Shimadzu (LC-20AT) liquid chromatograph, equipped with a pump (LC-20AT), an auto-sampler (SIL-20A), a diode array detector (SPD-M20A) and an automatic column temperature control oven (CTO-20A). Separation was conducted on an Agilent ZORBAX 300-SCX column (2.1 mm × 150 mm, 5 m, Agilent Technologies, MD, USA) at 30 °C. The mobile phase consisted of aqueous ammonium dihydrogen phosphate solution (pH 4.9, 8 mM) (A) and acetonitrile (B) using an isocratic elution of 45% (B) in 0-18 min. The flow rate was kept at 0.3 mL/min and the detection wavelength was 224 nm.
2.2.3 Instrument and GC-MS analysis of volatile oils GC-MS analysis of volatile oils was performed on an Agilent 7890B gas chromatography instrument (Agilent Technologies, USA) coupled to an Agilent 5977 Mass Selective Detector (MSD). An HP-5MS capillary column (30 m × 0.25 mm i. d.) coated with a 0.25 m film of 5% phenyl methyl siloxane was used for the separation. High-purity helium was used as the carrier gas, with a flow rate of 1.0 mL/min. The splitting ratio was set at 40: 1, the injection temperature and the interface temperature to 250 °C, standard electronic impact MS source temperature to 230 °C and MS quadrupole temperature to 180 °C. The mass spectra plot was acquired using full scan monitoring mode with a mass scan range of m/z 45-450. The column temperature was set at 50 °C and programmed to rise at 4 °C/min to 90 °C (1 min held), 10 °C/min to 120 °C (1 min held), 3 °C/min to 160 °C (1 min held), 30 °C/min to 200 °C kept for 1 min and finally rising at 15 °C/min to 280 °C (1 min held).
2.3. Preparation of standard solutions Reference standards of ten flavonoids and synephrine were accurately weighed, respectively, then dissolved in methanol to form corresponding standard stock solutions. Standard working solutions were prepared from those stock solutions by further dilution with appropriate volume of methanol, and then stored at 4 °C or -20 °C before use.
2.4 Preparation of sample solutions
2.4.1 Preparation for flavonoids analysis The tested samples were ground into powder and passed through a 60-mesh (0.3 mm) sieve. About 0.5 g sample powder was exactly weighed into a 100 mL conical flask, added 25 mL methanol, weighed and sonicated for 30 min at 100 Hz. After cooled at room temperature, methanol was added to compensate for the lost weight. The extracts were filtered through 0.22m nylon filter membrane and preserved in 4 °C refrigerator before HPLC analysis.
2.4.2 Preparation for synephrine analysis About 0.5 g of finely ground and homogenized samples were accurately weighed. After being soaked for 1 h in 5 mL of 0.1 M HCl, 20 mL of a water: methanol (25: 75, v/v) solution were further added and sonicated for 30 min. When cooled to room
temperature, methanol was added to compensate for the lost weight. All samples were centrifuged at 13,000 rpm/min for 10 min prior to use, and one microliter of the supernatant of AFI or two microliters of the supernatant of AF was injected into the HPLC instrument for analysis.
2.4.3 Preparation for essential oils analysis 31 batches of AFI and AF samples were randomly selected, ground into coarse powder and accurately weighed. Using a standard apparatus set, the essential oils were extracted by steam distillation for 8 h with an amount of distilled water eight times the volume of the sample. Finally, white yellow or brown oil samples were collected, and the extraction yields of essential oil (mL/g) were shown in the Supplementary Table 2. Then they were dried using anhydrous sodium sulphate to remove traces of moisture, and stored in the dark at -20 °C prior to analysis. About 1 L of each essential oil solutions (dissolved in n-hexane as 1/250) and a series of n-alkanes (C8-C20) were separately analyzed by GC-MS.
2.5. Data analysis
PCA and HCA were carried out based on the quantification of ten flavonoids compounds in 60 samples, using SIMCA-P 14.1 (Umetrics, Sweden) and IBM SPSS Statistics 19 (IBM, Chicago) Software, respectively. When the target compounds were not detected or the contents of them less than the quantitation limit in the samples, the
values of such elements were considered to be 0. T-test and one-way ANOVA were employed with GraphPad Prism 6 (GraphPad Software, California) for analyzing the content of synephrine in 60 samples to discriminate the two herbal medicines. The relative contents of the 17 major volatile components in 31 batches of AFI and AF samples selected were used as input data for PCA and PLS-DA analyses. The program for PLS-DA was performed on SIMCA-P 12.
