Comparative Study on Chemical Constituents of Ginseng Flowers with Four Consecutive Cultivation Age

The harvest period of cultivated ginseng is generally 4–6 years. Ginseng flowers (GFs), the nonmedicinal parts, are usually removed every autumn, in which components are generally believed to stay unchanged with the increasing cultivation age. Recently, few documents were reported on the variation of volatile organic compounds (VOCs) and other components about ginseng flowers. This study had an insight into the variation of the chemical constituents with the cultivation ages through the comparison of the volatile organic compounds, gross ginsenosides, crude polysaccharide, and gross proteins of ginseng flowers from 3-, 4-, 5-, and 6-yr-old (GF3, GF4, GF5, and GF6) which were conducted by headspace solid-phase microextraction-gas chromatography-triple quadrupole mass spectrometry (HS-SPME-GC-QQQ/MS) and spectroscopic analysis combined with multivariate statistical analysis, including one-way ANOVA analysis and T test. The results indicated that the crude polysaccharide contents raised significantly depending on cultivation age except 6-yr-old, whereas the gross ginsenosides and the gross protein content were indistinctive. According to the peak intensity of determined VOCs, the contents of most differential compounds arranged in an order from high to low are GF3, GF4, GF5, and GF6, such as the compounds 2–15, 17–19, 22, and 25–26, therefore, they can be inferred that they are important markers to identify the age of GFs. 461 common differential compounds were gained and 26 common volatile organic compounds were identified with RSI >800 and RI and RIx no more than 30, including alcohols (such as 11, 12, and 15), sesquiterpenes (such as 2, 3, and 4), esters (such as 1 and 26), naphthalene and naphthol (such as 7 and 20), which had potential effects on curing Alzheimer's disease, inflammatory diseases, and prostate cancer based on network pharmacology analysis. This paper firstly revealed the variation rules of constitutions of GFs, which may provide a reference for the harvest and making rational application.


Introduction
Ginseng (Panax ginseng C. A. Mey.) is a kind of herb belonging to the Araliaceae family with the reputation of being "Te King of Herbs" that has a long international history [1]. Te root and rhizome, the stem, leaf, fower bud, and berry of the ginseng are rich in ginsenosides [2][3][4]. Recently, interest in the fower buds, as a component of ginseng, has grown because they have valuable medicinal potential with antifatigue and immunity-enhancing functions [5], although ginseng fowers (GFs) buds are not recorded in the Chinese Pharmacopoeia (2020 edition) [6]. It has been reported that ginsenosides and polysaccharide are the major bioactive constituents of GFs with gross ginsenoside contents approximately fve times than those in the ginseng root [7][8][9].
Te quality of traditional Chinese medicinal (TCM) materials is usually defned by their texture and color but their VOCs have rarely been considered. VOCs acting as sensory indicators or a marker play a key role in production or evaluating product quality because of the potent efcacy of VOCs [10]. Tey represent about 1% of plant secondary metabolites with around 1700 substances, mediate plant semi chemistry, and are involved in abiotic stress responses [11]. Extraction is a crucial procedure for analyzing VOCs with many methods being reported, such as hydro-distillation, microwave-assisted extractions, and carbon dioxide supercritical fuid extraction [12,13]. However, these methods are time-and labor-consuming. Recently, headspace solid-phase microextraction (HS-SPME) has been increasingly used for analyzing VOCs, researchers can perform reliable analysis of VOCs of diferent kind of samples, especially aldehydes, ketones, alcohols, esters, phenolic compounds, and some key terpenic hydrocarbons, such as foodstufs, plants, and their metabolites, because the technique is generally solvent-free, quite rapid, require minimum sample preparation, and minimize the matrix constituents [14][15][16].
Chemometrics is a discipline related to the application of mathematics, statistics, and computer science. Te main missions of the chemometrics are manipulating and analyzing the chemical data, dealt with complex data sets to obtain the chemical information to the maximum extent [17][18]. At present, an increasing number of applications of chemometric pattern recognition in the analysis of food or agricultural products, such as wine aging studies, authenticity identifcation of TCM, and evaluation of the extraction method of artichoke [19][20][21].
Te present study will use physicochemical methods to determine the content of gross ginsenosides, crude polysaccharide, and gross protein in GFs from 3-, 4-, 5-, and 6yr-old ginseng plants (GF3, GF4, GF5, and GF6), which will be extracted and analyzed by the headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-QQQ/MS). Pattern recognition approaches, such as principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), will be used to discover potential chemical markers. Te results will be useful for clearly discriminating between GFs of various ages, and thus improve the quality control of GFs both as a raw drug material and for their use in TCM formulations.

