Volatile Components’ Variation Analysis on Ginseng Stems and Leaves at Different Growth Ages by HS-SPME-GC-MS

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Introduction
Panax ginseng C. A. Mey is the root and rhizome of the Araliaceae species ginseng, which originated from the Far East, mainly distributed in China, the Korean Peninsula, Japan, and eastern Russia in eastern Asia, with China as the main producing area, among which the Changbai Mountain in Jilin province has the highest yield [1][2][3]. It is a perennial crop, and 3-7-year-old ginseng is used in many medicines [4]. Compared to ginseng roots, many studies of ginsenosides have focused on the roots and rhizomes of ginseng because the aerial parts including stems and leaves are usually discarded. However, some recent studies have focused on the pharmacological efects of compounds in ginseng stems and leaves (GSLs) harvested annually, showing that the ginsenoside components in GSLs are related to the rhizome and root of ginseng [5]. In addition, GSLs have the advantages of being rich in resources, large in annual output, inexpensive, and easy to obtain [6]. So it has development potential and commercial value. As the main active ingredient of ginseng, ginsenoside has various therapeutic efects, such as enhancing immunity, anticancer, lowering blood sugar, anti-infammatory, antioxidation, antiapoptosis [7], and protecting the central nervous system [8].
Moreover, polysaccharides and proteins are the basic substances for plant growth and development [9]. Tey are involved in a series of physiological activities such as seed germination and development, root and leaf diferentiation, fruit ripening, embryo formation, and senescence [10]. Terpenoids are a kind of natural hydrocarbon compound that widely exists in plants. Tere are more than 7000 kinds of sesquiterpenes, which are the largest kind of terpenoids and play an important role in plant growth and development [11]. For example, the plant hormone abscisic acid is formed from sesquiterpenes degraded by carotenoid precursors [12]. Te accumulation of patchouli oil (mainly composed of sesquiterpene patchouli alcohol) gradually increased with the plant's age [13].
Several studies have reported that the pharmacological components and efects of ginseng change with age [14,15]. Similarly, the content of ginsenosides in GSL can be diferent depending on the growth age. Qu et al. [16] have reported that the contents of ginsenosides Rg1, Re, Rf, Rb1, Rc, and Rb2 tend to increase with growing age. As the growth age of ginseng can hardly be determined by physical appearance, a reliable method to discriminate the growth age of ginseng is required. A study shows that ginseng of diferent growth ages can be successfully diferentiated by the metabolomics approach [17]. However, to make better use of ginseng resources and be dedicated to establishing a nondestructive pathway to achieve in situ, rapid detection, it is necessary to study the variation of GSLs' characteristics under diferent growth years. Headspace solid-phase microextraction (SPME) is a relatively new volatile extraction technique. Furthermore, SPME allows the enrichment of volatiles in gas or liquid samples by fused silica fbers followed by subsequent desorption of these less volatile analytes [18,19]. Tis technique is suitable for sample processing and analysis of volatile and semivolatile organic compounds. Compared to other commonly used volatile substances acquisition technologies, HS-SPME has the advantages of simplicity, solvent-free, and high sensitivity. It integrates sampling, extraction, concentration, and sample injection. After enrichment, it can be directly combined with gas chromatography-mass spectrometry (GC-MS), highperformance liquid chromatography (HPLC), capillary electrophoresis (CE), and other methods [20]. Besides the advantages, it also has drawbacks, including the limited mechanical robustness of the fber and the rather small sorption phase volume of the commercially available fbers [21]. In addition, GC-MS is a highly sensitive and comprehensive analytical tool for volatile and semivolatile organic compounds in mixture samples. A reference library of GC-MS has been established for many primary metabolites [22]. By using the method, we tried to obtain a complementary profle to discriminate volatile components by checking the overall profle diference of primary and secondary metabolites in ginseng stems and leaves [23,24]. Metabolomics can identify changes in metabolic profles [25]. We speculate that the volatile components and metabolic profles of GSLs at diferent growth ages may change in a predictable manner.
Tis study was based on 3-7-year-old GSLs to explore the diferences of saponins, crude polysaccharide, total protein, and volatile content in GSLs. Trying to discover their correlation and variation, and to provide a more scientifc basis for identifying the GSLs and other Chinese herbal medicine of diferent growth years, it also provides a reference for the reasonable collection time and quality control of ginseng.