3. Results and discussion
3.1 Flavonoids comparison of AFI and AF
3.1.1. Optimization of extraction conditions In order to make a comparison on the content of flavonoids in AFI and AF, the extraction method (dipping, ultrasound, refluxing), time (30, 60, 90 min) and solvents (water, methanol, methanol-water (1:3, 1:1, 3:1, v/v), ethanol, ethanol-water (1:1, 3:1, v/v), water-saturated n-butanol) were optimized. Compared with refluxing extraction, ultrasound technique provided similar peak area of principal peaks but shorter extraction time. Thus, ultrasound extraction was selected in this study (the supplementary table 3). For extraction solvents, the results achieved indicated that different solvents significantly affected the extraction efficiencies. By comparing the extraction efficiency of ten flavonoids and the simplicity of the method, ultrasonic
extraction with pure methanol was eventually selected.
3.1.2. Optimization of HPLC conditions A segmental monitoring method based on HPLC-variable wavelength detection was performed for simultaneous quantification of ten flavonoids in AFI and AF samples. The UV detector was monitored at 283 nm for flavanone O-glycosides (eriocitrin (1), narirutin (2), naringin (3), hesperidin (4), neohesperidin (5), didymin (6), poncirin (7)) at 0-19 min and 330 nm for polymethoxyflavones (nobiletin (8), tangeretin (9), 5-hydroxy-6,7,8,3‟,4‟-pentamethoxyflavone (10)) at 19-30 min (Supplementary Fig. 1). In order to achieve a good separation of the analytes, different flow rate (0.5, 0.6, 0.7, 0.8 and 0.9 mL/min) and column temperature (20, 25, 30, 35 and 40 °C) were examined. Furthermore, formic acid (0.1%, 0.2% and 0.3%, v/v) was added into the mobile phase to improve the peak shape and restrain the peak tailing. As a result, a flow rate of 0.8 mL/min, a column temperature of 30 °C, the detection wavelength of 283 nm (0 ~ 19 min) and 330 nm (19 ~ 30 min) and mobile phase system (0.3% aqueous formic acid (A) and acetonitrile (B)) provided good separation and strong UV absorption for most constituents in AFI and AF. The typical LC chromatograms are shown in Fig. 2.
3.1.3 Method validation The proposed HPLC-DAD method was validated by determination of linearity, limit of detection (LOD), limit of quantification (LOQ), precision, stability,
repeatability and recovery. For the calibration curves, seven or eight working solution concentrations were analyzed. Between the corresponding peak areas and the concentrations, a good linear relationship was obtained. LOD and LOQ were determined by injecting serial diluted standard working solutions and taking generated peaks with signal-to-noise ratio of 3 and 10 as criteria, respectively. The precision of the method was evaluated by the determination of intra- and inter-day variances. The intra-day variability test was analyzed at three different concentration levels (low, medium, high) with six replicates at each level on the same day, whereas the inter-day variability test was conducted in duplicates on the consecutive three days. To confirm the stability, the same sample was stored at 4 °C and room temperature and evaluated by replicate injection at 0, 2, 4, 8, 12 and 18 h. For the repeatability test, six replicates of the same samples were extracted and analyzed. Recovery was used to further evaluate the accuracy of the method by calculating the mean recoveries of the analytes. Three different concentration levels (approximately equivalent to 0.5, 1.0 and 1.5 times of the concentration of the target compounds in sample) of the standard solutions were added with three parallels at each level. The relative standard deviations (RSDs) were calculated as the measures of them. As shown in Supplementary Table (4-6), all the calibration curves showed good linearity (r2 >0.9993) over the wide concentration ranges. The RSD values of intra-day precisions and inter-day precisions were less than 3.73% and 4.69%, respectively. The stability study demonstrated that the sample was stable within 18 h at room temperature. The overall recoveries of all the compounds were ranged from
87.2% to 103.7% with RSD values less than 2.37%. The verified method was precise and accurate for the simultaneous determination of the ten flavonoids.
3.1.4. Quantitative analysis of ten flavonoids in AFI and AF samples A total of 60 batches of different herbal samples were collected and analyzed by the method described above. The quantitative results are presented in Table 1. It was found that the total contents of ten analytes in AFI (the mean value was 222.24 mg/g) were significantly higher than those in AF (the mean value was 83.89 mg/g). The content disparity might be explained by the different collection periods of AFI and AF, which caused the differences in the accumulation of secondary metabolites. The data also indicated that both naringin and neohesperidin were the predominant compounds in most AFI and AF samples. For AFI samples from Sichuan and Hunan province, narirutin and hesperidin were abundantly present, while neohesperidin and poncirin were almost not detected. For some batches of AF samples from Sichuan and Yunnan province, the content of narigin was significantly higher than other compounds analyzed. Moreover, it should be noted that the contents of three PMFs were very low in AFI and AF, even could not be quantified or detected in several samples. Besides, the total flavonoids contents of samples from Jiangxi were relatively higher compared with other provinces. The content distributions of ten flavonoids in 60 samples were displayed in Supplement Fig.2.