Contents of Gross Ginsenosides in GFs of Various Ages.
Te gross ginsenosides content was determined by using the reference method of the State Standard of the People's Republic of China (GB/T 19506-2009): GF powder (passed through a 60-mesh sieve) was accurately weighed (about 1.0 g) then packaged in a neutral flter paper. Te powder was extracted with ether using a Soxhlet extractor for 1 h. Te sample package was then dried to evaporate the ether solvent. Methanol was added to the extractor to soak overnight. Te next day, the appropriate amount of methanol was added to repeat the extraction for 6 times. Te methanol extracts were combined and recovered, steaming a small amount of methanol extracts in the water bath and then dissolving them in water. 30 mL water extract was extracted for 4 times with 30 mL of water-saturated n-butanol. Te upper liquid was steamed, then dissolved in methanol and made up to 10 mL. Finally, the end product was used as the ginsenosides sample solutions for further analysis. Te total ginsenoside content was determined using the reference method of the product of geographical indication-Jilin Changbaishan ginseng (GB/T 19506-2009), Ginsenoside Re (National Institutes for Food and Drug Control, Beijing, China)was used as the standard to calculate the content of the gross ginsenoside. In order to prepare the ginsenoside Re standard solution, 10 mg ginsenoside Re was put into a 10 mL volumetric fask, dilute to scale with methanol as solvent and mixed. 10 μL, 20 μL, 30 μL, 40 μL, 60 μL, 80 μL, and 100 μL of the standard solution and 30 μL sample solution were transferred to 10 mL tubes and dried (60°C water bath). Ten, 0.5 ml 8% vanillinethanol and 5 ml 72% concentrated sulfuric acid were added to the prepared tubes. After fully shaking and mixing, the solution was heated in a 60°C water bath for 10 min and then cooled down in an ice-water bath for 10 min immediately. Te mixed reagent without ginsenoside was used as a reference. Finally, both were determined at 544 nm using an enzyme calibration (Infnite M200 PRO, Tecan, Switzerland). To reduce the error in determining the gross ginsenosides content, three parallel extracts were obtained from the same origin of GF raw materials. Te changes in gross ginsenosides content in GFs of various ages were then compared. Te following equation was used to calculate the gross ginsenoside content: Equation (1) of Cui et al. [22] was used to calculate the gross ginsenosides content: where X is the gross ginsenosides content; m 1 represents the weight of ginsenoside Re, m 2 is the weight of GF powder, and A 1 and A 2 are the absorbances of the ginsenoside Re standard solution and sample solution, respectively.

Contents of Crude Polysaccharide in GFs of Various Ages.
Te crude polysaccharide content of the GFs was determined by a phenol-sulfuric acid method [23]. GF powder (2.00 g) was accurately weighed and added with 70 mL of water and extracted using a 100 mL volumetric fask for 30 min by ultrasonic extraction. Te extract solution was placed in a 100°C water bath for extraction for 4 h and diluted to a 100 mL after cooled. Te 5 mL solution was mixed with 15 mL, 5 mL, and 5 mL of ethanol solution and centrifuged at a speed of 10000 r/min for 10 min, respectively. Te supernatant was discarded and the residue was dissolved in 100 mL volumetric fask with water. Take 2 mL solution in 25 mL graduated test tube with stopper and shake well after added 5% phenol solution 1.0 mL. Add 5 mL sulfuric acid solution quickly and also shake for 5 min. Placing it in boiling water bath for 20 min and then cooled to room temperature. Te glucose standard solution 0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, and 1.6 mL was accurately removed and the standard curve was made by the previous method. Te mixed reagent without glucose was used as a reference. Finally, both were determined at 486 nm using an enzyme calibration. Tree parallel extracts were obtained from the same origin of GF raw materials.