Chemicals and Materials.
Te alkanes (C 8 -C 30 ) were purchased from Alfa Aesar (USA); methanol, anhydrous ethanol, concentrated sulfuric acid, and acetic acid (all analytical purity >98%) were purchased from Beijing Chemical Plant; vanillin was purchased from Shanghai Yongyi Biotechnology Co., Ltd. Reference standards for ginsenoside Re were obtained from the Department of Organic Chemistry, Jilin University. Bovine serum albumin was purchased from the National Institute of Metrology, China, glucose standard was purchased from Beijing Putian Tongchuang Biotechnology Co., Ltd. 96-well plates were purchased from Costar, USA, polydimethylsiloxanedivinylbenzene (PDMS-DVB/65 μm) and 12 mL headspace extraction bottles were purchased from Supelco, USA.
TRACE 1310 GC-triple quadrupole MS gas chromatography-tandem mass spectrometry was purchased from Termo Fisher, USA, P-VP-III-40 ultrapure water machine for laboratory was purchased from Sichuan Walter Technology Development Co., Ltd., centrifuge 5804R was purchased from Eppendorf, USA; XP205 precision analytical balance was purchased from Metler, Switzerland, H-8 digital display constant temperature water bath pot was purchased from Changzhou Zhiborui Instrument Manufacturing Co., Ltd., infnite M200 PRO microplate reader (Tecan, Switzerland), and A11B S025 Crusher was purchased from Germany IKA Equipment Co., Ltd.

Plant Materials.
Ginseng stems and leaves were collected in Wanliang Town, Fusong County, Jilin Province, which is a major production area in the northern-east region. Ginseng stems and leaves of 3-, 4-, 5-, 6-, and 7year-olds were harvested in 2019 (September).

Sample Preparation. 3-7-
year-old ginseng stem and leaf samples were washed and dried in a drying oven at 40°C for 48 h, then weighed. Te stems and leaves were ground to 10 mm or less using a superfne grinding machine (XDW-6B, Jinan, China). Ten, 100 mg of fnely ground GSLs and 10 mg NaCl (GR) were transferred into a 12 mL headspace vial refrigerator for spare.

Contents of Total Saponins in GSLs.
Te total saponin content was determined according to Bradford's method with BSA as a standard [26].

Contents of Crude Polysaccharide in GSLs.
Te total polysaccharide content was evaluated by a phenol sulfuric acid method using glucose as a reference [27].

Contents of Total Protein in GSLs.
Te total protein content was determined according to the Coomassie brilliant blue G-250 colorimetric method [28].

Headspace Solid-Phase Microextraction (HS-SPME).
Ginseng stem and leaf powders were weighed (100 mg), placed in a 12 mL headspace fask, and sealed hermetically with a Tefon cap. Te sample was immersed in a constant temperature water bath at 70°C for 30 minutes prior to the analysis. Ten, the 65 μm PDMS-DVB fber (Supelco, USA) was exposed to the solid-phase microextraction fber 2 cm above the sample for 45 minutes to extract the VOCs. Ten, the fber was conditioned according to the instructions of the supplier [29,30]. Te retention index was determined by nalkanes (C 8 -C 30 ). Te QC samples were obtained by equally mixing all the samples analyzed. Te QC samples were processed according to the abovementioned method, and one needle of QC samples was injected every 6-8 samples to ensure the stability and reproducibility of the experimental system for validating the stability and reproducibility of the GC-MS system. Each sample was measured three times in parallel.