3.1.5 Discrimination of AFI and AF based on chemometrics
To provide more information about the chemical differences of AFI and AF, HCA and PCA were conducted based on the contents of ten bioactive flavonoids [28-29]. In HCA, a method named as ward linkage was applied, and square euclidean distance was selected as a measurement for analysis. As shown in Fig. 3, AF1-AF27 could be included in A cluster, AFI1-AFI21 and AFI32- AFI33 in B cluster and AFI22-AFI31 in the C cluster. They, in turn, represent AF samples, AFI samples from Jiangxi, Hubei, Anhui and Yunnan province, and AFI samples from Sichuan, Hunan and Zhejiang province, respectively. In PCA, as displayed in the score scatter 3D plot (Fig. 4) and the loading scatter 3D plot (Supplementary Fig. 3), the first, second and third principal components described 39.9%, 29.1% and 17.9% of the variability in the original observations, respectively, and the first three principal components (PC1, PC2, PC3) accounted for 86.9% of total variance. The PCA results were consistent with HCA analysis, 60 sample dots were classified into three sample groups, corresponding to AFI samples (AFI1-AFI21, AFI32-AFI33), AFI samples (AFI22-AFI31) and AF samples (AF1-AF27), respectively. Besides, dots present in each group were relatively nearer to each other, indicating a closer relationship among them. From the loading scatter plot, it could be observed that most of the variables have made considerable contributions in samples differentiation, and the variables located near to each other had the same effect on similarity and dissimilarity of different samples. The chemometrics analysis results indicated the contents of the flavonoids in AFI and AF were significantly different, and the samples from different geographical origins could be discriminated based on quantitative analysis.
3.2 Alkaloid comparison of AFI and AF
3.2.1. Optimization of extraction conditions and HPLC conditions Synephrine is a highly water-soluble sympathomimetic amine, and could not be easily retained on C18 stationary phase. In order to determine synephrine in AFI and AF, a SCX column, which has specificity for alkaloids, was selected in this study based on the principle of high performance ion exchange chromatography. To obtain appropriate extraction and good chromatographic behavior for synephrine, different extraction solvents (Supplementary Fig. 4A), extraction time (Supplementary Fig. 4B), extraction methods and mobile phase system were examined and compared. The results demonstrated that the optimum extraction of synephrine from AFI and AF were as follows: About 0.5 g of sample was weighed accurately. After being soaked for 1 h in 5 mL of 0.1 M HCl, 20 mL of 75% methanol were further added and sonicated for 30 min. The isocratic eluent of 55% A (8 mM ammonium dihydrogen phosphate aqueous solution) and 45% B (acetonitrile) at a flow rate of 0.3 ml/min with the column temperature of 30 °C resulted in a satisfactory separation of synephrine in a short analysis time. The typical LC chromatograms are shown in Fig. 5.
3.2.2. Quantitative analysis of synephrine in AFI and AF 3.2.2.1 Method validation
The linearity of calibration curve was measured by analyzing eight working stock solutions. The stability was carried out at room temperature and analyzed at 0, 2, 4, 8, 12 and 24 h within one day, respectively. The LOD, LOQ, precision, repeatability and recovery were determined as the same as the method described above in 3.1.3. The calibration curves exhibited good linearity (r2 > 0.9999) with a relatively wide range of concentrations (from 1.1 g/mL to 540 g/mL). The LOD and LOQ were less than 0.185 g/ml and 0.417g/ml, respectively. The intra- and inter-day variations (RSDs) for synephrine were less than 2%. The repeatability presented as RSD was 2.54%, and the stability was 3.56%. The recoveries varied between 96.1% and 103.5% with RSDs less than 5%. The above data (Supplementary Table 7-8) were considered to be satisfactory for subsequent analysis of all the samples.