Contents of Gross Protein in GFs of Various Ages.
Diferent volumes (0, 10, 20, 30, 40, 50, and 60 μL) of the BSA standard solution (1 mg/mL) were transferred to an Eppendorf tube, then PBS bufer was added to make up to a volume of 150 μL. After taking out 15 μL and placing in another tube in turn, 285 μL of Coomassie brilliant blue solution was added, mixed well then left at room temperature for 5-10 min. Te solution was then transferred to a 96-well plate to measure the absorbance at 595 nm by an enzyme calibration. Te standard curve was drawn with protein concentration (mg/15 μL) as the abscissa and optical density as the ordinate. Te reagent without BSA was used as a blank control. 15 μL of GF extracts were used to determine the gross protein content of GFs in the above method. Te content values were gained by three parallel samples (n � 3).

Sample Pretreatment.
Te VOCs from the GFs were extracted by using HS-SPME. A sample of GFs about 100 mg was accurately weighed and transferred to a 12 mL headspace vial and then placed in a water bath at 70°C for 30 min. Tereafter, the VOCs were collected using a PDMS-DVB SPME fber by exposing the fber to the headspace of 2 cm above the sample for another 45 min under the same conditions. Te fbers were activated before sampling according to the instructions. After this step, the SPME fber was inserted directly into the injection port of the GC system for thermal desorption (30 s) in a splitless mode.

Preparation of Quality Control and Blank Samples.
Te quality control (QC) samples were obtained by mixing the same amount of all the analyzed samples. Te QC samples were processed as described previously. To ensure the stability and reproducibility of the experiment, QC samples were injected once every 6 to 8 GF samples.
To eliminate interference between the experimental environment and diferent samples, the blank sample was injected before the GF samples of various ages, that is, the above method was used without any powder. Blank sample were injected before each QC sample.

Gas Chromatograph-Mass Spectrometer Conditions.
Te GC-QQQ/MS system comprised of a TRACE 1310 system and TSQ ENDURA mass spectrometer with electron impact (EI) ion source and Xcalibur data processing system and was purchased from Termo Scientifc (Waltham, MA, USA). Te GC-QQQ/MS instrument was used to analyze the VOCs of the GFs with a DB-5MS silica capillary column (30 m × 0.25 mm, 0.25 μm). Te temperature of the injector was set at 250°C. Helium (99.999% purity) was used as the carrier gas at a fow rate of 1.0 mL/min and a split ratio of 1 : 30. Te temperature sequence of the column was programmed as follows: an initial temperature of 50°C held for 2 min, increased at 10°C/min to 200°C then increased to 280°C at 5°C/min and held there for 10 min. Te mass detector was equipped with EI, the ionization voltage was set at 70 eV in the 50-550 m/z scan range for the mass spectra collection, and the temperatures of the ion source and mass spectrum transmission line were 280°C.

Qualitative and Quantitative
Analysis. Te VOCs of the GFs were identifed as described by Choi et al. [24]. GC-QQQ/MS was used to determine the total ion chromatogram (TIC) of the VOCs of the GFs. Te chromatogram was processed by automatic mass spectral deconvolution and identifcation system (AMDIS) software (https://www. amdis.net/) to eliminate interference and increase the accuracy of matching results, overlapping peaks were separated to purify the mass spectra. Finally, the detection results were qualitatively analyzed using the NIST spectrometry library combined with the temperature-programmed retention indices (PTRI). Te mixture of a homologous series of n-alkane standards (C 8 -C 30 ) dissolved in n-hexane, employed for auxiliary qualitative analysis, was analyzed under the same conditions as the samples. Te RI of the samples was calculated by a linear heating formula, equation (2), with the chemical structure with the closest similarity to the mass spectrum and RI value being selected as the best identifcation result.
where n represents the number of carbon atoms, t x represents the retention time of the analyzed compound, and t n and t n + 1 represent the retention times of the outfow peaks of the n-alkanes with carbon numbers of n and n + 1, respectively. Otherwise, t n < t x < t n + 1 . Te quantitative method adopted in this experiment is the peak area normalization method.