Gas Chromatography-Mass Spectrometry (GC-MS)
Analysis. Gas chromatography with a time-of-fight mass spectrometry system consisting of a TRACE 1310 (Waltham, MA, USA) with a fused silica capillary column DB-5MS (30 m × 0.25 mm, 0.25 μm; Agilent, USA) was employed. Te injection was conducted in splitless mode at 250°C. Te condition of the program raising temperature was performed as follows: 50°C lasted for 0-2 min, then increased to 200°C at a rate of 10°C/min and held for 10 min, and then increased to 280°C at a rate of 5°C/min. Ultrahigh purity (99.9995%) helium was used as a carrier gas with an average linear velocity of 1.0 mL/min and a split ratio of 1 : 30. Te transfer line temperature of mass spectrometry was 280°C, an ion source of 280°C, and the electron impact ionization was tuned to 70 eV with mass ranges from 50 to 500 m/z.

Data Processing and Statistical
Analysis. All majority peaks, being above the analytical noise and representing 95% of the chromatogram peaks [31], were integrated using MassHunter Qualitative Analysis B.06.00 software (Agilent Technologies). Tis software allows for the deconvolution of the chromatograms by separating the coeluted compounds. Te identifcation of the volatile compounds was performed by comparing the retention indices (RI) relative to n-alkanes (C 8 -C 20 ), run under identical conditions for GC-MS, with those of the compounds in the National Institute of Standard and Technologies (NIST) online library (http://webbook. nist.gov/chemistry/casser.html), by comparing the mass spectra of the compounds with those of the compounds referenced in the NIST databases (RSI > 800) and by comparing the retention times and mass spectra of the compounds with those of the available standards [32]. For statistical analysis, peak area values of the total ion chromatograms were measured with MassHunter and transferred to Excel (Microsoft Excel 2010). Te data obtained after the analysis of the GSLs of diferent harvest periods were subjected to PCA using SIMCA-P software (v15.0, Umetrics, Sweden).
Te Microsoft Excel 2010 software was conducted for statistical analysis and charting of data. Te data were expressed as mean values ± standard deviation with six replicated measurements. Te analysis of variance (ANOVA) was used in the statistical analysis of the data and was computed in SPSS Statistics 21.0 (SPSS Inc., Shanghai, China) software, and a probability value of P < 0.05 was considered to represent a statistically signifcant diference among mean values. Te heat map and receiver operating characteristic (ROC) analysis were conducted by Metab-oAnalyst 5.0 platform (https://www.metaboanalyst.ca/).

Determination of Total Saponins in GSLs
. 5 year old ginseng plants are usually regarded as mature enough to harvest for medicinal utilization [33]. In this study, the change of total saponin content in GSLs with growth years was GSL5 (4. , and the content of total saponins in GSLs of GSL5 was the highest (Figure 1(a)).

Multivariate Statistical Analysis of VOCs in GSLs of Various Growth Times.
To compare the changes in these volatile metabolites between ginseng stem and leaf samples of diferent growth ages, the PCA and OPLS-DA methods were employed. PCA is a multivariate statistical analysis method that can reduce the dimensionality of the data while information on the original data is still retained by using several variables to select a smaller number of important variables by linearly transforming the data. Figure 2 shows the PCA score plot of the major principal components. All samples fell outside Hotelling's T2 tolerance ellipse with 95% confdence, suggesting that no outlier was observed [34,35]. QC samples were clustered closely, indicating that the method was stable and reproducible.