3.2.2.2 Quantitative analysis of synephrine in AFI and AF samples The SCX-HPLC method described above was applied to determine synephrine, the predominant alkaloid in 60 batches of different AFI and AF samples. The quantitative results are shown in Table 2. To compare the content of synephrine in AFI and AF, t-test was firstly carried out to discriminate these two medicinal herbs (Fig. 6A), then one-way ANOVA followed by Tukey's multiple comparisons test with two tailed P value < 0.05 as significant was used to evaluate the difference in AFI or AF of different geographical origins (Fig. 6B, 6C). The results demonstrated that the contents of synephrine in AFI were significantly higher than those in AF. And p-value of 0.0001 suggested that there was
a dramatic variation of synephrine between AFI and AF. Moreover, the synephrine content in AFI from Sichuan and Hunan province was slightly higher than from Jiangxi province, revealing that a significant difference also occurred in AFI of different sources. However, the differences of synephrine content were not significant among the AF samples. The developed method for analysis of synephrine could be helpful for discrimination and classification of AFI and AF samples, as well as facilitating their rational medical use.
3.3 volatile oils comparison of AFI and AF
3.3.1. GC-MS analysis of the essential oils from AFI and AF All the samples were analyzed as the GC-MS method described in 2.2.3. The typical total ion chromatograms (TIC) of AFI and AF are shown in Fig. 7. The volatile components were identified by comparison of their mass spectra with those recorded in the National Institute of Standards and Technology (NIST) mass spectral library and further confirmed by comparing their retention indices with published literatures [30-34] and NIST11 library. The relative contents of the volatile constituents were calculated using the area normalization method (the peaks whose area beyond 10000 were labelled and automatically integrated). The quantitative and qualitative results of the essential oils in AFI and AF are shown in Supplementary Table 9.
3.3.2. Comparison of volatile oil in AFI and AF
The Supplementary Table 2 showed the global yields of volatile oil in AFI and AF. On average, the total volatile oil content in AFI from Jiangxi province was significantly higher (0.94%) than AF (0.47%). In this work, 63 compounds were identified in AFI and 62 compounds identified in AF. Most of them belonged to monoterpenes and sesquiterpenes. As shown in the Supplementary Table 8, the most predominant volatile constituent was D-limonene (57.4% on average in AFI and 67.2% in AF, respectively), followed by γ-terpinene (12.2% in AFI and 8.2% in AF, respectively), linalool, o-cymene, germacrene D, β-myrcene, β-pinene, α-pinene, trans-β-ocimene, terpinolene, α-terpineol, and δ-Cadinene, the mean amounts of which were higher than 0.5%. Other compounds, such as cis-linaloloxide, terpinen-4-ol, caryophyllene, α-terpinene and β-elemene, occurred at a relatively low proportion between 0.1% ~ 0.5%. Besides, some trace components such as cis-p-Menth-2-en-1-ol, thymol methyl ether and α-cubebene were only identified in AFI, while 1,3,8-p-Menthatriene, p-Menth-1-en-9-al, geranyl acetate and elemol were only found in AF. The results indicated that variation in chemical compositions occurred during the period of growth.
3.3.3 Discrimination of AFI and AF based on chemometrics Although most compounds presented in the essential oils of AFI and AF are the same, their contents are different. Here, 17 main common constituents (Table 3), the contents of which were higher than 0.1% in almost all batches of AFI and AF samples, were selected and entered into analysis.
PCA was firstly employed to visualize the classification trends. As shown in the supplementary figure 5, the discrimination of AFI and AF, and the difference in AFI or AF of different geographical origins were not significant. The PLS-DA method was subsequently applied, which uses class information to maximize the separation between classes and minimize the distance between intragroup clustering. It can enable the reduction of large data into PC1, PC2 or PC3 with relative score plots showing clear separation among samples. As shown in Fig. 8, samples could be clearly classified as one of two groups corresponding to AFI and AF. Furthermore, it was found that there were notable differences among AFI and AF of different geographical origins, but these intragroup differences lacked abundant proof. Even though, the results confirmed that the volatile compounds in AFI and AF samples were different in terms of the values of fitting parameters (Q2=0.767; R2Y=0.878), which indicated that the PLS-DA model had a good predictive capability and a significant explanatory power on the discrimination of the samples. Moreover, the variable important for the projection (VIP) values revealed that α-Terpinene, β-Elemene,
D-Limonene, γ-Terpinene, Terpinen-4-ol,
β-Pinene,
trans-β-Ocimene, o-Cymene, and cis-Linaloloxide, whose values beyond 1, contributed significantly to the intergroup differences. Table 3 shows that, AFI contains more β-Pinene, α-Terpinene, β-Elemene, γ-Terpinene, trans-β-Ocimene, Terpinen-4-ol and γ-Terpinene, but less D-Limonene and cis-Linaloloxide compared with AF. These components (VIP>1) might serve as potential chemical markers for discrimination of AFI and AF.