Statistical Analysis.
Te GC-MS data were collected by Xcalibur software (version 2.2). XCMS Online (https:// xcmsonline.scripps.edu/) was used to preprocess the GC-QQQ/MS data, using peak matching, peak alignment, and peak area normalization, leading to the data table containing the sample name, retention time, mass-to-charge ratio, and peak intensity. Te data table was then imported into SIMCA-p software (version 17.0, Umetrics AB, Umeå, Sweden) for processing and a discriminant model for distinguishing between GFs of various ages was established using PCA and OPLS-DA. Te data for the diferent VOCs were presented as means ± standard errors. In combination with the T test diferential compounds were screened out based on the variable importance in projection (VIP) > 1 and p < 0.05. Based on the literature combined with the NIST 11 library, only diferential compounds with a relative strength index (RSI) greater than 800 were recorded, at the same time, the experimental RIx, which do not difer from the reported RI values by more than 30, are considered an important indicator for identifcation. Te fgures were drawn using Origin 2021 (OriginLab Corp., Northampton, MA, USA), GraphPad Prism 7 (Graph Pad Software, San Diego, CA, USA), and Draw Venn diagram online (https:// bioinformatics.psb.ugent.be/webtools/Venn/).

Gross Ginsenoside Content.
Te calibration curve and the gross ginsenosides content (external standard method) in GFs for each age were summarized in Tables 1 and 2, the method showed good linearity (R 2 > 0.999). Figure 1(a) shows that the gross ginsenoside content did not increase linearly. Te GFs of a 4-yr-old exhibited the highest average content (16.68 ± 0.98%), and the 5-yr-old is the lowest (14.55 ± 0.20%), but the subsequent pairwise comparison models showed no signifcant diferences (p > 0.05).

Crude Polysaccharide Content.
Te correlation coefcients between the content of crude polysaccharide in GFs are shown in Table 1. Te specifc values of the gross crude polysaccharide content (external standard method) in GFs for each age are summarized in Table 2, the method showed good linearity (R 2 > 0.99). Figure 1(b) shows that the crude polysaccharide content did not increase linearly, but tended to increase as the age increased from 3-to 5-yr-old and decrease as the age of 6-yr-old. Te GF5 sample exhibited the highest average content (5.60 ± 0.25%) and the GF6 sample showed the lowest value (3.78 ± 0.14%), the pairwise comparison model showed diferences (p < 0.05). Compared with GFs from 3-yr-olds, the crude polysaccharide content increased signifcantly at the 4-yr-old (p < 0.05) and extremely signifcant at the 5-yr-old (p < 0.01). Compared with those from 4-yr-olds, the content of GFs from 5-yr-olds increased extremely signifcant (p < 0.01) but 6-yr-old GFs decreased signifcantly (p < 0.05). Compared with GFs from 5-yr-olds, the crude polysaccharide content of GFs from 6-yr-olds had decreased extremely signifcant (p < 0.01).

Gross Protein Content.
Te calibration curve and the gross protein content (external standard method) in GFs for each age are summarized in Tables 1 and 2, the method showed good linearity (R 2 > 0.99). Figure 1(c) shows that the gross protein content did not increase linearly. Te GF6 samples exhibited the highest average content (11.40 ± 0.73%) and the GF4 samples showed the lowest value (10.03 ± 0.67%), but the subsequent pairwise comparison models showed no signifcant diferences (p > 0.05).

Multivariate Statistical Analysis of VOCs in GFs of
Various Ages. Although the VOCs in the GFs of various ages can be measured by qualitative and quantitative analyses, these methods have little help in determining the characteristic VOCs in the samples. PCA is a multivariate statistical analysis method that can reduce the dimensionality of the data while information on the original data still retained by using several variables to select a smaller number of important variables by linearly transforming the data. Tus, PCA can refect the trends in the data such as clusters and groupings with chemical similarities or diferences [25][26][27][28], so this method can be used to determine the characteristic VOCs of GFs of various ages.
In the scatterplot of the PCA score ( Figure S1), QC samples were clustered closely, indicating that the method was stable and reproducible. However, among the four groups of GFs of various ages, except that GF5-5 had poor clustering patterns in PCA models, the distribution efect of other samples was perfect.