Discovery and Identifcation of Biomarkers for Volatile Components in GSLs in Diferent Harvest Periods.
A supervised OPLS-DA model was used to identify the variables responsible for the classifcation [36]. In the OPLS-DA score plot (Figure 3), the results of group separation were consistent with the PCA score plot (Figure 2), and each group was more closely clustered. A permutation test (n � 200) was applied to estimate the robustness and predictive ability of the model indicating the robustness of the model and showing a low risk of overftting. Te results demonstrated that the model was stable and reproducible. Critical volatile metabolites were selected based on the two following conditions: variable importance in projection (VIP) values >1.0 and P values <0.05. Te marker diferential components of GSL3-GSL7 were screened, which are presented in Figure 4. 415 variables were screened out in GSL3 and GSL4. 501 variables were screened out from GSL3 and GSL5 ratios. 701 variables were screened out by GSL3 and GSL6 comparison. 629 variables were screened out from GSL3 and GSL7. Ten, the Origin software was employed to demonstrate the common variables, 263 were shown to be the common variables among the 3-7 old consecutive cultivation years.
All the 263 common diferential metabolites were matched with the NIST online library, in-house library, and references, and then compared to the retention indices (RI)  value. As a result, 32 metabolites were selected to be identifed, and fold change values were also conducted to express the variation trends between the four compared groups (Table 1). According to reports, the target volatile components were mainly terpenes, alcohols, acids, esters, aldehydes, and alkanes [37]. Terpenoids are the largest group of natural products discovered in plants. Sesquiterpenes, the largest subgroup of terpenoids, are known as more than 7,000 species components and possessed many functions in plant physiological and ecological interactions [38][39][40]. Plants produce sesquiterpenes as bioactive compounds to protect themselves from insects and pathogenic microorganisms [41]. Sesquiterpenes have shown to contribute to defense in multiple plant species [42]. Figure 5 shows the identifed volatile components labeled in TIC, profles of intensities of 32 common diferential compounds from GSLs revealing changes within various growth periods. Te peak intensities of GSL 3 vs. 4, 5, 6, and 7 were all signifcantly diferent (P < 0.05) ( Figure 6). Regarding specifc compounds, diferential compounds 1-3 (C1-C3) had the highest content in the GSL5 and showed irregular changes with the growth years; diferential compound 4 (C4) had the highest content in the GSL4; the GSL3 had the highest concentration of diferential compounds including C5, 16, and 21-24, 27, 30, and 32; and diferential compounds 5, 21, and 24 and 30 (C5, 21, 24, and 30) showed a downward trend with age. Diferential compounds 6-16 (C6-C16) and 17-20 (C17-C20) were the highest in GSL6. With GSL6 as the turning point, the content increased with the increase of years in GSL3-GSL6 years and decreased signifcantly in GSL7. Te content of metabolites 25, 26, 28, 29, and 31 (C25-29 and C31) were the highest in GSL7 and decreased from GSL3 to GSL6. It should also be noted that the GSL4 showed a downward trend in the volatile components identifed above and the GSL6 showed an upward trend in the volatile substances mentioned above, especially sesquiterpenes, indicating that the GSL6 may contain more volatile components than the GSL4. Tis result may provide a new direction for the identifcation of GSL in diferent growth years. A heatmap was generated using those 32 metabolites gained above by MetaboAnalyst 5.0 (Figure 7(a)). In order to fnd out the metabolites that contribute to age prediction obtained from OPLS-DA result, with the signifcant value of VIP >1 and P value <0.05 as the screening standard of potential biomarkers, 32 metabolites between the four compared groups (GSL3 vs. 4, 5, 6, and 7) were analyzed to determine the most valuable potential biomarkers. Te fuctuation trend and intensity changes between diferent metabolites can be obtained by correlation heatmap analysis and statistical histogram of the diferential metabolites (Figures 7(b) and 7(c)). For the purpose of evaluating the biomarker that contributed to prominent age discrimination capacity, the