Flavonoids, alkaloids and volatile oil are the major bioactive constituents in AFI and AF. As described above, we established three different methods to discriminate AFI and AF. Any one of them was independent of each other and complementary. Based on the contents of ten flavonoids and synephrine, the developed identification model could be used to differentiate AFI and AF samples, and the difference in samples from different geographical origins could also be found out by the models. In addition, the AFI and AF samples could be distinguished by the method according to the relative contents of essential oils. All the developed methods are reliable, simple and feasible, and can be applied to evaluate the quality of traditional citrus herbs.
4. Conclusion
In this paper, the flavonoids, alkaloids and essential oils in AFI and AF were analyzed by the developed HPLC-variable wavelength detection, SCX-HPLC and GC-MS method, respectively. The established methodology displayed acceptable levels of linearity, precision, repeatability and accuracy. And chemometrics analysis (HCA, PCA, t-test, one-way ANOVA and PLS-DA) were performed to discriminate the two herbal medicines based on the quantitative data. All results indicated that AFI and AF samples could be discriminated from each other, and significant difference existed in samples from different geographical origins. The proposed method could be
employed for quality control of AFI and AF, and the comparative results will facilitate better understanding of their different traditional uses.
Acknowledgments
The authors greatly appreciate financial support from National Natural Science Foundation of China (81473343, 81673569), Program for New Century Excellent Talents in University (NECT-13-1034), “Six Talent Peaks Program” of Jiangsu Province of China (2013-YY-001) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Legend to figures
Fig. 1. Chemical structures of 11 reference substances (ten flavonoids and one alkaloid) Fig. 2. The typical HPLC chromatograms of AFI and AF. The peak numbers are in accordance with the flavonoid compound numbers in Fig.1. Fig. 3. Dendrograms of hierarchical cluster analysis for 60 samples based on the content of ten flavonoids compounds Fig. 4. PCA score scatter plot of 60 samples. 1 corresponding to AFI samples (AFI1-AFI21, AFI32-AFI33), 2 corresponding to AFI samples (AFI22-AFI31), and 3 corresponding to AF samples (AF1-AF27) Fig. 5. The typical LC chromatograms of AFI and AF. (1. Synephrine) Fig. 6. T-test and one-way ANOVA of 60 samples based on the content of synephrine. (JX: Jiangxi; SC: Sichuan; HN: Hunan; Other: other provinces) Fig. 7. GC-MS typical total ion chromatograms of the AFI and AF samples Fig. 8. PLS-DA score scatter plot of 31 samples based on seventeen volatile oils. (A: Jiangxi, B: Sichuan, C: Hunan, D: Jiangxi, E: Sichuan and Hunan)