Discovering and Identifying Chemical Markers of VOCs in GFs of Various
Ages. Supervised OPLS-DA was performed based on the above PCA model to defne the most signifcant contributors towards discrimination. OPLS-DA not only extends PCA regression, but also provides better discriminatory ability than PCA on the larger divergence of intraclass for samples [29]. In the present study, the GF3 and GF4 samples (GF3 vs. GF4), GF3 and GF5 samples (GF3 vs. GF5), and GF3 and GF6 samples (GF3 vs. GF6) were distinctly separated in the PCA and OPLS-DA score plot (Figures 2(a)-2(f )), suggesting that the various ages contributed to the distinctive VOCs of the GFs. For example, the parameters value of R 2 X for the GF3 vs. GF4 of OPLS-DA model was 0.753, indicating that 75.3% of the variation in the dataset could be modeled by the selected components. Te value of R 2 Y was 1, indicating that the model ftted the data very well. Te value of Q 2 was 0.795, indicating a good   International Journal of Analytical Chemistry predictive ability. Te other two groups of pairwise comparison models also exhibited perfect ftness and predictivity. Overall, establishing the OPLS-DA model was efective for discriminating between the GFs. Combined with the T test, the common diferential compounds in GF3, GF4, GF5, and GF6 were screened out by the threshold of VIP > 1 and p < 0.05. 674 diferential compounds were screened out between GF3 vs. GF4, 866 and 971 diferential compounds were screened out between GF3 vs. GF5 and GF3 vs. GF6, respectively. Figure 3 shows that 461 compounds with common symbolic diferences in GFs from 4 ages were screened out. Te TIC of the blank and 26 common diferential compounds identifed by NIST 11 and the literature are shown in Figure 4. It is not difcult to see that the blank had little efect on the sample. Te preliminary list of components is shown in Table 3. Te values of fold change describe the extent of the change from the initial to the fnal value. For the calculation of the fold change value of GFs of two ages, it was obtained by using the peak area of a compound of GF of the older age to that of the younger one. For example, the value of fold change was 2.3692, indicating that the peak intensity had increased by 2.3692 times between GFs from 3-yr-old to 4-yr-old. Figure 5 summarizes the trend in the 26 identifed common diferential compounds of GFs and can also be visualized intuitively from the heatmap and dendrogram of HCA ( Figure 6). For GFs of various ages, the colors of the heatmap and dendrogram varied dramatically in the GF3 and GF4 columns but varied more subtly in the GF5 and GF6 columns. Tis phenomenon indicated that depending on the age of the ginseng, the intensity of several peaks varied, thus demonstrating that diferent growth periods/ages change the metabolic composition of ginseng, the contents of GF3 and GF4 changed signifcantly, but over the next two years changed inapparently. Regarding specifc compounds, it can          International Journal of Analytical Chemistry be seen that the content of diferential compound 1 was highest in GF4, and the contents of diferential compounds 2-26 were highest in GF3. According to the peak intensity of determined VOCs, the contents of most diferential compounds arranged in an order from high to low are GF3, GF4, GF5, and GF6, such as the compounds 2-15, 17-19, 22, and 25-26; therefore, they can be inferred that they are important markers to identify the age of GFs. But other differential compounds arranged in an order from high to low are GF3, GF4, GF6, and GF5, such as the diferential compounds 16, 20-21, and 24. It is notable that the lowest content of diferential compound 23 was in GF4, followed by those from GF5, GF6, and GF3, showing a various trend from the other diferential compounds.