receiver operating characteristic (ROC) analysis was employed for all data of four compared model groups. Te area under the curve (AUC) value was used to show the extent of deviation between 3-and 4-, 3-and 5-, 3-and 6-, and 3and 7-year-old GSL, respectively. In our results, 32 metabolites previously identifed all gain good value (AUC value >0.8) on the contribution of age discrimination (Tables S1 to S4). Signifcantly, the metabolite named 1-Cyclohexene-1-carboxaldehyde, 2, 6, 6-trimethyl-showed an excellent prediction with the AUC value of 1.0 among all the four compared groups (Figure 7(d)), which also demonstrated that it could be the potential biomarker to discriminate the 3-and 5-, 3and 6-, and 3-and 7-year-old GSL. Te observation showed a signifcant efect on the chemical composition of GSLs infuenced by cultivation years, especially on the VOCs.
Polysaccharides, proteins, and ginsenosides are the key and dominating ingredients in ginseng plants, which are responsible for structural formation, energy reserves, and physiological accommodation [43][44][45]. In this study, the contents of crude polysaccharides, total protein, and total ginsenosides are shown to be irregular changes with the growth age increased. Te current results provided an indication of GSLs application on specifc compositions. If the polysaccharides and proteins are selected as the research objects, 4-year-old GSLs will be a good choice. If the studied objects are the saponins, it is better to choose 3-year-old GSLs. If volatile oil is going to be the target, it is recommended to harvest the 6-year-old GSLs.  Journal of Chemistry C31 C32 Figure 6: Profles of 32 common diferential compounds (compound 1-32 and C1-C32) from GSLs revealing changes in intensities within various harvest periods. Te peak intensities of GSL 3 vs. 4, 5, 6, and 7 were all signifcantly diferent (P < 0.05).  C25  C11  C16  C28  C4  C26  C8  C1  C3  C22  C14  C10  C20  C12  C9  C7  C21  C24  C23  C17  C18  C2  C15  C32  C27  C6  C31  C13  C19   C30  C19  C13  C31  C6  C27  C32  C15  C2  C18  C17  C23  C24  C21  C7  C9  C12  C20  C10  C14  C22  C3  C1  C8  C26  C4  C28  C16  C11  C25   1-Cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-  C15  C6  C20  C23  C9  C29  C28  C1  C27  C18  C32  C13  C26  C3  C8  C14  C24  C2  C5  C31  C7  C11  C4  C25  C12  C19  C21  C22  C17  C10  C16  C30  3  3.6. HCA of HS-SPME-GC-MS Analysis. Te 32 kinds of volatile compounds were the elements of this new data matrix, and the matrix with dimensions of 5 samples × 6 parallel replicates × 32 variables (volatiles) was constructed to perform HCA (Figure 8). Tese classifcation groups were consistent with the PCA results. In addition, six replicate samples at each harvest age were closely related, which further proved the great repeatability within the group. Terefore, it could be speculated that the GC-MS is highly correlated in distinguishing the volatile compound changes of ginseng stems and leaves during the diferent harvest periods, which could be confrmed by PCA and HCA. In conclusion, the HS-SPME combined with GC-MS was applied to the analysis of 3-7 year ginseng stems and leaves, and it was highly efcient and meaningful in the volatile components' research.

Conclusions
In this study, dynamic variations in the volatile composition of GSLs of fve growth periods were investigated by using HS-SPME-GC-MS, combined with untargeted metabolomics analysis. A total of 263 active volatile metabolites were identifed, among which 32 volatile metabolites were identifed as critical volatile metabolites for discriminating the 3-and 4-, 3-and 5-, 3-and 6-, and 3-and 7-year-old GSL, but not all the compared groups. Notably, there was one volatile component of sesquiterpenes, which are the unique constitutions in ginseng and could be the potential biomarker to discriminate all the compared groups including the 3-and 4-, 3-and 5-, 3-and 6-, and 3-and 7-year-old GSL. Te results could provide a basis for the determination of the harvesting time of GSLs and the study of factors causing the diference in quality. Overall, our fndings provided new insights into variations in the volatile metabolite profles of GSLs during diferent growth ages and improved our understanding of the chemical nature of the characteristics of diferent growth periods of GSLs.

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.