Tables
Table 1 The content of ten flavonoids in AFI and AF samples (mg/g, n = 3). Sample
1
2
3
4
5
6
7
8
9
10
Sample
1
2
3
4
5
6
7
8
9
10
AFI1
1.76
11.92
97.47
62.38
110.12
0.83
3.55
1.08
0.70
0.19
AFI31
0.51
28.27
21.91
104.14
19.16
5.55
0.54
0.50
0.23
0.06
AFI2
1.74
11.91
90.27
60.93
113.36
0.80
3.34
1.11
0.72
0.21
AFI32
0.91
14.01
98.57
8.38
74.16
0.17
3.97
0.70
0.45
0.06
AFI3
1.53
10.91
24.45
93.98
46.38
1.51
0.14
0.92
0.61
0.18
AFI33
2.56
8.30
94.35
8.58
91.21
0.14
0.73
0.55
0.20
0.07
AFI4
2.04
11.25
113.51
27.37
98.22
0.41
4.17
1.52
1.03
0.23
AF1
0.23
3.42
43.33
1.93
31.61
-
3.05
0.63
0.25
0.06
AFI5
0.78
1.09
132.83
1.05
169.55
-
1.64
0.83
0.42
0.08
AF2
0.61
6.44
67.06
3.45
31.83
0.20
5.42
1.52
1.07
0.12
AFI6
1.40
10.19
92.94
32.25
116.41
0.59
4.99
1.22
0.76
0.18
AF3
0.11
1.85
40.21
0.95
29.18
-
2.82
0.54
0.22
*
AFI7
1.49
10.54
75.50
51.57
71.57
1.15
1.27
0.76
0.38
0.13
AF4
0.08
6.91
37.96
4.91
23.74
0.21
1.81
0.43
0.19
-
AFI8
2.04
13.85
102.08
60.97
113.88
0.85
3.78
1.07
0.70
0.20
AF5
0.46
5.78
59.53
2.10
27.43
0.08
4.32
1.15
0.93
0.10
AFI9
1.09
26.73
59.06
88.17
87.23
3.52
2.48
0.47
0.24
-
AF6
0.28
3.79
47.05
2.41
31.33
0.06
2.92
0.56
0.34
0.04
AFI10
0.69
23.97
102.61
13.72
83.32
0.20
5.49
0.74
0.47
0.05
AF7
0.06
3.05
53.38
4.07
47.85
0.10
2.85
0.25
0.06
-
AFI11
3.90
8.73
89.69
14.40
155.79
0.13
0.36
0.83
0.47
0.18
AF8
0.69
5.46
75.50
0.95
34.84
0.13
6.71
2.24
1.39
0.16
AFI12
2.36
8.98
158.13
10.15
125.23
0.10
5.32
1.63
1.06
0.29
AF9
0.58
4.60
44.83
2.62
32.08
0.09
1.49
0.38
0.16
0.05
AFI13
2.47
12.64
169.53
3.05
86.53
0.17
5.30
2.02
1.42
0.30
AF10
0.27
6.19
26.66
18.16
20.78
1.59
0.47
0.30
0.05
*
AFI14
1.89
7.36
136.06
13.18
173.46
0.30
4.18
1.50
0.91
0.30
AF11
0.29
4.37
34.65
9.29
20.88
0.71
0.25
0.24
0.04
*
AFI15
1.53
7.90
144.10
12.04
152.52
0.21
6.55
1.81
1.09
0.25
AF12
0.37
3.58
25.98
4.28
18.28
0.18
0.16
0.19
0.03
-
AFI16
1.56
17.21
83.29
76.32
44.79
2.22
0.19
0.50
0.23
0.13
AF13
0.31
10.04
43.05
6.29
26.50
0.12
3.56
0.75
0.41
0.04
AFI17
1.73
14.95
56.33
83.19
53.28
1.88
0.34
0.77
0.53
0.20
AF14
0.47
3.83
43.86
1.67
28.05
-
0.22
0.25
0.09
*
AFI18
2.51
9.23
57.63
73.48
97.27
1.18
0.26
1.06
0.60
0.18
AF15
0.39
5.60
53.49
2.99
40.27
*
0.71
0.20
0.06
-
AFI19
1.62
12.87
35.61
93.56
49.02
1.86
0.16
1.09
0.73
0.19
AF16
0.75
5.04
62.59
1.90
33.90
*
4.70
1.53
0.98
0.12
AFI20
3.70
7.33
91.31
15.15
144.83
0.33
0.68
0.32
0.14
AF17
0.54
5.21
54.50
0.32
20.81
*
3.14
1.27
0.94
0.10
AFI21
1.05
8.32
98.38
0.50
1.73
0.43
0.14
0.07
AF18
0.72
4.68
53.17
2.01