Network Analysis between the Active Variable Metabolites and Diseases.
Recently, an increasingly interest of medical usage of GF which is leading to a rapid progress of research into their pharmacological efects on many related diseases and they are now considered to be a trustworthy plant with medicinal potential. With the increase of cultivation age, the main efcacy of GFs will change to a certain extent, which may be related to these 26 identifed common diferential compounds of VOCs. Accordingly, the 26 identifed common diferential compounds of VOCs are researched for the related diseases. Te CAS, chemical name, or InChlKey of the 26 identifed diferential compounds were used to search in the TCMSP database. Figure 7 shows that 95 diseases were related to 16  Polysaccharides and proteins, the basic substances concerned with plant growth and development, are involved in a series of physiological activities such as seed germination and development, root and leaf diferentiation, fruit ripening, embryo formation, and senescence. Te present study found that the crude polysaccharide content of GFs increased signifcantly at the age of 3 to 5, but decreased apparently at the age of 6, but there was no signifcant diference in the content between 3-and 6-yr-old, the results showed that the accumulation of crude polysaccharide of GFs was not positively correlated with the age of GFs. Te gross ginsenosides and gross protein content of GFs changed slightly with the increase of age, but the changes were unobvious, indicating that the growth period of GFs had little efect on the gross ginsenosides and gross protein content.
Te accumulation of chemical components is directly related to the activity of secondary metabolism-related regulatory enzymes and the expression of related genes. With the continuous growth and development of plants, the related enzyme activities and the expression level of regulatory genes will also change, resulting in the production and accumulation of chemical components in the 3 Figure 6: Heatmap and dendrogram of HCA of peak intensity of GFs of various ages. It is normalized for column scale; the size of the circle represents the size of the peak intensity; colors from red and yellow to blue represent the peak intensity of compounds from bigger to smaller. process of plant growth and development are afected, therefore, the content of chemical components in diferent growth periods is diferent [30,31]. Previous studies had shown that the content of volatile oil in fowers is afected by the distribution density of oil cells and the accumulation degree of volatile oil in oil cells [32]. Terefore, the content of VOCs in GFs may be closely related to the growth and development of oil cells. It can be inferred that ginseng blossoms after the 3-yr-old and the accumulation of early substances is deep, which can provide more energy and nutrition for the growth and development of oil cells in the buds of GFs. Terefore, the content of most VOCs is the highest in the 3-yr-old.
Sesquiterpenes and alcohols are the main identifed common diferential compounds [33]. Sesquiterpenes are an abundant group belonging to the terpenoid family, with a C15 structure comprise of three isoprene units. At least 300 types of sesquiterpenes have been discovered yet and numerous sesquiterpenes are an essential oil constituent which usually are released from the fowers and leaves as chemical messenger in plant signaling, particularly in the defense mechanism against biotic and abiotic stresses, such as to increase the plant tolerance, to inhibit the pathogen growth, and to attract pollinators or dispel predatory and parasitic insects [34]. Previous studies have shown that sesquiterpenes have anti-infammatory and antioxidant efects, and the anti-infammatory efect might mediate by the NF-κB and MAPKs signaling pathways [35]. Te results of the present study have shown that the age of the GF had an important infuence on the chemical constituents and biological activities of GFs. For instance, if crude polysaccharide is the focus of research interest, it is recommended that GF5 should be harvested; if the target is on gross ginsenosides, gross proteins, or VOCs, it is advocated to harvest GF3.

Conclusions
GF is a potential medicinal part of ginseng and has been increasingly appreciated by consumers and growers worldwide. Elucidating the diferences in the contents of GFs of various ages, the gross ginsenosides, crude polysaccharide, and gross protein were achieved using a physicochemical method. An accurate and feasible analytical method based on HS-SPME-GC-QQQ/MS combined with multivariate statistical analyses was then developed to allow a comprehensive comparison of the VOCs of GFs of various ages, such as alcohols, terpenes, esters, and so on, leading to establishing a discriminant model. To our knowledge, this is the frst report that systematically compares the diferences of the contents of 26 identifed diferential compounds in GFs of various ages. Te method used in this investigation comprehensively analyzed the chemical composition of GFs, which can contribute to a reference for the collection and rational application of GFs of various ages, as well as can bring new methods and ideas for the identifcation and quality assessment of GFs of various ages and other Chinese medicinal material. However, this method still has some limitations, such as the nondestructive detection of ginseng age cannot be achieved. In the future, we hope to develop more techniques for nondestructive identifcation of ginseng age, which can provide theoretical guidance for future production practice and contribute to the development of ginseng industry.

Data Availability
Te data used to support the fndings of this study are included within the article.

Conflicts of Interest
Te authors declare that they have no conficts of interest.