39.42
*
0.47
0.35
0.13
0.05
AFI22
0.05
6.71
-
84.89
AFI23
0.40
34.95
-
119.60
-
4.17
-
6.33
3.60
1.02
AF19
0.35
3.55
37.95
3.33
19.94
0.12
2.98
1.69
0.96
0.10
-
6.59
-
0.50
0.27
0.10
AF20
0.38
6.71
55.93
4.20
44.25
0.08
1.25
0.27
0.13
0.04
AFI24
0.24
11.45
102.37
-
4.92
-
0.81
0.07
0.06
AF21
0.49
3.13
48.02
0.36
24.52
0.05
3.11
1.79
1.14
0.14
AFI25
0.46
40.16
-
113.76
-
7.38
-
0.10
*
-
AF22
0.59
3.77
43.15
2.27
36.47
-
0.53
0.31
0.15
0.04
AFI26
0.28
31.73
-
108.84
-
9.71
-
1.23
0.47
0.14
AF23
0.11
-
44.60
-
-
0.14
0.39
-
-
-
AFI27
0.44
30.04
-
108.26
-
5.41
-
0.12
*
-
AF24
0.13
0.19
48.71
0.40
0.11
0.19
0.35
*
-
0.02
AFI28
0.08
20.82
0.02
105.55
-
7.57
-
1.05
0.13
0.06
AF25
0.59
4.87
50.93
4.27
34.72
0.07
0.69
0.38
0.20
0.05
AFI29
0.48
33.86
0.02
112.88
-
5.88
-
0.17
0.01
-
AF26
*
-
41.86
*
0.28
0.10
-
-
-
AFI30
0.12
10.42
3.16
93.11
*
4.19
-
1.49
0.77
0.24
AF27
0.08
0.65
18.75
*
-
0.15
0.08
*
-
24.02
- not detected * under limit of quantification
12.35
68.51
* 14.36
Table 2 The content of synephrine in AFI and AF samples (mg/g, n = 3). Sample
Mean ± SD
Sample
Mean ± SD
Sample
Mean ± SD
Sample
Mean ± SD
AFI1
5.68 ± 0.10
AFI16
3.46 ± 0.09
AFI31
4.68 ± 0.06
AF13
1.17 ± 0.00
AFI2
6.25 ± 0.01
AFI17
6.22 ± 0.05
AFI32
3.33 ± 0.03
AF14
0.21 ± 0.00
AFI3
5.25 ± 0.05
AFI18
4.14 ± 0.03
AFI33
1.79 ± 0.07
AF15
0.50 ± 0.02
AFI4
5.40 ± 0.09
AFI19
5.34 ± 0.01
AF1
1.27 ± 0.02
AF16
1.03 ± 0.01
AFI5
6.484 ± 0.06
AFI20
2.54 ± 0.01
AF2
1.92 ± 0.05
AF17
1.83 ± 0.01
AFI6
5.39 ± 0.03
AFI21
0.98 ± 0.01
AF3
1.09 ± 0.02
AF18
0.29 ± 0.00
AFI7
7.76 ± 0.06
AFI22
12.28 ± 0.11
AF4
1.66 ± 0.02
AF19
1.34 ± 0.01
AFI8
2.71 ± 0.02
AFI23
6.06 ± 0.06
AF5
2.42 ± 0.02
AF20
0.43 ± 0.00
AFI9
5.92 ± 0.07
AFI24
4.60 ± 0.04
AF6
1.30 ± 0.01
AF21
1.41 ± 0.01
AFI10
2.30 ± 0.01
AFI25
7.81 ± 0.06
AF7
1.64 ± 0.01
AF22
0.19 ± 0.00
AFI11
3.37 ± 0.02
AFI26
5.97 ± 0.03
AF8
2.23 ± 0.02
AF23
*
AFI12
7.27 ± 0.03
AFI27
6.46 ± 0.03
AF9
0.10 ± 0.01
AF24
*
AFI13
5.42 ± 0.03
AFI28
6.52 ± 0.05
AF10
0.41 ± 0.01
AF25
0.22 ± 0.00
AFI14
7.50 ± 0.04
AFI29
8.98 ± 0.07
AF11
0.21 ± 0.00
AF26
*
AFI15
4.71 ± 0.03
AFI30
7.00 ± 0.02
AF12
0.19 ± 0.00
AF27
*
* under limit of quantification
Table 3 The relative contents of 17 main common compounds in AFI and AF samples. α-Pine
β-Pine
β-Myrce
α-Terpinen
o-Cymen
D-Limone
trans-β-
γ-Terpinen
cis-Linalolox
Terpin
ne
ne
ne
e
e
ne
Ocimene
e
ide
olene
Terpinen
α-Terpine
β-Eleme
Caryophy
Germacre
δ-Cadine
-4-ol
ol
ne
llene
ne D
ne
AFI1
1.69
2.16
0.88
0.53
4.03
45.51
4.19
18.91
0.75
1.36
9.75
0.42
0.48
1.09
0.31
3.48
0.31
AFI3
1.56
2.02
0.73
0.52
4.05
35.94
4.62
18.12
1.46
1.76
15.17
0.54
0.72
1.8
0.38
4.97
0.43
AFI6
1.4
1.76
0.86
0.46
4.13
48.07
3.68
16.44
1.15
1.53
9.31
0.51
0.65
1.13
0.37
3.61
0.39
AFI7
1.24
1.6
0.73
0.32
4.72
48.61
0.76
13.16
0.32
0.84
0.99
0.27
0.31
1.64
0.88
10.07
1.24
AFI8
0.79
0.87
0.74
0.34
3.86
57.35
0.44
11.79
0.26
0.78
1.3
0.56
0.39
1.28
0.79
8.45
0.95
AFI9
1.25
1.22
1.12
0.93
3.13
60.28
2.51
12.59
0.71
1.13
4.87
1.97
0.53
0.28
0.35
2.61
0.27
AFI17
0.6
0.81
0.64
0.25
6.35
54.36
0.36
9.01
0.55
0.84
2.77
0.81
0.78
1.56
0.66
4.58
1.47
AFI22
1.28
0.74
1.1
0.35
3.62
72.66
10.11
0.08
0.76
3.45
0.1
0.91
1.03
-
-
0.21
0.12
AFI23
0.64
0.75
0.71
0.26
4.38
57.81
0.37
10.11
-
0.67
3.45
0.61
0.53
2.02
0.59
1.76
0.98
AFI24
0.71
AFI25
0.65
1.02
1.02
0.57
2.88
66.42
2.07
12.23
0.18
0.84
4
1.81
0.79
0.17
0.15
0.5
0.14
0.65
0.66
0.21
3.52
52.7
0.87
9.31
-
0.63
0.72
0.23
0.7
2.34
0.88
2.55
2.32
AFI26
0.97
0.68
0.94
0.44
2.52
73.82
0.53
8.05
0.52
2.41
0.18
1.38
0.47
0.24
0.24
0.59
0.26
AFI27
1.18
1.67
0.94
0.34
5.54
52.91
3.22
15.51
0.76
3.02
0.23
0.53
1.68
0.44
1.47
0.49
AFI29
1.12
1.19
1.05
0.5
6.05
63.14
1.86
11.48
-
0.71
2.54
0.84
0.39
1.54
0.29
0.6
0.8
AFI30
0.41
0.55
0.7
0.61
3.64
60.92
2.66
15.56
0.13
1.25
3.06
2.08
1.76
0.36
0.34
1.18
0.6
AF2
0.81
0.58
-
0.19
2.43
68.16
0.5
8.21
1.46
1.33
7.28
0.41
1.22
0.37
0.13
1.55
0.57
AF3
0.85
0.76
0.92
0.23
3.42
61.31
1.01
9.26
1.94
1.59
4.61
0.56
1.09
0.57
0.46
3.67
0.77
AF4
0.41
0.66
0.81
0.26
2.78
62.49
1.43
9.5
1.12
1.25
4.26
0.82
1.15
0.52
0.38
3.27
0.64
AF5
0.51
0.44
0.78
0.16
2.65
56.44
0.61
7.74
2.64
1.77
10.22
0.74
2.34
0.66
0.24
2.37
1.07
AF6
0.87
0.74
1
0.27
3.42
63.42
0.78
7.85
1.77
1.42
4.55
0.77
1.2
0.25
0.31
2.31
0.64
AF7
0.83
1.63
0.83
0.18
2.27
67.36
0.58
5.7
0.92
0.82
3.28
0.67
1.24
0.52
0.25
3.62
0.72
AF8
0.85
0.6
1.08
0.17
2.45
77.95
0.36
8.36
0.63
0.79
3.36
0.24
0.58
0.11
-
1.01
0.25
Sample
Linalol
AF15
1.01
0.73
1.15
0.19
1.17
74.28
0.51
8.9
0.53
0.7
5.32
0.17
0.47
0.23
0.09
1.62
0.31
AF16
0.96
1.09
0.97
0.23
3.26
68.15
1.16
9.97
0.21
0.65
2
0.38
0.52
0.52
0.23
3.47
0.59
AF17
1.16
1.04
1.02
0.19
6.1
73.81
0.48
9.33
0.33
0.84
0.32
0.3
0.66
-
0.4
0.75
0.37
AF18
0.97
0.92
0.91
0.19
2.82
66.16
0.44
7.87
0.26
0.53
2.19
0.44
0.69
0.73
0.3
5.9
0.77
AF19
0.77
0.8
0.72
0.2
2.11
57.84
0.43
8.64
0.11
0.47
0.78
0.26
0.41
1.4
0.56
12.83
1.54
AF21
0.93
0.6
1.11
0.23
2.83
62.71
0.42
7.74
1.65
1.55
7.46
0.55
1.56
0.51
0.19
2.26
0.86
AF22
0.91
0.66
1.06
0.22
3.1
66.64
0.61
8.13
1.74
1.55
5.65
0.48
1.53
0.32
0.16
1.62
0.81
AF23
1.08
1.01
2.4
0.14
3.38
75.7
0.08
7.52
0.2
0.46
0.12
0.15
0.24
0.49
0.5
3.55
0.25
AF25
0.77
0.46
1.01
0.19
3.83
65.04
0.49
6.67
2.06
1.77
5.98
0.52
1.95
0.46
0.23
1.44
1.18
“ - ”peak area less than 10000 or not detected. The bolded compound are the volatile oils, whose VIP value >1
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.